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Ahmed, Mobyen Uddin
Publications (10 of 10) Show all publications
Banaee, H., Ahmed, M. U. & Loutfi, A. (2015). Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data. In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015): . Paper presented at HEALTHINF 2015 : HEALTHINF 8th International Conference on Health Informatics, 12-15 january, Lisabon, Portugal. SciTePress
Open this publication in new window or tab >>Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data
2015 (English)In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015), SciTePress, 2015Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an approach to automatically mine rules in time series data representing physiologicalparameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined foreach physiological time series data. The generated rules based on the prototypical patterns are then describedin a textual representation which captures trends in each physiological parameter and their relation to the otherphysiological data. In this paper, a method for measuring similarity of rule sets is introduced in order tovalidate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classesin the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rulemining technique is able to acquire a distinctive model for each clinical condition, and represent the generatedrules in a human understandable textual representation

Place, publisher, year, edition, pages
SciTePress, 2015
Keywords
rule mining, pattern abstraction, health parameters, physiological time series, clinical condition.
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-39650 (URN)978-989-758-068-0 (ISBN)
Conference
HEALTHINF 2015 : HEALTHINF 8th International Conference on Health Informatics, 12-15 january, Lisabon, Portugal
Available from: 2014-12-14 Created: 2014-12-14 Last updated: 2018-01-11Bibliographically approved
Ahmed, M. U., Banaee, H., Rafael-Palou, X. & Loutfi, A. (2015). Intelligent Healthcare Services to Support Health Monitoring of Elderly. In: INTERNET OF THINGS: USER-CENTRIC IOT, PT I. Paper presented at 1st International Conference on IoT Technologies for HealthCare, HealthyIoT, October 27-29, Rome, Italy, 2014 (pp. 178-186). Springer, 150
Open this publication in new window or tab >>Intelligent Healthcare Services to Support Health Monitoring of Elderly
2015 (English)In: INTERNET OF THINGS: USER-CENTRIC IOT, PT I, Springer, 2015, Vol. 150, p. 178-186Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposed an approach of intelligent healthcare services to support health monitoring of old people through the project named SAAPHO. Here, definition and architecture of the proposed healthcare services are presented considering six different health parameters such as: 1) physical activity, 2) blood pressure, 3) glucose, 4) medication compliance, 5) pulse monitoring and 6) weight monitoring. The outcome of the proposed services is evaluated in a case study where total 201 subjects from Spain and Slovenia are involved for user requirements analysis considering 1) end users, 2) clinicians, and 3) field study analysis perspectives. The result shows the potentiality and competence of the proposed healthcare services for the users.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, ISSN 1867-8211 ; 150
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-36619 (URN)10.1007/978-3-319-19656-5_26 (DOI)000365176500026 ()978-3-319-19656-5 (ISBN)978-3-319-19655-8 (ISBN)
Conference
1st International Conference on IoT Technologies for HealthCare, HealthyIoT, October 27-29, Rome, Italy, 2014
Projects
SAAPHO
Available from: 2014-09-16 Created: 2014-09-16 Last updated: 2018-01-11Bibliographically approved
Begum, S., Barua, S., Filla, R. & Ahmed, M. U. (2014). Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning. Expert systems with applications, 41(2), 295-305
Open this publication in new window or tab >>Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
2014 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, no 2, p. 295-305Article in journal (Refereed) Published
Abstract [en]

Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis-Case-Based Reasoning (MMSE-CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE-CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify 'stressed' and 'healthy' subjects 83.33% correctly compare to an expert's classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions 'adapt' (training) and 'sharp' (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE-CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.

Keywords
Sensor fusion, Case-based reasoning, Multi-scale entropy analysis, Physiological signals, Classification
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-32908 (URN)10.1016/j.eswa.2013.05.068 (DOI)000327279900007 ()2-s2.0-84885957580 (Scopus ID)
Funder
Knowledge Foundation
Note

Funding Agency: Volvo Construction Equipment AB, Sweden (se även Forskningsfinansiärer)

Available from: 2014-01-03 Created: 2014-01-03 Last updated: 2023-12-08Bibliographically approved
Banaee, H., Ahmed, M. U. & Loutfi, A. (2013). A framework for automatic text generation of trends in physiological time series data. In: IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester: . Paper presented at IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester (pp. 3876-3881). IEEE conference proceedings
Open this publication in new window or tab >>A framework for automatic text generation of trends in physiological time series data
2013 (English)In: IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester, IEEE conference proceedings, 2013, p. 3876-3881Conference paper, Published paper (Refereed)
Abstract [en]

Health monitoring systems using wearable sensorshave rapidly grown in the biomedical community. The mainchallenges in physiological data monitoring are to analyse largevolumes of health measurements and to represent the acquiredinformation. Natural language generation is an effective methodto create summaries for both clinicians and patients as it candescribe useful information extracted from sensor data in textualformat. This paper presents a framework of a natural languagegeneration system that provides a text-based representation ofthe extracted numeric information from physiological sensorsignals. More specifically, a new partial trend detection algorithmis introduced to capture the particular changes and events ofhealth parameters. The extracted information is then representedconsidering linguistic characterisation of numeric features. Ex-perimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
Keywords
Health monitoring, physiological data analysis, body area networks, natural language generation, linguistic summarisation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-32260 (URN)10.1109/SMC.2013.661 (DOI)000332201904002 ()2-s2.0-84890455979 (Scopus ID)978-1-4799-0652-9 (ISBN)
Conference
IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester
Available from: 2013-11-06 Created: 2013-11-06 Last updated: 2018-01-11Bibliographically approved
Banaee, H., Ahmed, M. U. & Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors, 13(12), 17472-17500
Open this publication in new window or tab >>Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges
2013 (English)In: Sensors, E-ISSN 1424-8220, Vol. 13, no 12, p. 17472-17500Article, review/survey (Refereed) Published
Abstract [en]

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems

Place, publisher, year, edition, pages
Basel: MDPI, 2013
Keywords
data mining, wearable sensors, healthcare, physiological sensors, health monitoring system, machine learning technique, vital signs, medical informatics
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-32866 (URN)10.3390/s131217472 (DOI)000330220600086 ()2-s2.0-84890453428 (Scopus ID)
Funder
Vinnova
Note

Funding Agencies:

SAAPHO (Secure Active Aging: Participation and Health for the Old)

Available from: 2013-12-22 Created: 2013-12-22 Last updated: 2024-05-14Bibliographically approved
Ahmed, M. U., Banaee, H. & Loutfi, A. (2013). Health monitoring for elderly: an application using case-based reasoning and cluster analysis. ISRN Artificial Intelligence, 2013(2013), 1-11
Open this publication in new window or tab >>Health monitoring for elderly: an application using case-based reasoning and cluster analysis
2013 (English)In: ISRN Artificial Intelligence, ISSN 2090-7435, E-ISSN 2090-7443, Vol. 2013, no 2013, p. 1-11Article in journal (Refereed) Published
Abstract [en]

This paper presents a framework to process and analyze data from a pulse oximeter which measures pulse rate and blood oxygen saturation from a set of individuals remotely. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to how well they are similar. Record collection has been performed using a personalized health profiling approach where participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction in time, frequency and time-frequency domains, and data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates alarm and flag according to the case outcomes. The system has been compared with an expert's classification and 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in analysis of continuous health monitoring and be used as a suitable method as in home/remote monitoring systems.

Keywords
Health Monitoring, Elderly, Case-Based Reasoning, Cluster Analysis
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-28738 (URN)10.1155/2013/380239 (DOI)
Projects
SAAPHOREMOTE
Funder
EU, FP7, Seventh Framework Programme
Available from: 2013-04-20 Created: 2013-04-20 Last updated: 2022-12-14Bibliographically approved
Ahmed, M. U. & Loutfi, A. (2013). Physical Activity Classification for Elderly based on Pulse Rate. In: : . Paper presented at 10th International Conference on Wearable Micro and Nano Technologies for Personalized Health Tallinn, June 26 - 28 (pp. 152-157). IOS Press
Open this publication in new window or tab >>Physical Activity Classification for Elderly based on Pulse Rate
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Physical activity is one of the key components for elderly in order to be actively ageing. However, it is difficult to differentiate and identify the body movement and actual physical activity using only accelerometer measurement. Therefore, this paper presents an application of case-based retrieval classification scheme to classify the physical activity of elderly based on pulse rate measurements. Here, case-based retrieval approach used the features extracted from both time and frequency domain. The evaluation result shows the best accuracy performance while considering the combination of time and frequency domain features. According to the evaluation result while considering the control measurements, the sensitivity, specificity and overall accuracy are achieved as 95%, 96% and 96% respectively. Considering the test dataset, the system was succeeded to identify 13 physical activities out of 16 i.e. the percentage of the correctness was 81%.

Place, publisher, year, edition, pages
IOS Press, 2013
Keywords
Pulse rate, Case-Based Reasoning (CBR), classification, physical activity recognition
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-28477 (URN)23739375 (PubMedID)
Conference
10th International Conference on Wearable Micro and Nano Technologies for Personalized Health Tallinn, June 26 - 28
Projects
RemoteSAAPHO
Available from: 2013-03-26 Created: 2013-03-26 Last updated: 2019-04-03Bibliographically approved
Ahmed, M. U. & Loutfi, A. (2013). Physical activity identification using supervised machine learning and based on pulse rate. International Journal of Advanced Computer Sciences and Applications, 4(7), 210-217
Open this publication in new window or tab >>Physical activity identification using supervised machine learning and based on pulse rate
2013 (English)In: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 4, no 7, p. 210-217Article in journal (Refereed) Published
Abstract [en]

Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a Case-Based Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate

Place, publisher, year, edition, pages
The Science and Information (SAI) Organization, 2013
Keywords
Physical activity, Elderly, Pulse rate, Case-based Reasoning (CBR), Support Vector Machine (SVM) and Neural Network (NN)
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-30066 (URN)
Projects
SAAPHO, Remote
Available from: 2013-07-29 Created: 2013-07-29 Last updated: 2023-05-11Bibliographically approved
Banaee, H., Ahmed, M. U. & Loutfi, A. (2013). Towards NLG for Physiological Data Monitoring with Body Area Networks. In: 14th European Workshop on Natural Language Generation: . Paper presented at 14th European Workshop on Natural Language Generation, Sofia, Bulgaria, August 8-9, 2013 (pp. 193-197).
Open this publication in new window or tab >>Towards NLG for Physiological Data Monitoring with Body Area Networks
2013 (English)In: 14th European Workshop on Natural Language Generation, 2013, p. 193-197Conference paper, Published paper (Refereed)
Abstract [en]

This position paper presents an on-goingwork on a natural language generationframework that is particularly tailored fornatural language generation from bodyarea networks. We present an overview ofthe main challenges when considering thistype of sensor devices used for at homemonitoring of health parameters. The paperpresents the first steps towards the implementationof a system which collectsinformation from heart rate and respirationusing a wearable sensor.

Keywords
NLG, Physiological Data, Body Area Networks
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-30257 (URN)
Conference
14th European Workshop on Natural Language Generation, Sofia, Bulgaria, August 8-9, 2013
Projects
SAAPHORemote
Available from: 2013-08-19 Created: 2013-08-19 Last updated: 2018-03-09Bibliographically approved
Ahmed, M. U., Islam, A. M. & Loutfi, A. (2012). A case-based patient identification system using pulseoximeter and a personalized health profile. In: : . Paper presented at Workshop on CBR in the Health Sciences at 20th International Conference on Case-Based Reasoning (ICCBR12).
Open this publication in new window or tab >>A case-based patient identification system using pulseoximeter and a personalized health profile
2012 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

This paper proposes a case-based system framework in order to identify patient using their health parameters taken with physiological sensors. It combines a personalized health profiling protocol with a Case-Based Reasoning (CBR) approach. The personalized health profiling helps to determine a number of individual parameters which are important inputs for a clinician to make the final diagnosis and treatment plan. The proposed system uses a pulse oximeter that measures pulse rate and blood oxygen saturation. The measurements are taken through an android application in a smart phone which is connected with the pulseoximeter and bluetooth communication. The CBR approach helps clinicians to make a diagnosis, classification and treatment plan by retrieving the most similar previous case. The case may also be used to follow the treatment progress. Here, the cases are formulated with person’s contextual information and extracted features from sensor signal measurements. The features are extracted considering three domain analysis:1) time domain features using statistical measurement, 2) frequency domain features applying Fast Fourier Transform (FFT), and 3) time-frequency domain features applying Discrete Wavelet Transform (DWT). The initial result is acceptable that shows the advancement of the system while combining the personalized health profiling together with CBR.

National Category
Signal Processing Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-24086 (URN)
Conference
Workshop on CBR in the Health Sciences at 20th International Conference on Case-Based Reasoning (ICCBR12)
Projects
Remote
Available from: 2012-08-24 Created: 2012-07-12 Last updated: 2023-05-11Bibliographically approved
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