Q
Quazi Abidur Rahman
Researcher at University of Western Ontario
Publications - 19
Citations - 196
Quazi Abidur Rahman is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Software requirements specification & Deep learning. The author has an hindex of 5, co-authored 18 publications receiving 146 citations. Previous affiliations of Quazi Abidur Rahman include Queen's University & King Fahd University of Petroleum and Minerals.
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Journal ArticleDOI
Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification
Quazi Abidur Rahman,Larisa G. Tereshchenko,Matthew Kongkatong,Theodore P. Abraham,M. Roselle Abraham,Hagit Shatkay +5 more
TL;DR: A cardiovascular-patient classifier to identify HCM patients using standard 10-second, 12-lead ECG signals, and results show that a relatively small subset of 264 highly informative features can achieve performance measures comparable to those achieved by using the complete set of features.
Journal ArticleDOI
Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation.
Quazi Abidur Rahman,Tahir Janmohamed,Meysam Pirbaglou,Paul Ritvo,Jane M. Heffernan,Hance Clarke,Joel Katz,Joel Katz +7 more
TL;DR: Although most users of the Manage My Pain app reported being female, male users were more likely to be highly engaged in the app, and use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-Engaged females, and to those with relatively more complex chronic pain problems.
Journal ArticleDOI
Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods.
Quazi Abidur Rahman,Tahir Janmohamed,Meysam Pirbaglou,Hance Clarke,Paul Ritvo,Jane M. Heffernan,Joel Katz,Joel Katz +7 more
TL;DR: This study uses data mining and machine learning methods to define a new measure of pain volatility and predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features.
Proceedings ArticleDOI
Evaluating the Possibility of Integrating Augmented Reality and Internet of Things Technologies to Help Patients with Alzheimer’s Disease
TL;DR: Preliminary results on an Ambient Assisted Living (AAL) real-time system, achieved through Internet of Things (IoT) and Augmented Reality (AR) concepts, to fulfil this goal are reported.
Proceedings ArticleDOI
Identifying hypertrophic cardiomyopathy patients by classifying individual heartbeats from 12-lead ECG signals
Quazi Abidur Rahman,Larisa G. Tereshchenko,Matthew Kongkatong,Theodore P. Abraham,M. Roselle Abraham,Hagit Shatkay +5 more
TL;DR: A cardiovascular-patient classifier to identify HCM patients using standard 10-seconds, 12-lead ECG signals, which shows that a relatively small subset of 304 highly informative features can achieve performance measures comparable to that achieved by using the complete set of features.