IoT Based Compressive Sensing for ECG Monitoring
Author | Djelouat H. |
Author | Baali H. |
Author | Amira A. |
Author | Bensaali F. |
Available date | 2020-02-05T08:53:35Z |
Publication Date | 2018 |
Publication Name | Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 |
Publication Name | Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 |
Resource | Scopus |
ISBN | 9.78E+12 |
Abstract | The Internet of Things (IoT) has empowered several sets of applications related to remote monitoring for patients with chronic cardiovascular diseases, where, electrocardiogram (ECG) monitoring has been widely studied and applied. Furthermore, in order to optimize the energy consumption in these monitoring systems, compression techniques have been widely deployed. Compressive sensing (CS) has gained a lot of attention in ECG monitoring as a result of its ability to leverage the ECG signal structure in order to achieve a high efficient acquisition scheme. The paper investigates the incorporation of CS in IoT-based ECG monitoring platforms. The platform consists of a CS-based compression and recovery, in addition, the platform provides an abnormality detection for each heart beat using different pattern recognition algorithms. The obtained results reveal that transmitting only 15 % of the samples is enough to recover the signal efficiently. Moreover, using up to 20% of the total sample can achieve a high classification accuracy as using the original data with a maximum drop down of 3.3 % in the worst case scenario. 2017 IEEE. |
Sponsor | This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Classification compressive sensing (CS) connected health ECG Internet of things(IoT) |
Type | Conference Paper |
Pagination | 183-189 |
Volume Number | 2018-January |
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