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AuthorAboumadi, Abdulla
AuthorYaacoub, Elias
AuthorAbualsaud, Khalid
Available date2024-03-26T11:56:48Z
Publication Date2021
Publication NameProceedings - IEEE Congress on Cybermatics: 2021 IEEE International Conferences on Internet of Things, iThings 2021, IEEE Green Computing and Communications, GreenCom 2021, IEEE Cyber, Physical and Social Computing, CPSCom 2021 and IEEE Smart Data, SmartData 2021
ResourceScopus
URIhttp://dx.doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00021
URIhttp://hdl.handle.net/10576/53533
AbstractWith the development of Internet-of-Things (IoT) applications, the concept of smart healthcare applications has gradually emerged to be the main factor in medicine. In fact, this raises the need to have a secure system that is efficient at the same time, due to the limited resources of IoT devices. Many different techniques have been developed and studied recently. For example, with centralized learning (CL), all data are collected and processed in one place. But many of these models are heavy and do not meet IoT's resource constraints. Therefore, in this paper, the concept of CL using a convolutional neural network is performed to identify and classify arrhythmia, while taking into consideration the accuracy and simplicity in simulating a system model that would be used in medical devices. The MIT-BIH dataset was used in this work to test and validate the proposed approach, and compare it to other methods in the literature.
SponsorThis work was made possible by NPRP grant # 13S-0205-200270 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectarrhythmia detection
centralized Learning
Internet of Things
smart healthcare
TitleA Lightweight Central Learning Approach for Arrhythmia Detection from ECG Signals
TypeConference Paper
Pagination37-42
dc.accessType Abstract Only


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