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  • 1.
    Aghanavesi, Somayeh
    et al.
    Department of Computer Engineering, Dalarna University, Borlänge, Sweden.
    Bergquist, Filip
    Department of Pharmacology, Institute of Neuroscience and Physiology, Gothenburg University, Gothenburg, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests: results from levodopa challenge2019In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed)
    Abstract [en]

    Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

  • 2.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Borlänge, Dalarna University, Sweden.
    Bergquist, Filip
    Dept. of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors2018Conference paper (Refereed)
    Abstract [en]

    Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors

    Objective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.

    Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].

    Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.

    Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).

    Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.

  • 3.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Filip, Bergquist
    Dept. of Pharmacology, University of Gothenburg, Gothenbrug, Sweden.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms2018Conference paper (Other academic)
    Abstract [en]

    Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

    Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

    Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

    Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

    Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

    Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

  • 4.
    Aghanavesi, Somayeh
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Dougherty, Mark
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Verification of a Method for Measuring Parkinson’s Disease Related Temporal Irregularity in Spiral Drawings2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed)
    Abstract [en]

    Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

  • 5.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Westin, Jerker
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Measuring temporal irregularity in spiral drawings of patients with Parkinson’s disease2017In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders, John Wiley & Sons, 2017, Vol. 32, p. s252-s252, article id 654Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    Objective: The aim of this work is to evaluate clinimetric properties of a method for measuring Parkinson’s disease (PD) upper limb temporal irregularities during spiral drawing tasks.

    Background: Basal ganglia fluctuations of PD patients are associated with motor symptoms and relating them to objective sensor-based measures may facilitate the assessment of temporal irregularities, which could be difficult to be assessed visually. The present study investigated the upper limb temporal irregularity of patients at different stages of PD and medication time points.

    Methods: Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated the videos of patients' performance according to six items of UPDRS-III, dyskinesia (Dys), and Treatment Response Scale (TRS). A temporal irregularity score (TIS) was developed using approximate entropy (ApEn) method. Differences in mean TIS between two groups of patients and healthy subjects, and also across four subject groups: early, intermediate, advanced patients and, healthy subjects were assessed. The relative ability of TIS to detect changes from baseline (no medication) to later time points when patients were on medication was assessed. Correlations between TIS and clinical rating scales were assessed by Pearson correlation coefficients and test-retest reliability of TIS was measured by intra-class correlation coefficients (ICC).

    Results: The mean TIS was significantly different between healthy subjects and patients (P<0.0001). When assessing the changes in relation to treatment, clinical-based scores (TRS and Dys) had better responsiveness than TIS. However, the TIS was able to capture changes from Off to On, and the wearing off effects. Correlations between TIS and clinical scales were low indicating poor validity. Test-retest reliability correlation coefficient of the mean TIS was good (ICC=0.67).

    Conclusions: Our study found that TIS was able to differentiate spiral drawings drawn by patients from those drawn by healthy subjects. In addition, TIS could capture changes throughout the levodopa cycle.TIS was weakly correlated to clinical ratings indicating that TIS measures high frequency upper limb temporal irregularities that could be difficult to be detected during clinical observations.

  • 6.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Dept. of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business. Computer Engineering, School of Technology and Business Studies, Dalarna University, Falun, Sweden.
    A smartphone-based system to quantify dexterity in Parkinson's disease patients2017In: Informatics in Medicine Unlocked, ISSN 2352-9148, Vol. 9, p. 11-17Article in journal (Refereed)
    Abstract [en]

    Objectives

    The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD.

    Methods

    Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables.

    Results

    There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls.

    Conclusions

    Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

  • 7.
    Forsman, Anders
    et al.
    Informatik, Högskolan Dalarna, Falun, Sweden.
    Larsson, Hed Kerstin
    Naturvetenskap, Högskolan Dalarna, Falun, Sweden.
    Memedi, Mevludin
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Rosendahl, Hans
    Informatik, Högskolan Dalarna, Falun, Sweden.
    Hur kan man flippa klassrum – några exempel på "blended learning" från olika kurser på campus och distans2015Conference paper (Other academic)
    Abstract [sv]

    Syftet var att beskriva och jämföra hur vi arbetar med blended learning/flipped classroom (FC) i fyra olika kurser (campus och/eller distans) vid Högskolan Dalarna, där målet var att få syn på den egna praktiken, dela erfarenheter och inspirera varandra. Inventering av och diskussioner om hur vi arbetar med blended learning/FC och varför. De tre kurserna Forskningsmetodikkurs i informatik (campus), kurs i informatik om webbsidor (campus) och Datateknikkurs i programmering (distans) ges i princip enligt "klassiskt" FC, dvs. med inspelad föreläsning följt av diskussion vid efterföljande seminarium/motsvarande (med viss variation). I den fjärde kursen, naturvetenskap i lärarutbildningen (campus/distans), blandas inspelade och streamade föreläsningar, och laborationer och seminarier genomförs både på campus och distans (gäller både campus- och distanskurser). Vi arbetar både på liknande sätt men även olika beroende av "ämneskultur", ämnenas olika karaktär och olika kursers karaktär, men vi har ungefär samma mål: att försöka få bättre förberedda och mer aktiva studenter, dvs. försöka att gynna djupinlärning. Men möjligheterna till mer genomgripande förändringar i arbetssätt beror också av hur "öppen" ämneskulturen är för detta. En gemensam slutsats är att det är viktigt att fundera över hur man kan använda tekniken i pedagogikens tjänst för att möjliggöra/iscensätta FC. Vi upplever allihop att FC är en möjlighet att göra något på ett nytt och förhoppningsvis bättre sätt. Vi är också överens om betydelsen av att få igång studenterna och att få dem att samarbeta - kruxet är hur man kan åstadkomma detta. Nya arbetssätt ska inte medföra att studenterna sitter och tittar på när vi arbetar, om än vi gör det på ett annat sätt än vid t.ex. traditionella föreläsningar, för då är vi tillbaka i den "envägskommunikation" man vill komma bort från FC. Vi har också fått upp ögonen för att olika ämnen/kurser har olika förutsättningar/utmaningar vilket lett till att vi använder delvis olika strategier och metoder. En skillnad gentemot litteraturens beskrivningar av FC är att vi alla även använder detta i våra distanskurser, vilket ger ytterligare en dimension vad gäller utmaningar, både pedagogiskt och tekniskt, jämfört med att "flippa" på campus. Det har varit inspirerande och utvecklande att ventilera hur vi resonerar om och genomför våra olika versioner av FC, och samtalen har även medfört att vi haft möjlighet att reflektera över den egna praktiken och att spegla den i de andras praktiker, vilket både inspirerat och gett praktiska tips.

  • 8.
    Javed, Farrukh
    et al.
    Örebro University, Örebro University School of Business.
    Thomas, Ilias
    Dalarna University, Falun, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease2018Conference paper (Refereed)
    Abstract [en]

    The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson’s disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

  • 9.
    Johansson, Dongni
    et al.
    Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Thomas, Ilias
    Department of Micro-data Analysis, Dalarna University, Falun, Sweden.
    Ericsson, Anders
    RISE Acreo, Gothenburg, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Medvedev, Alexander
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Ohlsson, Fredrik
    RISE Acreo, Gothenburg, Sweden.
    Senek, Marina
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden.
    Westin, Jerker
    Department of Micro-data Analysis, Dalarna University, Falun, Sweden.
    Bergquist, Filip
    Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Evaluation of a sensor algorithm for motor state rating in Parkinson's disease2019In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126Article in journal (Refereed)
    Abstract [en]

    Introduction: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

    Methods: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

    Results: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (rs = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (rs = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

    Conclusion: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

  • 10.
    Jusufi, Ilir
    et al.
    Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    TapVis: A Data Visualization Approach for Assessment of Alternating Tapping Performance in Patients with Parkinson’s Disease2018In: EuroVis 2018 - Short Papers, The Eurographics Association , 2018, p. 55-59Conference paper (Refereed)
    Abstract [en]

    Advancements in telemedicine have been helpful for frequent monitoring of patients with Parkinson’s disease (PD) from remote locations and assessment of their individual symptoms and treatment-related complications. These data can be useful for helping clinicians to interpret symptom states and individually tailor the treatments by visualizing the physiological information collected by sensor-based systems. In this paper we present a visualization metaphor that represents symptom information of PD patients during tapping tests performed with a smartphone. The metaphor has been developed and evaluated with a clinician. It enabled the clinician to observe fine motor impairments and identify motor fluctuations regarding several movement aspects of patients that perform the tests from their homes.

  • 11.
    Jusufi, Ilir
    et al.
    Department of Computer Science, University of California, Davis CA, United States.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    School of Technology and Business Studies, Computer Engineering Dalarna University, Borlänge, Sweden.
    Visualization of spiral drawing data of patients with Parkinson's disease2014In: Information Visualisation (IV), Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 346-350Conference paper (Refereed)
    Abstract [en]

    Patients with Parkinson's disease (PD) need to be frequently monitored in order to assess their individual symptoms and treatment-related complications. Advances in technology have introduced telemedicine for patients in remote locations. However, data produced in such settings lack much information and are not easy to analyze or interpret compared to traditional, direct contact between the patient and clinician. Therefore, there is a need to present the data using visualization techniques in order to communicate in an understandable and objective manner to the clinician. This paper presents interaction and visualization approaches used to aid clinicians in the analysis of repeated measures of spirography of PD patients gathered by means of a telemetry touch screen device. The proposed approach enables clinicians to observe fine motor impairments and identify motor fluctuations of their patients while they perform the tests from their homes using the telemetry device.

  • 12.
    Karni, Liran
    et al.
    Örebro University, Örebro University School of Business.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Klein, Gunnar O.
    Örebro University, Örebro University School of Business.
    Targeting Patient Empowerment via ICT interventions: An ICT-specific Analytical Framework2019In: AMCIS 2019 Proceedings, Cancun, Mexico: Association for Information Systems, 2019Conference paper (Refereed)
    Abstract [en]

    Empowerment of patients is today often an explicit goal of various ICT interventions where the patients themselves use ICT tools, often via the internet. This study is proposing a framework model for ICT interventions aiming to empower patients. Our new model includes different aspects of the Empowerment concept, general possible strategies to achieve Empowerment using different ICT services. Finally, the ICT services and the underlying strategic model can be used to define evaluations of such interventions where the aim is to demonstrate Empowerment. Our model is based on a review of various general models of Empowerment and the Behavioral Intervention Technology Model (BIT). The implications of our model are discussed using two case studies projects, the C3-Cloud EU project about empowering patients with 4 chronic diseases and the EMPARK project about Internet-of-Things sensors based real time feedback to Parkinson patients.

  • 13.
    Karni, Liran
    et al.
    Örebro University, Örebro University School of Business.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Kolkowska, Ella
    Örebro University, Örebro University School of Business.
    Klein, Gunnar O.
    Örebro University, Örebro University School of Business.
    EMPARK: Internet of Things for Empowerment and Improved Treatment of Patients with Parkinson's Disease2018Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    Objective: This study aims to assess the effects of patient-directed feedback from remote symptom, medication, and disease activity monitoring on patient empowerment and treatment in Parkinson’s disease (PD).

    Background: There is a need to empower patients with PD to be able to understand better and control their disease using prescribed medication and following recommendations on lifestyle. The research project EMPARK will develop an Internet of Things system of sensors, mobile devices to deliver real-time, 24/7 patient symptom information with the primary goal to support PD patients empowerment and better understanding of their disease. The system will be deployed in patient homes to continuously measure movements, time-in-bed and drug delivery from a micro-dose levodopa system. Subjective symptom scoring, time of meals and physical activities will be reported by the patients via a smartphone application. Interfaces for patients and clinicians are being developed based on the user center design methodology to ensure maximal user acceptance. 

    Methods: This is a randomized controlled trial where 30 PD patients from 2 university clinics in Sweden will be randomized to receive (intervention group) or not (control group) continuous feedback from the results of the EMPARK home monitoring for 2 weeks. Disease-specific (UPDRS, PDQ-39), Quality of Life (QoL) (modified EuroQoL EQ-5D) and empowerment questionnaires will be collected prior and after the intervention. The correlation of technology-based objective and patient-reported subjective parameters will be assessed in both groups. Interviews will be conducted with the clinicians and observations will be made about the patient-clinician interaction to assess the potential treatment benefits of the intervention.

    Results: Preliminary results from workshops with patients and clinicians show potential to improve patient empowerment and disease control among patients. Completion of the trial will show the degree of patient empowerment, individualized treatment, and patientclinician interactions.

    Conclusions: Raising patients’ awareness about disease activity and home medication is possible among PD patients by providing them with feedback from the results of a home monitoring system. This randomized, controlled trial aims to provide evidence that this approach leads to improved patient empowerment and treatment results.

  • 14.
    Khan, Taha
    et al.
    Microdata Analysis Lab, Computer Engineering, Dalarna University, Borlänge, Sweden .
    Memedi, Mevludin
    Microdata Analysis Lab, Computer Engineering, Dalarna University, Borlänge, Sweden .
    Song, William Wei
    Microdata Analysis Lab, Informatics, Dalarna University, Borlänge, Sweden .
    Westin, Jerker
    Microdata Analysis Lab, Computer Engineering, Dalarna University, Borlänge, Sweden .
    A case study in healthcare informatics: a telemedicine framework for automated parkinson’s disease symptom assessment2014In: Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings / [ed] Zheng X. et al., Springer , 2014, p. 197-199Conference paper (Refereed)
    Abstract [en]

    This paper reports the development and evaluation of a mobile-based telemedicine framework for enabling remote monitoring of Parkinson’s disease (PD) symptoms. The system consists of different measurement devices for remote collection, processing and presentation of symptom data of advanced PD patients. Different numerical analysis techniques were applied on the raw symptom data to extract clinically symptom information which in turn were then used in a machine learning process to be mapped to the standard clinician-based measures. The methods for quantitative and automatic assessment of symptoms were then evaluated for their clinimetric properties such as validity, reliability and sensitivity to change. Results from several studies indicate that the methods had good metrics suggesting that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients.

  • 15.
    Kolkowska, Ella
    et al.
    Örebro University, Örebro University School of Business.
    Scandurra, Isabella
    Örebro University, Örebro University School of Business.
    Avatare Nöu, Anneli
    RISE SICS, Kista, Sweden.
    Sjölinder, Marie
    RISE SICS, Kista, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    A user-centered ethical assessment of welfare technology for elderly2018In: Human Aspects of IT for the Aged Population. Applications in Health, Assistance, and Entertainment / [ed] Jia Zhou, Gavriel Salvendy, Springer, 2018, p. 59-73Conference paper (Refereed)
    Abstract [en]

    Welfare technology (WT) is often developed with a technical perspective, and little consideration is taken regarding the involvement of important ethical considerations and different values that come up during the development and implementation of WT. Safety, security and privacy are significant, as well as the usability and overall benefit of the tool, but to date assessments often lack a holistic picture of the WT as seen by the users. This paper suggests a user-centered ethical assessment (UCEA) framework for WT to be able to evaluate ethical consequences as a part of the user-centered aspects. Building on established methodologies from research on ethical considerations, as well as the research domain of human-computer interaction, this assessment framework joins knowledge of ethical consequences with aspects affecting the “digitalization with the individual in the center”, e.g. privacy, safety, well-being, dignity, empowerment and usability. The framework was applied during development of an interface for providing symptom information to Parkinson patients. The results showed that the UCEA framework directs the attention to values emphasized by the patients. Thus, functionality of the system was evaluated in the light of values and expected results of the patients, thereby facilitating follow-up of a user-centered assessment. The framework may be further developed and tested, but in this study it served as a working tool for assessing ethical consequences of WT as a part of user-centered aspects.

  • 16.
    Matić, Teodora
    et al.
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Aghnavesi, Somayeh
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Dalarna, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Groznik, Vida
    Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia.
    Žabkar, Jure
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Sadikov, Aleksander
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease2019In: AIME 2019: Artificial Intelligence in Medicine / [ed] Riaño D., Wilk S., ten Teije A., Springer, 2019, Vol. 11526, p. 420-424Conference paper (Refereed)
    Abstract [en]

    Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement.

  • 17.
    Memedi, Mevludin
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    A mobile-based system can assess Parkinson's disease symptoms from home environments of patients2014In: Neurologi i Sverige, ISSN 2000-8538, no 3, p. 24-28Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    Treatment of Parkinson's disease (PD) patients involves major challenges like the large within- and between-patient variability in symptom profiles and the emergence of motor complications. As PD progresses, the symptoms develop slowly and they represent a significant source of disability in advanced patients. During evaluation of treatments and symptoms, both the physician- and patient-oriented outcomes offer complementary information. In addition, quantitative assessments of symptoms using sensing technologies can potentially complement and enhance both patient and clinician perspectives. At Högskolan Dalarna, the Lecturer Mevludin Memedi has developed a telemetry system that assesses symptoms via analysis of self-assessments and motor tests to objectively measure disease-related outcomes and to improve the management of PD.

  • 18.
    Memedi, Mevludin
    Örebro University, School of Science and Technology. Dalarna University, Falun, Sweden.
    Mobile systems for monitoring Parkinson's disease2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    A challenge for the clinical management of Parkinson's disease (PD) is the large within- and between-patient variability in symptom profiles as well as the emergence of motor complications which represent a significant source of disability in patients. This thesis deals with the development and evaluation of methods and systems for supporting the management of PD by using repeated measures, consisting of subjective assessments of symptoms and objective assessments of motor function through fine motor tests (spirography and tapping), collected by means of a telemetry touch screen device.

    One aim of the thesis was to develop methods for objective quantification and analysis of the severity of motor impairments being represented in spiral drawings and tapping results. This was accomplished by first quantifying the digitized movement data with time series analysis and then using them in data-driven modelling for automating the process of assessment of symptom severity. The objective measures were then analysed with respect to subjective assessments of motor conditions. Another aim was to develop a method for providing comparable information content as clinical rating scales by combining subjective and objective measures into composite scores, using time series analysis and data driven methods. The scores represent six symptom dimensions and an overall test score for reflecting the global health condition of the patient. In addition, the thesis presents the development of a web-based system for providing a visual representation of symptoms over time allowing clinicians to remotely monitor the symptom profiles of their patients. The quality of the methods was assessed by reporting different metrics of validity, reliability and sensitivity to treatment interventions and natural PD progression over time.

    Results from two studies demonstrated that the methods developed for the fine motor tests had good metrics indicating that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients. The fine motor tests captured different symptoms; spiral drawing impairment and tapping accuracy related to dyskinesias (involuntary movements) whereas tapping speed related to bradykinesia (slowness of movements). A longitudinal data analysis indicated that the six symptom dimensions and the overall test score contained important elements of information of the clinical scales and can be used to measure effects of PD treatment interventions and disease progression. A usability evaluation of the web-based system showed that the information presented in the system was comparable to qualitative clinical observations and the system was recognized as a tool that will assist in the management of patients.

    List of papers
    1. A new computer method for assessing drawing impairment in Parkinson's disease
    Open this publication in new window or tab >>A new computer method for assessing drawing impairment in Parkinson's disease
    Show others...
    2010 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 190, no 1, p. 143-148Article in journal (Refereed) Published
    Abstract [en]

    A test battery, consisting of self-assessments and motor tests (tapping and spiral drawing tasks) was used on 9482 test occasions by 62 patients with advanced Parkinson's disease (PD) in a telemedicine setting. On each test occasion, three Archimedes spirals were traced. A new computer method, using wavelet transforms and principal component analysis processed the spiral drawings to generate a spiral score. In a web interface, two PD specialists rated drawing impairment in spiral drawings from three random test occasions per patient, using a modification of the Bain & Findley 10-category scale. A standardised manual rating was defined as the mean of the two raters’ assessments. Bland-Altman analysis was used to evaluate agreement between the spiral score and the standardised manual rating. Another selection of spiral drawings was used to estimate the Spearman rank correlations between the raters (r = 0.87), and between the mean rating and the spiral score (r = 0.89). The 95% confidence interval for the method's prediction errors was ±1.5 scale units, which was similar to the differences between the human raters. In conclusion, the method could assess PD-related drawing impairments well comparable to trained raters.

    Place, publisher, year, edition, pages
    Amsterdam: Elsevier, 2010
    Keywords
    Test battery, Home environment, Motor test, Tremor, Dyskinesia, Spiral drawing, Drawing impairment, Wavelet transform, Principal component analysis, Involuntary movement, Movement disorders, Motor fluctuations, Parkinson's disease, Telemedicine
    National Category
    Neurosciences Biochemistry and Molecular Biology
    Research subject
    Biochemistry
    Identifiers
    urn:nbn:se:oru:diva-20464 (URN)10.1016/j.jneumeth.2010.04.027 (DOI)000279888800019 ()20438759 (PubMedID)2-s2.0-77953725166 (Scopus ID)
    Available from: 2011-12-02 Created: 2011-12-02 Last updated: 2018-01-12Bibliographically approved
    2. Automatic and objective assessment of alternating tapping performance in parkinson’s disease
    Open this publication in new window or tab >>Automatic and objective assessment of alternating tapping performance in parkinson’s disease
    Show others...
    2013 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 12, p. 16965-16984Article in journal (Refereed) Published
    Abstract [en]

    This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson‟s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions („speed‟, „accuracy‟, „fatigue‟ and „arrhythmia‟) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regressionclassifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson‟s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance ofPD patients and can be included in telemedicine tools for remote monitoring of tapping.

    Place, publisher, year, edition, pages
    Basel: MDPI AG, 2013
    Keywords
    alternating tapping; touch-pad; handheld computer; telemedicine; Parkinson‟s disease; remote monitoring; automatic assessment; objective assessment; visual assessment
    National Category
    Computer Sciences Chemical Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-33095 (URN)10.3390/s131216965 (DOI)000330220600061 ()2-s2.0-84890107007 (Scopus ID)
    Funder
    Swedish Research Council
    Note

    Funding Agencies:

    Nordforce Technology AB, Stockholm, Sweden

    Animech AB, Uppsala, Sweden

    Dalarna University, Borlänge, Sweden

    Available from: 2014-01-14 Created: 2014-01-14 Last updated: 2018-09-06Bibliographically approved
    3. Spiral drawing during self-rated dyskinesia is more impaired than during self-rated off
    Open this publication in new window or tab >>Spiral drawing during self-rated dyskinesia is more impaired than during self-rated off
    2013 (English)In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126, Vol. 19, no 5, p. 553-556Article in journal (Refereed) Published
    Abstract [en]

    Objective: The purpose of this study was to examine repeated measures of fine motor function in relation to self-assessed motor conditions in Parkinson's disease (PD).

    Methods: One-hundred PD patients, 65 with advanced PD and 35 patients with different disease stages have utilized a test battery in a telemedicine setting. On each test occasion, they initially self-assessed their motor condition (from 'very off' to 'very dyskinetic') and then performed a set of fine motor tests (tapping and spiral drawings).

    Results: The motor tests scores were found to be the best during self-rated On. Self-rated dyskinesias caused more impaired spiral drawing performance (mean = 9.8% worse, P < 0.001) but at the same time tapping speed was faster (mean = 5.0% increase, P < 0.001), compared to scores in self-rated Off.

    Conclusions: The fine motor tests of the test battery capture different symptoms; the spiral impairment primarily relates to dyskinesias whereas the tapping speed captures the Off symptoms.

    Keywords
    Spiral drawing, Dyskinesia, Tapping, Bradykinesia, Self-assessment, Telemedicine
    National Category
    Engineering and Technology
    Research subject
    Electrical Engineering
    Identifiers
    urn:nbn:se:oru:diva-29045 (URN)10.1016/j.parkreldis.2013.01.011 (DOI)000317455800010 ()2-s2.0-84875551765 (Scopus ID)
    Available from: 2013-05-20 Created: 2013-05-17 Last updated: 2017-12-06Bibliographically approved
    4. Combined fine-motor tests and self-assessments for remote detection of motor fluctuations
    Open this publication in new window or tab >>Combined fine-motor tests and self-assessments for remote detection of motor fluctuations
    2013 (English)In: Recent Patents on Biomedical Engineering, ISSN 1874-7647, Vol. 6, no 2, p. 127-135Article in journal (Refereed) Published
    Abstract [en]

    A major problem with the clinical management of fluctuating movement disorders, e.g. Parkinson’s disease (PD), is the large variability in manifestation of symptoms among patients. In this condition, frequent measurements which account for both patient-reported and objective assessments are needed in order to capture symptom fluctuations, with the purpose to optimize therapy. The main focus of this paper is to present a mobile-based system for enabling remote monitoring of PD patients from their home environment conditions. The system consists of a patient diary section for collecting patient-based self-assessments, a motor test section for collecting fine motor movements through upper limb motor tests, and a scheduler for restricting operation to a multitude of predetermined limited time intervals. The system processes and compiles time series data into different summary scores representing symptom severity. In addition, the paper presents a review of recent inventions which were filed after year 2000 in the field of telemedicine applications. The review includes a summary of systems and methods which enable remote symptom assessments of patients, not necessarily suffering from movement disorders, through repeated measurements and which take into account their subjective and/or objective health indicators. The findings conclude that there are a small number of inventions which collect subjective and objective health measures in telemedicine settings. Consequently, there is a lack of mechanisms that combine these two types of information into scores to provide a more in-depth assessment of the patient’s general health, their motor and non-motor symptom fluctuations and treatment effects. The paper also provides a discussion concerning different approaches for analyzing and combining subjective and objective measures, and handling data from longitudinal studies.

    Place, publisher, year, edition, pages
    Bentham Science Publishers, 2013
    Keywords
    remote patient monitoring, Parkinson’s disease, subjective, objective, telemedicine
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-33941 (URN)10.2174/18747647113069990001 (DOI)2-s2.0-84882786800 (Scopus ID)
    Note

    Swedish Knowledge Foundation, Abbott Product Operations AG (nowAbbVie), Nordforce Technology AB and Animech AB are gratefully acknowledged for the financial support they have extended within the frameworks of the E-MOTIONS and PAULINA projects.

    Available from: 2014-02-26 Created: 2014-02-26 Last updated: 2018-09-12Bibliographically approved
    5. A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients
    Open this publication in new window or tab >>A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients
    Show others...
    2011 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 104, no 2, p. 219-226Article in journal (Refereed) Published
    Abstract [en]

    This paper describes a web-based system for enabling remote monitoring of patients with Parkinson's disease (PD) and supporting clinicians in treating their patients. The system consists of a patient node for subjective and objective data collection based on a handheld computer, a service node for data storage and processing, and a web application for data presentation. Using statistical and machine learning methods, time series of raw data are summarized into scores for conceptual symptom dimensions and an “overall test score” providing a comprehensive profile of patient's health during a test period of about one week. The handheld unit was used quarterly or biannually by 65 patients with advanced PD for up to four years at nine clinics in Sweden. The IBM Computer System Usability Questionnaire was administered to assess nurses’ satisfaction with the web application. Results showed that a majority of the nurses were quite satisfied with the usability although a sizeable minority were not. Our findings support that this system can become an efficient tool to easily access relevant symptom information from the home environment of PD patients.

    Place, publisher, year, edition, pages
    Amsterdam: Elsevier, 2011
    Keywords
    Parkinson's disease, Test battery, Web application, Decision support, Remote patient monitoring, Telemedicine, Principal component analysis
    National Category
    Information Systems
    Research subject
    Computer and Systems Science
    Identifiers
    urn:nbn:se:oru:diva-20469 (URN)10.1016/j.cmpb.2011.07.017 (DOI)000296945100024 ()21872355 (PubMedID)2-s2.0-80054120422 (Scopus ID)
    Note

    7th IFAC Symposium on Modelling and Control in Biomedical Systems

    Available from: 2011-12-02 Created: 2011-12-02 Last updated: 2018-01-12Bibliographically approved
    6. Self-assessments and motor test via telemetry in a 36-month levodopa-carbidopa intestinal gelinfusion trial
    Open this publication in new window or tab >>Self-assessments and motor test via telemetry in a 36-month levodopa-carbidopa intestinal gelinfusion trial
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Computer Sciences Neurology
    Research subject
    Computer Science; Neurology
    Identifiers
    urn:nbn:se:oru:diva-33942 (URN)
    Note

    Objective: The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression.

    Methods: Sixty-five patients diagnosed with advanced PD were recruited in an openlongitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on Unified PD Rating Scale (UPDRS) and 39-item PD Questionnaire (PDQ-39) scales.

    Results: In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG non-naïve patients, there were no significant changes in mean OTS, except at month 36 (P<0.01). The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59).

    Conclusions: PD symptoms can be remotely monitored over time with this test battery. The trends of the test scores were similar to the trends of clinical rating scores. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of the well-established scales.

    Available from: 2014-02-26 Created: 2014-02-26 Last updated: 2018-01-11Bibliographically approved
  • 19.
    Memedi, Mevludin
    Örebro University, School of Science and Technology.
    Mobile systems for monitoring Parkinson's disease2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis presents the development and evaluation of IT-based methods and systems for supporting assessment of symptoms and enabling remote monitoring of Parkinson‟s disease (PD) patients. PD is a common neurological disorder associated with impaired body movements. Its clinical management regarding treatment outcomes and follow-up of patients is complex. In order to reveal the full extent of a patient‟s condition, there is a need for repeated and time-stamped assessments related to both patient‟s perception towards common symptoms and motor function. In this thesis, data from a mobile device test battery, collected during a three year clinical study, was used for the development and evaluation of methods. The data was gathered from a series of tests, consisting of selfassessments and motor tests (tapping and spiral drawing). These tests were carried out repeatedly in a telemedicine setting during week-long test periods. One objective was to develop a computer method that would process tracedspiral drawings and generate a score representing PD-related drawing impairments. The data processing part consisted of using the discrete wavelet transform and principal component analysis. When this computer method was evaluated against human clinical ratings, the results showed that it could perform quantitative assessments of drawing impairment in spirals comparatively well. As a part of this objective, a review of systems and methods for detecting the handwriting and drawing impairment using touch screens was performed. The review showed that measures concerning forces, accelerations, and radial displacements were the most important ones in detecting fine motor movement anomalies. Another objective of this thesis work was to design and evaluate an information system for delivering assessment support information to the treating clinical staff for monitoring PD symptoms in their patients. The system consisted of a patient node for data collection based on the mobile device test battery, a service node for data storage and processing, and a web application for data presentation. A system module was designed for compiling the test battery time series into summary scores on a test period level. The web application allowed adequate graphic feedback of the summary scores to the treating clinical staff. The evaluation results for this integrated system indicate that it can be used as a tool for frequent PD symptom assessments in home environments.

    List of papers
    1. Methods for detection of handwriting/drawing impairment using inputs from touch screens
    Open this publication in new window or tab >>Methods for detection of handwriting/drawing impairment using inputs from touch screens
    2011 (English)In: Recent Patents on Signal Processing, ISSN 1877-6124, Vol. 1, no 2, p. 156-162Article in journal (Refereed) Published
    Abstract [en]

    Fine motor dysfunction in patients with movement disorders, such as Parkinson’s disease, is characterized by slowness of movements, decrease of reaction time and involuntary movements. In this article, recent patents on detecting and assessing the said dysfunction are reviewed; their implementation in telemedicine settings, design considerations and ability to assist in dose and time adjustments are discussed. These patents explain application of signal processing techniques in analysis and interpretation of digitized handwriting/drawing information of individuals based on data gathered using touch screens. The study reveals that measures concerning forces, accelerations and radial displacements are the most relevant measurements to detect fine movement anomalies. These findings demonstrate that digitized analysis of handwriting/drawing movements may be useful in clinical trials evaluating fine motor control. This review further depicts the role of employing event-based data acquisition and signal processing techniques suitable for nonstationary signals, such as Wavelet transform, in systems for patient home-monitoring.

    Place, publisher, year, edition, pages
    Bussum: Bentham Science Publishers, 2011
    Keywords
    Drawing, fine motor impairment, Fourier transform, handwriting, home monitoring, Parkinson’s disease, touch screens, Wavelet transform
    National Category
    Signal Processing
    Research subject
    Signal Processing
    Identifiers
    urn:nbn:se:oru:diva-20463 (URN)10.2174/2210686311101020156 (DOI)
    Available from: 2011-12-02 Created: 2011-12-02 Last updated: 2018-02-15Bibliographically approved
    2. A new computer method for assessing drawing impairment in Parkinson's disease
    Open this publication in new window or tab >>A new computer method for assessing drawing impairment in Parkinson's disease
    Show others...
    2010 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 190, no 1, p. 143-148Article in journal (Refereed) Published
    Abstract [en]

    A test battery, consisting of self-assessments and motor tests (tapping and spiral drawing tasks) was used on 9482 test occasions by 62 patients with advanced Parkinson's disease (PD) in a telemedicine setting. On each test occasion, three Archimedes spirals were traced. A new computer method, using wavelet transforms and principal component analysis processed the spiral drawings to generate a spiral score. In a web interface, two PD specialists rated drawing impairment in spiral drawings from three random test occasions per patient, using a modification of the Bain & Findley 10-category scale. A standardised manual rating was defined as the mean of the two raters’ assessments. Bland-Altman analysis was used to evaluate agreement between the spiral score and the standardised manual rating. Another selection of spiral drawings was used to estimate the Spearman rank correlations between the raters (r = 0.87), and between the mean rating and the spiral score (r = 0.89). The 95% confidence interval for the method's prediction errors was ±1.5 scale units, which was similar to the differences between the human raters. In conclusion, the method could assess PD-related drawing impairments well comparable to trained raters.

    Place, publisher, year, edition, pages
    Amsterdam: Elsevier, 2010
    Keywords
    Test battery, Home environment, Motor test, Tremor, Dyskinesia, Spiral drawing, Drawing impairment, Wavelet transform, Principal component analysis, Involuntary movement, Movement disorders, Motor fluctuations, Parkinson's disease, Telemedicine
    National Category
    Neurosciences Biochemistry and Molecular Biology
    Research subject
    Biochemistry
    Identifiers
    urn:nbn:se:oru:diva-20464 (URN)10.1016/j.jneumeth.2010.04.027 (DOI)000279888800019 ()20438759 (PubMedID)2-s2.0-77953725166 (Scopus ID)
    Available from: 2011-12-02 Created: 2011-12-02 Last updated: 2018-01-12Bibliographically approved
    3. A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients
    Open this publication in new window or tab >>A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients
    Show others...
    2011 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 104, no 2, p. 219-226Article in journal (Refereed) Published
    Abstract [en]

    This paper describes a web-based system for enabling remote monitoring of patients with Parkinson's disease (PD) and supporting clinicians in treating their patients. The system consists of a patient node for subjective and objective data collection based on a handheld computer, a service node for data storage and processing, and a web application for data presentation. Using statistical and machine learning methods, time series of raw data are summarized into scores for conceptual symptom dimensions and an “overall test score” providing a comprehensive profile of patient's health during a test period of about one week. The handheld unit was used quarterly or biannually by 65 patients with advanced PD for up to four years at nine clinics in Sweden. The IBM Computer System Usability Questionnaire was administered to assess nurses’ satisfaction with the web application. Results showed that a majority of the nurses were quite satisfied with the usability although a sizeable minority were not. Our findings support that this system can become an efficient tool to easily access relevant symptom information from the home environment of PD patients.

    Place, publisher, year, edition, pages
    Amsterdam: Elsevier, 2011
    Keywords
    Parkinson's disease, Test battery, Web application, Decision support, Remote patient monitoring, Telemedicine, Principal component analysis
    National Category
    Information Systems
    Research subject
    Computer and Systems Science
    Identifiers
    urn:nbn:se:oru:diva-20469 (URN)10.1016/j.cmpb.2011.07.017 (DOI)000296945100024 ()21872355 (PubMedID)2-s2.0-80054120422 (Scopus ID)
    Note

    7th IFAC Symposium on Modelling and Control in Biomedical Systems

    Available from: 2011-12-02 Created: 2011-12-02 Last updated: 2018-01-12Bibliographically approved
  • 20.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Aghanavesi, S.
    Nyholm, Dag
    Askmark, H.
    Aquilonius, S.- M.
    Constantinescu, R.
    Bergquist, F.
    Medvedev, A.
    Ericsson, A.
    Ohlsson, F.
    Lycke, S.
    Spira, J.
    Senek, M.
    Westin, J.
    Upper limb motor tests are related to clinical ratings of motor function in advanced Parkinson's disease2016Conference paper (Other academic)
  • 21.
    Memedi, Mevludin
    et al.
    Örebro University, Örebro University School of Business.
    Aghanavesi, Somayeh
    th Computer Engineering, Dalarna University, Sweden.
    Bergquist, Filip
    Department of Pharmacology, Gothenburg University, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Uppsala University, Sweden (.
    Senek, Marina
    Department of Neuroscience, Uppsala University, Sweden.
    A multimodal sensor fusion platform for objective assessment of motor states in Parkinson's disease2019In: IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI 19), 2019Conference paper (Refereed)
    Abstract [en]

    This study proposes a platform to objectively assess motor states in Parkinson’s disease (PD) using sensor technology and machine learning. The platform uses sensor information gathered during standardized motor tasks and fuses them in a data-driven manner to produce an index representing motor states of the patients. After investigating clinimetric properties of the platform it was found that the platform had good validity and responsiveness to treatment, which are essential for developing systems to individualize treatments.

  • 22.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Aghanavesi, Somayeh
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    A method for measuring Parkinson's disease related temporal irregularity in spiral drawings2016In: 2016 IEEE International Conference on Biomedical and Health Informatics, New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 410-413Conference paper (Refereed)
    Abstract [en]

    The objective of this paper was to develop and evaluate clinimetric properties of a method for measuring Parkinson's disease (PD)-related temporal irregularities using digital spiral analysis. In total, 108 (98 patients in different stages of PD and 10 healthy elderly subjects) performed repeated spiral drawing tasks in their home environments using a touch screen device. A score was developed for representing the amount of temporal irregularity during spiral drawing tasks, using Approximate Entropy (ApEn) technique. In addition, two previously published spiral scoring methods were adapted and their scores were analyzed. The mean temporal irregularity score differed significantly between healthy elderly subjects and advanced PD patients (P<0.005). The ApEn-based method had a better responsiveness and test-retest reliability when compared to the other two methods. In contrast to the other methods, the mean scores of the ApEn-based method improved significantly during a 3 year clinical study, indicating a possible impact of pathological basal ganglia oscillations in temporal control during spiral drawing tasks. In conclusion, the ApEn-based method could be used for differentiating between patients in different stages of PD and healthy subjects. The responsiveness and test-retest reliability were good for the ApEn-based method indicating that this method is useful for measuring upper limb temporal irregularity at a micro-level.

  • 23.
    Memedi, Mevludin
    et al.
    Örebro University, Örebro University School of Business. Datateknik, Högskolan Dalarna, Borlänge, Sweden.
    Aghanavesi, Somayeh
    Datateknik, Högskolan Dalarna, Borlänge, Sweden.
    Westin, Jerker
    Datateknik, Högskolan Dalarna, Borlänge, Sweden.
    Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease2015Conference paper (Other academic)
    Abstract [en]

    OBJECTIVES: To develop a method for objective assessment of fine motor timing variability in Parkinson’s disease (PD) patients, using digital spiral data gathered by a touch screen device.

    BACKGROUND: A retrospective analysis was conducted on data from 105 subjects including65 patients with advanced PD (group A), 15 intermediate patients experiencing motor fluctuations (group I), 15 early stage patients (group S), and 10 healthy elderly subjects (HE) were examined. The subjects were asked to perform repeated upper limb motor tasks by tracing a pre-drawn Archimedes spiral as shown on the screen of the device. The spiral tracing test was performed using an ergonomic pen stylus, using dominant hand. The test was repeated three times per test occasion and the subjects were instructed to complete it within 10 seconds. Digital spiral data including stylus position (x-ycoordinates) and timestamps (milliseconds) were collected and used in subsequent analysis. The total number of observations with the test battery were as follows: Swedish group (n=10079), Italian I group (n=822), Italian S group (n = 811), and HE (n=299).

    METHODS: The raw spiral data were processed with three data processing methods. To quantify motor timing variability during spiral drawing tasks Approximate Entropy (APEN) method was applied on digitized spiral data. APEN is designed to capture the amount of irregularity or complexity in time series. APEN requires determination of two parameters, namely, the window size and similarity measure. In our work and after experimentation, window size was set to 4 and similarity measure to 0.2 (20% of the standard deviation of the time series). The final score obtained by APEN was normalized by total drawing completion time and used in subsequent analysis. The score generated by this method is hence on denoted APEN. In addition, two more methods were applied on digital spiral data and their scores were used in subsequent analysis. The first method was based on Digital Wavelet Transform and Principal Component Analysis and generated a score representing spiral drawing impairment. The score generated by this method is hence on denoted WAV. The second method was based on standard deviation of frequency filtered drawing velocity. The score generated by this method is hence on denoted SDDV. Linear mixed-effects (LME) models were used to evaluate mean differences of the spiral scores of the three methods across the four subject groups. Test-retest reliability of the three scores was assessed after taking mean of the three possible correlations (Spearman’s rank coefficients) between the three test trials. Internal consistency of the methods was assessed by calculating correlations between their scores.

    RESULTS: When comparing mean spiral scores between the four subject groups, the APEN scores were different between HE subjects and three patient groups (P=0.626 for S group with 9.9% mean value difference, P=0.089 for I group with 30.2%, and P=0.0019 for A group with 44.1%). However, there were no significant differences in mean scores of the other two methods, except for the WAV between the HE and A groups (P<0.001). WAV and SDDV were highly and significantly correlated to each other with a coefficient of 0.69. However, APEN was not correlated to neither WAV nor SDDV with coefficients of 0.11 and 0.12, respectively. Test-retest reliability coefficients of the three scores were as follows: APEN (0.9), WAV(0.83) and SD-DV (0.55).

    CONCLUSIONS: The results show that the digital spiral analysis-based objective APEN measure is able to significantly differentiate the healthy subjects from patients at advanced level. In contrast to the other two methods (WAV and SDDV) that are designed to quantify dyskinesias (over-medications), this method can be useful for characterizing Off symptoms in PD. The APEN was not correlated to none of the other two methods indicating that it measures a different construct of upper limb motor function in PD patients than WAV and SDDV. The APEN also had a better test-retest reliability indicating that it is more stable and consistent over time than WAV and SDDV.

  • 24.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Aghanavesi, Somayeh
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Objective quantification of Parkinson's disease upper limb motor timing variability using spirography2015Conference paper (Refereed)
    Abstract [en]

    Objective quantification of the upper limb motor timing variability of Parkinson’s disease (PD) patients was evaluated using traces of spirals by groups of patients at different disease stages, stable (S), intermediate (I), advanced (A) and a healthy elderly (HE) group. The approximate entropy (APEN) method of quantifying motor timing variability in time series was applied to capture the amount of irregularity during the spiral drawing process. The APEN score was then normalized by total drawing completion time and used in subsequent analysis. In addition, two previously published methods (WAV and SDDV) were applied on the spiral data. Comparing subject groups’ APEN mean scores, they were found to be significantly different from HE group, for group A (P<0.001) indicating this method’s ability in distinguishing patients at advanced disease stage. Comparing the three methods’ ability to track response to advanced treatment, APEN scores were all significantly different between base-line and levodopa-carbidopa intestinal gel (LCIG) treatment, during the 36 month study period as opposed to WAV and SDDV as they were not significantly improving for all periods. APEN scores were weakly correlated to WAV and SDDV, indicating that they measure different aspects of symptom severity.

  • 25.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Bergqvist, Ulf
    Nordforce Technology AB, Hägersten, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    A web-based system for visualizing upper limb motor performance of Parkinson's disease patients2013Conference paper (Other academic)
    Abstract [en]

    Objective: To design, develop and set up a web-based system for enabling graphical visualization of upper limb motor performance (ULMP) of Parkinson’s disease (PD) patients to clinicians.

    Background: Sixty-five patients diagnosed with advanced PD have used a wireless handheld computer, consisting of upper limb motor tests (finger to tapping and spiral drawings), in their home environments over the course of a 3 year clinical study. For the tapping tests, they were asked to perform alternate tapping of two buttons as fast and accurate as possible, first using the right hand and then the left hand. The spiral test included tracing a pre-drawn Archimedes spiral using the dominant hand and the test was repeated three times per test occasion.

    Methods: The system employs advanced graphics such as animations and time-series plots to visualize the ULMP of PD patients to three trained neurologists. Performance during spiral tests is shown by animating the three spiral drawings, allowing the neurologists to observe real-time accelerations (or hesitations) during the actual drawing. Tapping performance is visualized by displaying graphs like distribution of taps over the two buttons, deviation of taps on horizontal axis, deviation of taps on vertical axis, and tapping reaction time.

    Results: Different scales are utilized by neurologists to rate the observed impairments. For the spiral drawing performance, neurologists rate firstly the impairment using a 0 (no impairment) – 10 (extremely severe) scale and secondly the probable cause for the said impairment using 3 choices including Tremor, Bradykinesia/Rigidity and Dyskinesia. For the tapping performance, a 0 (normal) – 4 (severe) scale is used for rating Speed, Accuracy, Fatigue, Arrhythmia and Global Tapping Severity. The system is currently in use and results (intra- and inter-neurologist agreements) will be available for the poster presentation.

    Conclusions: In contrast from current approaches used in clinical settings for the assessment of PD symptoms, this system enables clinicians to animate easily and realistically the ULMP of PD patients who at the same time are at their homes.

  • 26.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Jusufi, Ilir
    Computer Science, University of California, Davis, USA.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Visualization of spirography-based objective measures in Parkinson's disease2014Conference paper (Other academic)
    Abstract [en]

    Objective: To investigate whether advanced visualizations of spirography-based objective measures are useful in differentiating motor complications among Parkinson’s disease (PD) patients.

    Background: Sixty-five patients diagnosed with advanced PD have utilized a telemetry test battery, implemented on a touch screen handheld computer, in a telemedicine setting. On each test occasion, they were asked to perform repeated and time-stamped assessments of spiral drawing performance by tracing a pre-drawn Archimedes spiral. The test battery was also used by 10 healthy elderly (HE) subjects.

    Methods: A web-based framework was developed to visualize the performance during spirography of both patients and HE subjects to a clinician (DN). The performance was depicted by animating the spiral drawings (Fig 1). In addition, the framework displayed two time series views for representing drawing speed (blue line) and displacement from the ideal trajectory (orange line). The views are coordinated and linked i.e. user interactions in one of the views will be reflected in other views. For instance, when the user points in one of the pixels in spiral view, the circle size of the underlying pixel increases and a vertical line appears in the time series views to depict the corresponding position. Fig 1 shows single randomly selected spirals per each subject group: A) a PD patient in Dyskinesia state, B) a HE subject, and C) a PD patient in Off state.

    Results: The clinician recognized Dyskinesia symptoms as movements made with high speed, smooth/gradual spatial displacements, and a small amount of hesitation (Fig 1A). Similarly, Off symptoms were associated with low speed, sharp/abrupt spatial displacements, and a large amount of hesitation (Fig 1C). In contrast, the spiral drawn by a HE subject (Fig 1B) was associated with unchanging levels of kinematic features i.e. drawing speed, spatial displacements and hesitation over time.

    Conclusions: Visualizing spirography-based objective measures enables identification of trends and patterns of motor dysfunctions at the patient’s individual level. Dynamic access of visualized motor tests may be useful during the evaluation of therapy-related complications such as under- and over-medications. This will assist during individualized optimization of therapies, enabling patients to spend more time in the On state with a minimum of Off and dyskinetic states.

  • 27.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology. School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Khan, Taha
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden; School of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden.
    Grenholm, Peter
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Automatic and objective assessment of alternating tapping performance in parkinson’s disease2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 12, p. 16965-16984Article in journal (Refereed)
    Abstract [en]

    This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson‟s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions („speed‟, „accuracy‟, „fatigue‟ and „arrhythmia‟) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regressionclassifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson‟s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance ofPD patients and can be included in telemedicine tools for remote monitoring of tapping.

  • 28.
    Memedi, Mevludin
    et al.
    Örebro University, Örebro University School of Business.
    Lindqvist, Joakim
    Örebro University, Örebro, Sweden.
    Tunedal, Tobias
    Örebro University, Örebro, Sweden.
    Duvåker, Axel
    Örebro University, Örebro, Sweden.
    A study on pre-adoption of a self-management application by Parkinson’s disease patients2018Conference paper (Refereed)
    Abstract [en]

    The aim of this paper is to provide an overview of factors influencing the acceptance by Parkinson's disease (PD) patients of a self-management application for an Internet of Things system. Unified Theory of Acceptance and Use of Technology (UTAUT) factors including performance expectancy, effort expectancy, and social influence were tested along with sociodemographic (age and gender) and technology-associated (experience with modern technology) factors to determine their contributions for predicting behavioral intention to use the application. Fifty respondents completed the survey. The results show that the UTAUT-based factors, sociodemographic and technology-associated factors account for 82.9% of the variability in PD patients' behavioralintention to use the application. We found that women were significantly more positive than men (p<0.001) in their intention to use the application. If offered the application in the future, 70% of the respondents would use it. Respondents with lower level of experience with technology had less intention to use the application. Performance expectancy and social influence were the only factors that positively predicted intention to use the application. The results showed high scores related to intention to use the application, suggesting high acceptance of the application by the PD patients. Based on qualitative results, the application was seen by PD patients as a useful tool for providing them a better overview of their health status. Finally, the acceptance of the application can be increased by showing its benefits to the PD patients and by developing social strategies to encourage them to stimulate each other to use the application.

  • 29.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Computerized identification of motor complications in Parkinson's disease2014Conference paper (Other academic)
    Abstract [en]

    Objective: To investigate whether spirography-based objective measures of motor dysfunctions are able to discriminate between Parkinson’s disease (PD) patients with different motor states (Off and Dyskinesia) and healthy elderly (HE) subjects.

    Background: Sixty-five advanced PD patients and 10 HE subjects performed repeated assessments of spirography, using a touch screen telemetry device. On each test occasion, they were asked to trace a pre-drawn Archimedes spiral using dominant hand and repeating the test three times. The clinical assessment was only performed in the patient group by animating the three spirals in a web interface, allowing a clinician (DN) to observe accelerations and spa-tial changes during the drawing process. A scale ranging from 0 (normal) to 4 (extremely severe) was used for the assessment of kinematic properties of speed, irregularity and hesitation. Finally, the momentary motor state of the patient was marked using two classes: - 1 (Off) and 1 (Dyskinesia). The HE samples were assigned a 0 (On) class and used in subsequent analysis.

    Methods: After time series analysis, 13 quantitative measures were calculated for representing the severity of symptoms in each individual kinematic property. Principal Component Analysis was then used to reduce their dimensions by retaining the first 4 principal components (PC). To investigate differences in mean PC scores across the three classes a one-way ANOVA test followed by Tukey multiple comparisons was used. An ordinal logistic regression model, using 10-fold cross-validation, was used to map the 4 PC to the corresponding motor state classes.

    Results: The agreements between computer and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91 (Table 1). The mean PC scores were different across the three classes, only at different levels (Fig 1). The Spearman’s rank correlations between the first two PC and visually assessed kinematic properties were: speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17) and hesitation (PC1, 0.27; PC2, 0.77).

    Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits in PD. The differences among the three classes imply that these measures can be used to assess changes in the motor states in response to therapeutic interventions.

  • 30.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Johansson, Anders
    Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Pålhagen, Sven
    Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Willows, Thomas
    Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Widner, Håkan
    Neurology, Skåne University Hospital, Lund, Sweden.
    Linder, J.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Self-reported symptoms and motor tests via telemetry in a 36-month levodopa-carbidopa intestinal gel infusion trial2013Conference paper (Other academic)
    Abstract [en]

    Objective: To determine if a home environment test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression.

    Background: Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study. On inclusion, 35 of them were treated with continuous intraduodenal administration of a levodopa-carbidopa intestinal gel (LCIG) and 30 patients were candidates for switching from conventional oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments and fine motor tests (tapping and spiral drawings), in their homes. Assessments were performed four times per day during week-long test periods. For the majority of these test periods, UPDRS and PDQ-39 ratings were performed at the start of the period.

    Methods: The test battery time series were summarized into scores for representing symptom severities over test periods. Six conceptual dimensions were defined; four subjectively-reported: ‘Walking’, ‘Satisfied’, ‘Dyskinesia’ and ‘Off’, and two objectively-measured: ‘Tapping’ and ‘Spiral’. In addition, an overall test score (OTS) was defined to represent the overall condition of a patient during a test period.

    Results: In LCIG-naïve patients, mean OTS improved startingf rom the first test period on LCIG treatment and this improvement remained statistically significant until month 24 (figure). In contrast to objectively-measured dimensions, mean scores of subjectively-reported dimensions improved significantly throughout the study. In LCIG-non-na€ıve patients, there were no significant changes in mean OTS, except at month 36 (p < 0.01). The OTS correlated adequately with total UPDRS (rho 5 0.59) and total PDQ-39 (0.59).

    Conclusions: Using the test battery it is possible to monitor PD symptoms over time. The trends of the test scores were strikingly similar to the trends of the clinical rating scores. Correlations between OTS and the rating scales were adequate indicating that the test battery contains important elements of the information of these well-established scales.

  • 31.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Pålhagen, Sven
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Willows, Thomas
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Widner, Håkan
    Department of Neurology, Skåne University Hospital, Lund, Sweden.
    Linder, Jan
    Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Validity and Responsiveness of At-Home Touch Screen Assessments in Advanced Parkinson's Disease2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 6, p. 1829-1834Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson's disease (PD) treatment intervention and disease progression in patients with fluctuations. Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on unified PD rating scale (UPDRS) and 39-item PD questionnaire (PDQ-39) scales. In LCIG-naïve patients, the mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG-nonnaïve patients, there were no significant changes in the mean OTS until month 36. The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59). Responsiveness measured as effect size was 0.696 and 0.536 for OTS and UPDRS, respectively. The trends of the test scores were similar to the trends of clinical rating scores but the dropout rate was high. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of well-established scales. The responsiveness and reproducibility were better for OTS than for total UPDRS.

  • 32.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology. School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Pålhagen, Sven-Erik
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Willows, Thomas
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Widner, Håkan
    Department of Neurology, Skåne University Hospital, Lund, Sweden.
    Linder, Jan
    Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Self-assessments and motor test via telemetry in a 36-month levodopa-carbidopa intestinal gelinfusion trialManuscript (preprint) (Other academic)
  • 33.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology. Academy of Industry and Society, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    Academy of Industry and Society, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Combined fine-motor tests and self-assessments for remote detection of motor fluctuations2013In: Recent Patents on Biomedical Engineering, ISSN 1874-7647, Vol. 6, no 2, p. 127-135Article in journal (Refereed)
    Abstract [en]

    A major problem with the clinical management of fluctuating movement disorders, e.g. Parkinson’s disease (PD), is the large variability in manifestation of symptoms among patients. In this condition, frequent measurements which account for both patient-reported and objective assessments are needed in order to capture symptom fluctuations, with the purpose to optimize therapy. The main focus of this paper is to present a mobile-based system for enabling remote monitoring of PD patients from their home environment conditions. The system consists of a patient diary section for collecting patient-based self-assessments, a motor test section for collecting fine motor movements through upper limb motor tests, and a scheduler for restricting operation to a multitude of predetermined limited time intervals. The system processes and compiles time series data into different summary scores representing symptom severity. In addition, the paper presents a review of recent inventions which were filed after year 2000 in the field of telemedicine applications. The review includes a summary of systems and methods which enable remote symptom assessments of patients, not necessarily suffering from movement disorders, through repeated measurements and which take into account their subjective and/or objective health indicators. The findings conclude that there are a small number of inventions which collect subjective and objective health measures in telemedicine settings. Consequently, there is a lack of mechanisms that combine these two types of information into scores to provide a more in-depth assessment of the patient’s general health, their motor and non-motor symptom fluctuations and treatment effects. The paper also provides a discussion concerning different approaches for analyzing and combining subjective and objective measures, and handling data from longitudinal studies.

  • 34.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Sadikov, Aleksander
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Zabkar, Jure
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Mozina, Martin
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Department of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Deitrich
    Clinical Trials Unit, Office of the Clinical Director, NINDS Intramural Research Program, National Institutes of Health, Bethesda MD, USA.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson’s Disease2015In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 9, p. 23727-23744Article in journal (Refereed)
    Abstract [en]

    A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

  • 35.
    Memedi, Mevludin
    et al.
    Örebro University, Örebro University School of Business. Datateknik, Högskolan Dalarna, Borlänge, Sweden.
    Sadikov, Aleksander
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Department of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Neurology, Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Dietrich
    NINDS Intramural Research Program, Clinical Trials Unit, National Institutes of Health, Bethesda MD, USA.
    Nyholm, Dag
    Neurology, Neuroscience, Uppsala University, Uppsala, Sweden.
    Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease2015Conference paper (Other academic)
    Abstract [en]

    Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists.

    Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip.

    Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed.

    Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3.

    Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

  • 36.
    Memedi, Mevludin
    et al.
    Örebro University, Örebro University School of Business. Computer Engineering, Dalarna University, Falun, Sweden.
    Thomas, Ilias
    Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    Computer Engineering, Dalarna University, Falun, Sweden.
    Senek, Marina
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Aghanavesi, Somayeh
    Computer Engineering, Dalarna University, Falun, Sweden.
    Medvedev, Alexander
    Information Technology, Uppsala University, Uppsala, Sweden.
    Askmark, Håkan
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Aquilonius, Sten-Magnus
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Constantinescu, Radu
    Departement of Clinical Neuroscience, University of Gothenburg, Gothenburg, Sweden.
    Ohlsson, Fredrik
    Acreo Swedish ICT, Stockholm, Sweden.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden.
    Lycke, Sara
    Cenvigo AB, Uppsala, Sweden.
    Ericsson, Anders
    Acreo Swedish ICT, Stockholm, Sweden.
    Construction of levodopa-response index from wearable sensors for quantifying Parkinson's disease motor functions2016Conference paper (Other academic)
    Abstract [en]

    The goal of this study was to investigate the feasibility of wrist worn motion sensors to objectively measure motor functions in Parkinson’s disease (PD). More specifically, the aim was to construct a sensor-based levodopa-response index (SBLRI) and evaluate its clinimetric properties (convergent validity and internal consistency). Nineteen advanced PD patients and 22 healthy controls were recruited in a single center, open label, single dose clinical trial in Sweden. The subjects performed standardized motor tasks while wearing one sensor on each wrist and one on each ankle. Each sensor unit consisted of three-dimensional accelerometer and gyroscope. The patients were video recorded and the videos were blindly rated by three independent movement disorder specialists. The clinical scores were given using the Treatment Response Scale (TRS) on a scale from -3 = ‘Very Off’ to 0 = ‘On’ to +3 = ‘Very dyskinetic’. The clinical assessments were based on the overall motor function of the patients. A mean TRS was defined as the mean of the three specialists’ assessments per time point. The measurements were repeated over several time points following a single levodopa/carbidopa morning dose (50% over normal to induce dyskinesias). Sensor measurements during rapid alternating movements of hands were processed with time series analysis methods to calculate spatiotemporal parameters designed to measure bradykinesia and dyskinesia. For each hand, 96 spatiotemporal parameters were calculated and their average scores were then used in a principal component analysis to reduce the dimensionality by retaining 6 principal components. These components were then used as predictors to support vector machines and to be mapped to the mean TRS ratings of the three specialists and to calculate the SBLRI. For this analysis, a 10-fold stratified cross-validation was performed. The SBLRI was strongly correlated to mean TRS with a Pearson correlation coefficient of 0.79 (CI: 0.74-0.83, p<0.001). The 95% confidence interval for the mean squared error of SBLRI on patients data was ± 1.62 with a mean value of 0.57 whereas on healthy controls data was ± 1 with a mean value of 0.27. The sensor-based spatiotemporal parameters had good internal consistency with a Cronbach’s Alpha coefficient of 0.87 and significantly differed between patients and healthy controls. The results demonstrated that the SBLRI had good clinimetric properties for measuring motor functions (Off and dyskinesia) in PD patients. The method could also distinguish hand rotation movements exhibited by patients from those exhibited by healthy controls. The SBLRI provides effect-time profiles, which could be useful during therapy individualization of advanced PD patients.

  • 37.
    Memedi, Mevludin
    et al.
    Örebro University, Örebro University School of Business.
    Tshering, Gaki
    Informatics, Business School, Örebro University, Örebro, Sweden.
    Fogelberg, Martin
    Informatics, Business School, Örebro University, Örebro, Sweden.
    Jusufi, Ilir
    Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden.
    Kolkowska, Ella
    Örebro University, Örebro University School of Business.
    Klein, Gunnar O.
    Örebro University, Örebro University School of Business.
    An interface for IoT: feeding back health-related data to Parkinson's disease patients2018In: Journal of Sensor and Actuator Networks, E-ISSN 2224-2708, Vol. 7, no 1, article id 14Article in journal (Refereed)
    Abstract [en]

    This paper presents a user-centered design (UCD) process of an interface for Parkinson’s disease (PD) patients for helping them to better manage their symptoms. The interface is designed to visualize symptom and medication information, collected by an Internet of Things (IoT)-based system, which will consist of a smartphone, electronic dosing device, wrist sensor and a bed sensor. In our work, the focus is on measuring data related to some of the main health-related quality of life aspects such as motor function, sleep, medication compliance, meal intake timing in relation to medication intake, and physical exercise. A mock-up demonstrator for the interface was developed using UCD methodology in collaboration with PD patients. The research work was performed as an iterative design and evaluation process based on interviews and observations with 11 PD patients. Additional usability evaluations were conducted with three information visualization experts. Contributions include a list of requirements for the interface, results evaluating the performance of the patients when using the demonstrator during task-based evaluation sessions as well as opinions of the experts. The list of requirements included ability of the patients to track an ideal day, so they could repeat certain activities in the future as well as determine how the scores are related to each other. The patients found the visualizations as clear and easy to understand and could successfully perform the tasks. The evaluation with experts showed that the visualizations are in line with the current standards and guidelines for the intended group of users. In conclusion, the results from this work indicate that the proposed system can be considered as a tool for assisting patients in better management of the disease by giving them insights on their own aggregated symptom and medication information. However, the actual effects of providing such feedback to patients on their health-related quality of life should be investigated in a clinical trial.

  • 38.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology.
    Westin, Jerker
    Academy of Industry and Society, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Methods for detection of handwriting/drawing impairment using inputs from touch screens2011In: Recent Patents on Signal Processing, ISSN 1877-6124, Vol. 1, no 2, p. 156-162Article in journal (Refereed)
    Abstract [en]

    Fine motor dysfunction in patients with movement disorders, such as Parkinson’s disease, is characterized by slowness of movements, decrease of reaction time and involuntary movements. In this article, recent patents on detecting and assessing the said dysfunction are reviewed; their implementation in telemedicine settings, design considerations and ability to assist in dose and time adjustments are discussed. These patents explain application of signal processing techniques in analysis and interpretation of digitized handwriting/drawing information of individuals based on data gathered using touch screens. The study reveals that measures concerning forces, accelerations and radial displacements are the most relevant measurements to detect fine movement anomalies. These findings demonstrate that digitized analysis of handwriting/drawing movements may be useful in clinical trials evaluating fine motor control. This review further depicts the role of employing event-based data acquisition and signal processing techniques suitable for nonstationary signals, such as Wavelet transform, in systems for patient home-monitoring.

  • 39.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Westin, Jerker
    Spatial and temporal variability during spirography2015Conference paper (Other academic)
    Abstract [en]

    Objective: To investigate variability of spatial and temporal performance of Parkinson’s disease (PD) patients and healthy elderly subjects during spirography tasks.

    Background: A total of 105 subjects were recruited, comprising of 65 patients with advanced PD (mean 6 sd age; 65 6 11), 15 patients intermediate stage patients experiencing motor fluctuations (65 6 6), 15 clinically stable patients (65 6 6) and 10 healthy elderly subjects (61 6 7). Thirty of the 65 advanced patients switched from oral to continuous administration of treatment via pump. The subjects used a telemetry touch screen device in their home environment settings. On each test occasion, they were asked to trace pre-drawn Archimedes spirals as accurately and fast as possible.

    Methods: Spatial and temporal variability were measured by computing four intra-individual measures of drawing displacement and speed, respectively. Combined scores were derived by applying principal component analysis on these measures and retaining the first component. One-way ANOVA test followed by Tukey multiple comparisons test was used to assess differences between the groups.

    Results: Patients with advanced PD had slower drawing speed and more spatial variability than moderate patients, stable patients and healthy elderly subjects with a mean increase of 76%, 89% and 90% (p<0.001), respectively. The spatial variability of stable patients did not differ from healthy elderly subjects. In contrast, the mean temporal variability did not differ between healthy subjects, stable patients and advanced patients. Moderate patients had higher temporal variability than other groups.

    Conclusions: The findings suggest that patients with PD can be best discriminated from healthy subjects on measures of spatial variability rather than temporal variability. Spatial variability measures are not useful for diagnosing PD but can be used for measuring the severity of symptoms.

  • 40.
    Memedi, Mevludin
    et al.
    Academy of Industry and Society, Computer Science, Dalarna University, Borlänge, Sweden.
    Westin, Jerker
    Academy of Industry and Society, Computer Science, Dalarna University, Borlänge, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Finger tapping 'off' performance in Parkinson's disease is detected by digital signal processing2012Conference paper (Other academic)
    Abstract [en]

    Objective: To process tapping test results collected by a test battery device using signal processing methods. To analyze the tapping performance of PD patients in relation to their self-assessed motor conditions.

    Background: A series of tests in a mobile device test battery, consisting of self-assessments and motor tests, were carried out repeatedly in a telemedicine setting. The test battery has been used by 65 patients with advanced PD in Sweden and 33 Italian patients; including 17 patients experiencing on-off fluctuations and 16 who were clinically stable. On test occasions, patients were asked to self-assess their momentary motor condition, just before starting 20-seconds-long tapping tests. There were three types of tapping tests: alternate tapping (AT), tapping with increasing speed (IS) and random chasing (RC).

    Methods: Three self-assessed motor states were used in the analysis: OFF (including slightly, moderately and very off), ON (on without dyskinesias) and DYS (slightly, moderately and very dyskinetic). Digital signal processing and multivariate analysis methods based on data from the 65 Swedish patients were employed to process and summarize tapping results into scores for tapping distance and time, where 0 is worst and 1 is best possible scores. The scoring methods were then applied on the Italian data set. Fifty random samples of single test occasions per patient were drawn and then used in subsequent analysis. One-way ANOVA test with Tukey comparisons test was used to test whether the mean tapping scores were different among the self-assessed motor states. Statistical significance was set at p<0.05.

    Results: In the stable group, the tapping distance scores were not different across the motor states for any test. The tapping time scores during AT were lower in OFF than in ON and DYS. During RC, the time scores were lower in OFF han in ON. In the fluctuating group, the tapping distance scores during AT were lower in OFF than in ON and DYS. The tapping time scores during RC were not different (p50.096) whereas during AT, time scores were higher in ON than in OFF and DYS.

    Conclusions: Tapping scores in patient-rated OFF states were detected as poor performance by the signal processing method. The AT test shows best performance in separating the motor conditions and tapping time is a more important variable than tapping distance.

  • 41.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology. School of Technology and Business Studies, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Spiral drawing during self-rated dyskinesia is more impaired than during self-rated off2013In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126, Vol. 19, no 5, p. 553-556Article in journal (Refereed)
    Abstract [en]

    Objective: The purpose of this study was to examine repeated measures of fine motor function in relation to self-assessed motor conditions in Parkinson's disease (PD).

    Methods: One-hundred PD patients, 65 with advanced PD and 35 patients with different disease stages have utilized a test battery in a telemedicine setting. On each test occasion, they initially self-assessed their motor condition (from 'very off' to 'very dyskinetic') and then performed a set of fine motor tests (tapping and spiral drawings).

    Results: The motor tests scores were found to be the best during self-rated On. Self-rated dyskinesias caused more impaired spiral drawing performance (mean = 9.8% worse, P < 0.001) but at the same time tapping speed was faster (mean = 5.0% increase, P < 0.001), compared to scores in self-rated Off.

    Conclusions: The fine motor tests of the test battery capture different symptoms; the spiral impairment primarily relates to dyskinesias whereas the tapping speed captures the Off symptoms.

  • 42.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology. Department of Economy and Society, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Westin, Jerker
    Department of Economy and Society, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Dougherty, Mark
    Department of Economy and Society, Computer Engineering, Dalarna University, Borlänge, Sweden.
    Groth, Torgny
    Department of Medical Sciences, Biomedical Informatics and Engineering, Uppsala University, Uppsala, Sweden.
    A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients2011In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 104, no 2, p. 219-226Article in journal (Refereed)
    Abstract [en]

    This paper describes a web-based system for enabling remote monitoring of patients with Parkinson's disease (PD) and supporting clinicians in treating their patients. The system consists of a patient node for subjective and objective data collection based on a handheld computer, a service node for data storage and processing, and a web application for data presentation. Using statistical and machine learning methods, time series of raw data are summarized into scores for conceptual symptom dimensions and an “overall test score” providing a comprehensive profile of patient's health during a test period of about one week. The handheld unit was used quarterly or biannually by 65 patients with advanced PD for up to four years at nine clinics in Sweden. The IBM Computer System Usability Questionnaire was administered to assess nurses’ satisfaction with the web application. Results showed that a majority of the nurses were quite satisfied with the usability although a sizeable minority were not. Our findings support that this system can become an efficient tool to easily access relevant symptom information from the home environment of PD patients.

  • 43.
    Sadikov, Aleksander
    et al.
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business. Computer Engineering, Dalarna University, Borlänge, Sweden.
    Georgiev, Dejan
    Department of Neurology, Ljubljana University Medical Centre, Ljubljana, Slovenia.
    Feasibility of spirography features for objective assessment of motor function in Parkinson's disease2017In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 81, p. 54-62Article in journal (Refereed)
    Abstract [en]

    Objective: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication).

    Materials and methods: This work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the “cause” (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification.

    Results: Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases.

    Conclusions: The relatively high classication accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.

  • 44.
    Sadikov, Aleksander
    et al.
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia .
    Žabkar, Jure
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia .
    Možina, Martin
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia .
    Groznik, Vida
    Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia .
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Computer Engineering, Dalarna University, Borlänge, Sweden.
    Feasibility of spirography features for objective assessment of motor symptoms in Parkinson's disease2015In: Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings / [ed] John Holmes, Riccardo Bellazzi, Lucia Sacchi and Niels Peek, Springer , 2015, p. 267-276Conference paper (Refereed)
    Abstract [en]

    Parkinsons disease (PD) is currently incurable, however the proper treatment can ease the symptoms and significantly improve the quality of patients life. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper is to investigate the feasibility of using the features and methodology of a spirography device, originally designed to measure early Parkinsons disease (PD) symptoms, for assessing motor symptoms of advanced PD patients suffering from motor fluctuations. More specifically, the aim is to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). The work combines spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The target outcome was to learn to predict the “cause” of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. Using the machine learning methods with feature descriptions from the Slovenian application resulted in 86% classification accuracy and over 90% AUC, demonstrating the usefulness of this approach for objective monitoring of PD patients.

  • 45.
    Senek, Marina
    et al.
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Aquilonius, Sten-Magnus
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Askmark, Håkan
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Constantinescu, Radu
    Department of Clinical Neuroscience, University of Gothenburg, Gothenburg, Sweden.
    Ericsson, Anders
    Acreo Swedish ICT.
    Lycke, Sara
    Cenvigo AB.
    Medvedev, Alexander
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business. Computer Engineering, Dalarna University, Falun, Sweden.
    Ohlsson, Fredrik
    Acreo Swedish ICT.
    Spira, Jack
    Sensidose AB.
    Westin, Jerker
    Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Departement of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Levodopa/carbidopa microtablets in Parkinson disease: pharmacokinetics and blinded motor assessment2016In: Twentieth International Congress of Parkinson's Disease and Movement Disorders, 2016Conference paper (Other academic)
    Abstract [en]

    The aim of this study was to characterize the levodopa and carbidopa plasma concentration in relation to blinded motor function ratings. This is a part of a study where a Multimodal motor Symptoms Quantification (MuSyQ) platform consisting of three different types of sensors were tested while evoking motor fluctuations with levodopa/carbidopa(LD/CD) microtablets in fluctuating Parkinson’s disease (PD) patients. Today, dose titration and chronic treatment largely relies on the patient’s subjective assessment of symptoms and clinicians’ assessment of patient status during a visit at the clinic. This was a single-center, open-label, single dose study in patients experiencing motor fluctuations. Patients were given 150% of their individual levodopa equivalent morning dose. Blood sampling and motor function testing were conducted for up to 6.5 hours at prespecified time points. The patients performed standardized motor activities for clinical rating in accordance with parts of the Unified Parkinson’s disease Rating Scale (UPDRS) and Unified Dyskinesia RatingScale (UDysRS). Each test cycle was video recorded, and the video sequences were presented to three movement disorder specialists in a randomized order for blinded rating of UPDRS items and the treatment response scale (TRS). Concentration versus time profiles and the pharmacokinetics were compared with a study previously conducted in healthy subjects. Nineteen patients, 14 male and 5 female, were included in the study. The individual LD/CD doses ranged between 110/27.5mg to 410/102.5 mg. The concentration time profiles are similar to the LD/CD microtablet profiles reported in healthy subjects. The blinded video ratings managed to capture the most distinctive movements. This is the first pharmacokinetic study where patients received LD/CD microtablets. For patients fluctuating from 'off' to dyskinetic, the relationship between the plasma concentration and motor function was clearer compared to the patients that fluctuated to a lesser extent.

  • 46.
    Senek, Marina
    et al.
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Aquilonius, Sten-Magnus
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Askmark, Håkan
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Constantinescu, Radu
    Department of Clinical Neuroscience, University of Gothenburg, Gothenburg, Sweden.
    Ericsson, Anders
    Acreo Swedish ICT, Kista, Sweden.
    Lycke, Sara
    Cenvigo, Uppsala, Sweden.
    Medvedev, Alexander
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business. Computer Engineering, Dalarna University, Falun, Sweden.
    Ohlsson, Fredrik
    Acreo Swedish ICT, Kista, Sweden.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden.
    Westin, Jerker
    Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Levodopa/carbidopa microtablets in Parkinson's disease: a study of pharmacokinetics and blinded motor assessment2017In: European Journal of Clinical Pharmacology, ISSN 0031-6970, E-ISSN 1432-1041, Vol. 73, no 5, p. 563-571Article in journal (Refereed)
    Abstract [en]

    Background: Motor function assessments with rating scales in relation to the pharmacokinetics of levodopa may increase the understanding of how to individualize and fine-tune treatments.

    Objectives: This study aimed to investigate the pharmacokinetic profiles of levodopa-carbidopa and the motor function following a single-dose microtablet administration in Parkinson’s disease.

    Methods: This was a single-center, open-label, single-dose study in 19 patients experiencing motor fluctuations. Patients received 150% of their individual levodopa equivalent morning dose in levodopa-carbidopa microtablets. Blood samples were collected at pre-specified time points. Patients were video recorded and motor function was assessed with six UPDRS part III motor items, dyskinesia score, and the treatment response scale (TRS), rated by three blinded movement disorder specialists.

    Results: AUC0–4/dose and Cmax/dose for levodopa was found to be higher in Parkinson’s disease patients compared with healthy subjects from a previous study, (p = 0.0008 and p = 0.026, respectively). The mean time to maximum improvement in sum of six UPDRS items score was 78 min (±59) (n = 16), and the mean time to TRS score maximum effect was 54 min (±51) (n = 15). Mean time to onset of dyskinesia was 41 min (±38) (n = 13).

    Conclusions: In the PD population, following levodopa/carbidopa microtablet administration in fasting state, the Cmax and AUC0–4/dose were found to be higher compared with results from a previous study in young, healthy subjects. A large between subject variability in response and duration of effect was observed, highlighting the importance of a continuous and individual assessment of motor function in order to optimize treatment effect.

  • 47. Senek, Marina
    et al.
    Askmark, Håkan
    Aquilonius, Sten-Magnus
    Bergquist, Filip
    Constantinescu, Radu
    Ericsson, Anders
    Lycke, Sara
    Medvedev, Alexander
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Ohlsson, Fredrik
    Spira, Jack
    Westin, Jerker
    Nyholm, Dag
    Motor fluctuations in relation to plasma concentrations following a single-dose of levodopa/carbidopa microtablets in advanced Parkinson's disease2016Conference paper (Other academic)
    Abstract [en]

    Objective: The aim of this study was to characterize the levodopa and carbidopa plasma concentration in relation to blinded motor function ratings. This is a part of a study where a Multimodal motor Symptoms Quantification (MuSyQ) platform consisting of three different types of sensors were tested while evoking motor fluctuations with levodopa/car-bidopa (LD/CD) microtablets in fluctuating Parkinson’s disease (PD) patients.

    Background: Today, dose titration and chronic treatment largely relies on the patient’s subjective assessment of symptoms and clinicians’ assessment of patient status during a visit at the clinic.

    Methods: This was a single-center, open-label, single dose study in patients experiencing motor fluctuations. Patients were given 150% of their individual levodopa equivalent morning dose. Blood sampling and motor function testing were conducted for up to 6.5 hours at prepecified time points. The patients performed standardized motor activities for clinical rating in accordance with parts of the Unified Parkinson’s disease Rating Scale (UPDRS) and Unified Dyskinesia Rating Scale (UDysRS). Each test cycle was video recorded, and the video sequences were presented to three movement disorder specialists in a randomized order for blinded rating of UPDRS items and the treatment response scale (TRS). Concentration versus time profiles and the pharmacokinetics were compared with a study previously conducted in healthy subjects.

    Results: Nineteen patients, 14 male and 5 female, were included in the study. The individual LD/CD doses ranged between 110/27.5 mg to 410/102.5 mg. The concentration time profiles are similar to the LD/CD microtablet profiles reported in healthy subjects. The blinded video ratings managed to capture the most distinctive movements.

    Conclusions: This is the first pharmacokinetic study where patients received LD/CD microtablets. For patients fluctuating from ‘off’ to dyskinetic, the relationship between the plasma concentration and motor function was clearer compared to the patients that fluctuated to a lesser extent.

  • 48.
    Somayeh, Aghanavesi
    et al.
    Computer Engineering, Dalarna University, Falun, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business. Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Medvedev, Alexander
    Information Technology, Uppsala University, Uppsala, Sweden.
    Askmark, Håkan
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Aquilonius, Sten-Magnus
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Constantinescu, Rad
    Department of Clinical Neuroscience, University of Gothenburg, Gothenburg, Sweden.
    Ohlsson, Fredrik
    Acreo Swedish ICT, Stockholm, Sweden.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden.
    Lycke, Sara
    Cenvigo AB, Uppsala, Sweden.
    Ericsson, Anders
    Acreo Swedish ICT, Stockholm, Sweden.
    Quantification of upper limb motor symptoms of Parkinson's disease using a smartphone2016In: / [ed] Somayeh Aghanavesi, John Wiley & Sons, 2016, Vol. 31 Suppl. 2, no Suppl. 2, p. S640-S641, article id 1948Conference paper (Other academic)
    Abstract [en]

    Objective: The aim of this study is to develop and evaluate methods for quantifying motor symptoms in Parkinson’s disease (PD) using combined upper limb motor test data collected during tapping and spiral drawing tasks by a smart phone.

    Background: PD is a multidimensional and complex disorder affecting motor and non motor functionalities. Assessments of PD symptoms are usually done by clinical rating scales. One of them is the Unified PD Rating Scale (UPDRS) developed to provide comprehensive, efficient and flexible means to monitor PD-related disability and impairments. It has been the most commonly used rating scale. It is composed of four main parts where the third part is designed for rating of motor symptoms. However, UPDRS has relatively poor inter-rater reliability. Another scale that is used to grade motor function of patients is Treatment Response Scale (TRS). A limitation is that there is no general agreement on which parts of the symptomatology should be included in the TRS score. However, the scales have low intra- and inter-rater reliability and their use is limited, as they are only used during in-clinic observations.

    Methods: Participants: Nine-teen patients diagnosed with PD and 22 healthy controls were recruited in a single center, open label, single dose clinical observational study in Sweden. Simultaneous clinical- and smartphone-based measures were collected up to 15 times following a single levodopa/carbidopa morning dose (50% over normal to induce dyskinesias).

    Clinical assessment: Subjects were asked to perform standardized motor tests in accordance with UPDRS and were videotaped. The videos were blindly rated by three movement disorder specialists. The ratings were given based on TRS ranging from -3, 'Very Off' to 0, 'On' to +3, 'Very dyskinetic', three UPDRS motor items (item 23, finger taps; item 25, rapid alternating movements of hands, item 31, body bradykinesia and hypokinesia) and dyskinesia score. Means of the three specialists' assessments per time point on these scales were used in subsequent analysis.

    Smartphone-based data collection: On each test occasion, the subjects performed upper limb motor tests (tapping and spiral drawings) using a smartphone. The subjects were instructed to perform the tests using an ergonomic pen stylus with the device placed on a table and to be seated in a chair. During tapping tests, the subjects were asked to alternately tap two fields (as shown in the screen of the device) as fast and accurate as possible, using first right hand and then left hand. Each tapping test lasted for 20 seconds. During spiral tests, the subjects were instructed to trace a pre-drawn Archimedes spiral as fast and accurate as possible, using the dominant hand. The spiral test was repeated three times per test occasion. The smartphone recorded both position and time-stamps (in milliseconds) of the pen tip.

    Data processing and analysis: The raw tapping and spiral data were processed with time series analysis methods, including both time- and frequency-domains methods. Nineteen and 22 spatiotemporal features were extracted from spiral and tapping data, respectively. Features were calculated to represent various kinematic quantities during the motor tests such as acceleration, speed, time delay, and distance. The features from both tapping and spiral data were used in a Principal Component Analysis and 7 principal components (PCs) were retained, which in turn were used as inputs to a Support Vector Machines (SVM) to be mapped to mean clinical ratings. The analyses were performed with a stratified 10-fold cross-validation. Test-retest reliability of the spiral tests were assessed after calculating correlations between the first PCs for the three spiral tests and then calculating the mean of all possible correlations.

    Results: The correlation coefficients between SVM predictions and mean clinical ratings were as follows: 0.59 for TRS, 0.6 for dyskinesia score, 0.52 for item 23 of UPDRS (finger taps), 0.47 for item 25 of UPDRS (rapid alternating movements of hands), and 0.57 for item 31 of UPDRS (body Bradykinesia and Hypokinesia). The spiral test had a good test-retest reliability with a coefficient of 0.84, indicating that spiral scores are stable and consistent over time. When assessing the ability of the PCs to distinguish between patients and healthy controls the means of 3 out of 7 PCs (PC1, PC2 and PC4) were different between the two groups (p<0.005).

    Conclusions: The upper limb motor tests of the smartphone were able to capture important and relevant symptom information of the clinical rating scales. The methods for quantifying the upper limb motor symptoms of PD patients: had adequate correlations to clinical ratings were able to differentiate between movements of patients and healthy controls, and(Spiral tests) had good test-retest reliability.

  • 49.
    Thangavel, Gomathi
    et al.
    Örebro University, Örebro University School of Business.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Hedström, Karin
    Örebro University, Örebro University School of Business.
    A systematic review of Social Internet of Things: concepts and application areas2019In: Americas Conference on Information Systems 2019, Association for Information Systems, 2019Conference paper (Refereed)
    Abstract [en]

    Internet of Things (IoT) connects machines, devices, sensors and people. This technology is expected to connect billions of devices in the near future. Traditional methods make it very difficult to integrate and maintain so many devices. However, social networks manage to connect and maintain the communication of billions of people using social relationships. Social IoT (SIoT) is an emerging field that uses social relations to connect and maintain devices in IoT networks. This article presents a systematic literature review of conceptual papers in SIoT, together with application areas. The results show two themes from conceptual papers: Objects part of human social loop and have a role in human social network, and Objects form social network. Furthermore the results indicate that, SIoT is mostly applied in smart home environment. These findings will benefit academics and practitioners to better understand SIoT and its applications areas.

  • 50.
    Thomas, Ilias
    et al.
    Department of Micro-data Analysis, Dalarna University, Falun, Sweden.
    Alam, Moudud
    Department of Micro-data Analysis, Dalarna University, Falun, Sweden.
    Bergquist, Filip
    Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Dongni
    Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    Department of Micro-data Analysis, Dalarna University, Falun, Sweden.
    Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson’s disease: a first experience2019In: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, no 3, p. 651-658Article in journal (Refereed)
    Abstract [en]

    Objective: Dosing schedules for oral levodopa in advanced stages of Parkinson’s disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).

    Materials and methods: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson’s KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

    Results: The SBDS maintenance and morning dosing suggestions had a Pearson’s correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician’s adjustments.

    Conclusion: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

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