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Memedi, M., Tshering, G., Fogelberg, M., Jusufi, I., Kolkowska, E. & Klein, G. O. (2018). An interface for IoT: feeding back health-related data to Parkinson's disease patients. Journal of Sensor and Actuator Networks, 7(1), Article ID 14.
Open this publication in new window or tab >>An interface for IoT: feeding back health-related data to Parkinson's disease patients
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2018 (English)In: Journal of Sensor and Actuator Networks, ISSN 1007-7294, E-ISSN 1089-747X, Vol. 7, no 1, article id 14Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2018
Keywords
Information visualization; user-centered design; internet of things; sensor technology; Parkinson’s disease; patient empowerment; quality of life
National Category
Human Computer Interaction
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-65675 (URN)10.3390/jsan7010014 (DOI)000428559500013 ()2-s2.0-85044327671 (Scopus ID)
Projects
EMPARK
Funder
Knowledge Foundation, 20160176
Note

Funding Agencies:

Sensidose AB  

Cenvigo AB  

Nethouse Sverige AB  

Swedish Institute 

Available from: 2018-03-12 Created: 2018-03-12 Last updated: 2018-06-20Bibliographically approved
Aghanavesi, S., Nyholm, D., Senek, M., Bergquist, F. & Memedi, M. (2017). A smartphone-based system to quantify dexterity in Parkinson's disease patients. Informatics in Medicine Unlocked
Open this publication in new window or tab >>A smartphone-based system to quantify dexterity in Parkinson's disease patients
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2017 (English)In: Informatics in Medicine Unlocked, ISSN 2352-9148Article in journal (Refereed) Published
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.

Keywords
Parkinson's disease; Motor assessment; Spiral tests; Tapping tests; Smartphone; Dyskinesia; Bradykinesia; Objective measures; Telemedicine
National Category
Computer and Information Sciences
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-57657 (URN)10.1016/j.imu.2017.05.005 (DOI)
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2018-01-13Bibliographically approved
Thomas, I., Westin, J., Alam, M., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2017). A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states. IEEE journal of biomedical and health informatics
Open this publication in new window or tab >>A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states
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2017 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed) Epub ahead of print
Abstract [en]

The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Machine learning, Levodopa response, Signal processing, Parkinson’s disease, Accelerometry, Wearable sensors, Pattern recognition
National Category
Other Medical Engineering Computer Sciences
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-62864 (URN)10.1109/JBHI.2017.2777926 (DOI)
Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2018-01-13Bibliographically approved
Thomas, I., Bergquist, F., Johansson, D., Nyholm, D., Memedi, M. & Westin, J. (2017). Automated dosing schemes for administration of microtablets of levodopa for Parkinson’s disease, using wearable sensors. In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders: . Paper presented at 21st International Congress of Parkinson's Disease and Movement Disorders, Vancouver, BC, Canada, June 4-8, 2017. John Wiley & Sons, 32
Open this publication in new window or tab >>Automated dosing schemes for administration of microtablets of levodopa for Parkinson’s disease, using wearable sensors
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2017 (English)In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders, John Wiley & Sons, 2017, Vol. 32Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Objective: The aim of this study is to investigate the feasibility of using a dose optimization algorithm for dose individualization of microtables of levodopa.

Background: Motor complications in PD are managed by individualizing oral levodopa treatment. An algorithm that uses sensor ratings (Memedi et al., 2016) to produce individualized dosing schemes was developed. Dosing schemes with specific intervals and dose sizes can be programmed into the device that contains microtables of levodopa-carbidopa.

Methods: A clinical trial was conducted in Gothenburg, Sweden. For a two week period before a test day the patients used the device with a dosing schedule equivalent to their regular doses and wore the Parkinson’s kinetigraph (PKG) to monitor motor states in the last six days. Neurologist FB reprogrammed the device based on the PKG data and the clinical impression during the test day. The patients performed alternating hand movements at a pre-determined time schedule, before and following a single dose of levodopa (120% of the normal morning dose), while wearing one sensor on each wrist (Shimmer3 sensor). The signals from the sensors were mapped to a dose-effect scale, providing an effect value for each test. An individual dose-effect model was fitted to the values and the fitted model was used as input to a dose optimizing algorithm. The algorithm produced optimized dosing suggestions for multiple dosing intervals, a different suggestion for each one. The predicted outcome of each interval was visualized to facilitate the identification of the optimal interval. The dosing suggestions of the algorithm were compared to the experts’ prescriptions for the interval that the neurologist chose. The algorithm derived optimal interval was compared to the neurologist’s therapeutic decision.

Results: Preliminary results from 14 patients have been obtained. In 10 cases the dosing suggestions are within 20% of the actual doses and the interval suggestions within 20 minutes of the neurologist choice. In 3 cases the dosing suggestions deviate more than 20% but the suggested intervals are within 20 minutes of the neurologist choice in 2 of those cases. In 1 case an individual model could not be fitted.

Conclusions: The preliminary results suggest that the described system can suggest appropriate dosing schedules for a majority of the patients.

Place, publisher, year, edition, pages
John Wiley & Sons, 2017
Series
Movement Disorders, ISSN 0885-3185, E-ISSN 1531-8257
Keywords
Sensor technology, individualized treatment, Parkinson's disease
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:oru:diva-57895 (URN)10.1002/mds.27087 (DOI)
Conference
21st International Congress of Parkinson's Disease and Movement Disorders, Vancouver, BC, Canada, June 4-8, 2017
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2018-01-13Bibliographically approved
Senek, M., Aquilonius, S.-M., Askmark, H., Bergquist, F., Constantinescu, R., Ericsson, A., . . . Nyholm, D. (2017). Levodopa/carbidopa microtablets in Parkinson's disease: a study of pharmacokinetics and blinded motor assessment. European Journal of Clinical Pharmacology, 73(5), 563-571
Open this publication in new window or tab >>Levodopa/carbidopa microtablets in Parkinson's disease: a study of pharmacokinetics and blinded motor assessment
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2017 (English)In: European Journal of Clinical Pharmacology, ISSN 0031-6970, E-ISSN 1432-1041, Vol. 73, no 5, p. 563-571Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Heidelberg, Germany: Springer, 2017
Keywords
Parkinson's disease, Pharmacokinetics, Pharmacodynamics, Levodopa
National Category
Clinical Medicine Pharmacology and Toxicology
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-54910 (URN)10.1007/s00228-017-2196-4 (DOI)000399175100006 ()28101657 (PubMedID)2-s2.0-85009807605 (Scopus ID)
Funder
VINNOVA
Available from: 2017-01-24 Created: 2017-01-24 Last updated: 2018-01-13Bibliographically approved
Aghanavesi, S., Memedi, M. & Westin, J. (2017). Measuring temporal irregularity in spiral drawings of patients with Parkinson’s disease. In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders: . Paper presented at 21st International Congress of Parkinson's Disease and Movement Disorders, Vancouver BC, Canada, June 4-8, 2017 (pp. s252-s252). John Wiley & Sons, 32, Article ID 654.
Open this publication in new window or tab >>Measuring temporal irregularity in spiral drawings of patients with Parkinson’s disease
2017 (English)In: 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, Oral presentation with published abstract (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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2017
Series
Movement Disorders, ISSN 0885-3185, E-ISSN 1531-8257 ; 32:Suppl. S2
Keywords
digital spiral analysis, Parkinson's disease
National Category
Computer and Information Sciences Neurology
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-58004 (URN)10.1002/mds.27087 (DOI)000402672302278 ()
Conference
21st International Congress of Parkinson's Disease and Movement Disorders, Vancouver BC, Canada, June 4-8, 2017
Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2018-02-09Bibliographically approved
Thomas, I., Bergquist, F., Constantinescu, R., Nyholm, D., Senek, M. & Memedi, M. (2017). Using measurements from wearable sensors for automatic scoring of Parkinson’s disease motor states. In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders: . Paper presented at 21st International Congress of Parkinson's Disease and Movement Disorders, Vancouver, BC, Canada, June 4-8, 2017. John Wiley & Sons, 32, Article ID 659.
Open this publication in new window or tab >>Using measurements from wearable sensors for automatic scoring of Parkinson’s disease motor states
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2017 (English)In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders, John Wiley & Sons, 2017, Vol. 32, article id 659Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Objective: The aim of this study was to investigate the concurrent validity of an objective gait measure for assessment of motor states in advanced Parkinson’s disease (PD) patients.

Background: Five million people suffer from Parkinson’s disease (PD) worldwide. The use of wearable sensors could help monitor disease progression and medication efficacy.

Methods: A single center, single dose, open label clinical trial was conducted in Uppsala, Sweden in 2015. Patients repeatedly performed a walk task while wearing 3D accelerometry and gyroscope sensor units on all four limbs. Assessments were made once before dose administration, once at time of administration, and at pre-specified timepoints until the medication effect had worn off. Each test took approximately 15 seconds and consisted of a 2.5 meter straight walk, which was repeated three times by making 2 U-turns in the process. The patients were also video recorded and the videos were rated by three movement disorder experts according to a Treatment Response Scale (TRS), ranging from -3 (very Off) to +3 (very dyskinetic) and a Dyskinesia Rating scale (DysRS), ranging from 0 (no dyskinesia) to 4 (severe dyskinesia). For the predictive modelling, 32 features from each sensor were extracted together with 24 features that quantified the differences in symmetry between the sensors (152 total features). Principal component analysis was employed on the features of each sensor and the principal components of the four sensors together with the symmetry features were used as predictors in two separate support vector machine (SVM) models. One model mapped the features to the TRS scale and the other to the DysRS scale.

Results: Preliminary results from 7 patients showed good predictive ability of the SVM models in a leave-one-individual out cross-validation setting. The predictions on the TRS scale had correlation of 0.79 to the experts’ mean ratings and Root Mean Square Error (RMSE) of 0.70. The predictions in the DysRS scale had correlation of 0.79 and RMSE of 0.47.

Conclusions: The results of the study indicate that the use of wearable sensors when performing walking tasks can generate measurements that have a good correlation to subjective expert assessments. This could be useful during individualized evaluation of symptoms and treatments.

Place, publisher, year, edition, pages
John Wiley & Sons, 2017
Series
Movement Disorders, ISSN 1531-8257
Keywords
Sensor technology, movement disorders, Parkinson's disease
National Category
Computer and Information Sciences
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-57894 (URN)10.1002/mds.27087 (DOI)000402672302283 ()
Conference
21st International Congress of Parkinson's Disease and Movement Disorders, Vancouver, BC, Canada, June 4-8, 2017
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2018-02-12Bibliographically approved
Thomas, I., Bergquist, F., Constantinescu, R., Nyholm, D., Senek, M. & Memedi, M. (2017). Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): . Paper presented at 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju Island, South Korea, July 11-15, 2017 (pp. 131-134). IEEE
Open this publication in new window or tab >>Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients
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2017 (English)In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2017, p. 131-134Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this study was to investigate the validity of an objective gait measure for assessment of different motor states of advanced Parkinson's disease (PD) patients. Seven PD patients performed a gait task up to 15 times while wearing sensors on their upper and lower limbs. Each task was performed at specific points during a test day, following a single dose of levodopa-carbidopa. At the time of the tasks the patients were video recorded and three movement disorder experts rated their motor function on three clinical scales: a treatment response scale (TRS) that ranged from −3 (very bradykinetic) to 0 (ON) to +3 (very dyskinetic), a dyskinesia score that ranged from 0 (no dyskinesia) to 4 (extreme dyskinesia), and a bradykinesia score that ranged from 0 (no bradykinesia) to 4 (extreme bradykinesia). Raw accelerometer and gyroscope data of the sensors were processed and analyzed with time series analysis methods to extract features. The utilized features quantified separate limb movements as well as movement symmetries between the limbs. The features were processed with principal component analysis and the components were used as predictors for separate support vector machine (SVM) models for each of the three scales. The performance of each model was evaluated in a leave-one-patient out setting where the observations of a single patient were used as the testing set and the observations of the other 6 patients as the training set. Root mean square error (RMSE) and correlation coefficients for the predictions showed a good ability of the models to map the sensor data into the rating scales. There were strong correlations between the SVM models and the mean ratings of TRS (0.79; RMSE=0.70), bradykinesia score (0.79; RMSE=0.47), and bradykinesia score (0.78; RMSE=0.46). The results presented in this paper indicate that the use of wearable sensors when performing gait tasks can generate measurements that have a good correlation to subjective expert assessments.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Engineering in Medicine and Biology Society. Conference Proceedings, ISSN 1557-170X, E-ISSN 1558-4615
Keywords
Physiological monitoring - Modeling and analysis, Wearable wireless sensors, motes and systems
National Category
Computer and Information Sciences
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-61053 (URN)10.1109/EMBC.2017.8036779 (DOI)000427085300032 ()29059827 (PubMedID)2-s2.0-85032218845 (Scopus ID)978-1-5090-2809-2 (ISBN)978-1-5090-2810-8 (ISBN)
Conference
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju Island, South Korea, July 11-15, 2017
Funder
Knowledge FoundationVINNOVA
Note

Funding Agencies:

Acreo (Sweden)  

Cenvigo (Sweden)  

Sensidose (Sweden)  

Uppsala University (Sweden)  

Dalarna University (Sweden) 

Available from: 2017-09-15 Created: 2017-09-15 Last updated: 2018-04-13Bibliographically approved
Aghanavesi, S., Memedi, M., Dougherty, M., Nyholm, D. & Westin, J. (2017). Verification of a Method for Measuring Parkinson’s Disease Related Temporal Irregularity in Spiral Drawings. Sensors, 17(10), Article ID 2341.
Open this publication in new window or tab >>Verification of a Method for Measuring Parkinson’s Disease Related Temporal Irregularity in Spiral Drawings
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Basel: MDPI AG, 2017
Keywords
Parkinson’s disease; smartphone; spiral tests; temporal irregularity; timing variability; motor assessment; approximate entropy; complexity
National Category
Computer and Information Sciences Analytical Chemistry
Research subject
Informatics
Identifiers
urn:nbn:se:oru:diva-61534 (URN)10.3390/s17102341 (DOI)000414931500183 ()29027941 (PubMedID)2-s2.0-85032855199 (Scopus ID)
Funder
Knowledge FoundationVINNOVA
Note

Funding Agencies:

Cenvigo AB  

Sensidose AB  

Swedish ICT Acreo  

Uppsala University  

Sahlgrenska University  

Dalarna University 

Available from: 2017-10-13 Created: 2017-10-13 Last updated: 2018-01-13Bibliographically approved
Memedi, M., Aghanavesi, S. & Westin, J. (2016). A method for measuring Parkinson's disease related temporal irregularity in spiral drawings. In: 2016 IEEE International Conference on Biomedical and Health Informatics: . Paper presented at 3rd IEEE EMBS International Conference on Biomedical and Health Informatics (IEEE BHI), Las Vegas, NV, USA, February 24-27, 2016 (pp. 410-413). New York: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
2016 (English)In: 2016 IEEE International Conference on Biomedical and Health Informatics, New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 410-413Conference paper, Published 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.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
IEEE Journal of Biomedical and Health Informatics, E-ISSN 2168-2208
National Category
Computer Systems Signal Processing
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
urn:nbn:se:oru:diva-61389 (URN)10.1109/BHI.2016.7455921 (DOI)000381398000102 ()2-s2.0-84968538061 (Scopus ID)978-1-5090-2455-1 (ISBN)
Conference
3rd IEEE EMBS International Conference on Biomedical and Health Informatics (IEEE BHI), Las Vegas, NV, USA, February 24-27, 2016
Available from: 2016-03-01 Created: 2017-10-10 Last updated: 2017-10-11Bibliographically approved
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