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Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab Kreidieh

Abstract

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

Item Metadata

Title
Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach
Creator
Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab Kreidieh
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2018-01-16
Description
Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.
Subject
Wearable sensors; Telemedicine; Digital medicine; Smart healthcare; Wireless systems; Remote healthcare; Mobile health; E-Health
Genre
Article
Type
Text
Language
eng
Date Available
2019-06-27
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0379652
URI
http://hdl.handle.net/2429/70834
Affiliation
Applied Science, Faculty of; Medicine, Faculty of; Non UBC; Electrical and Computer Engineering, Department of; Obstetrics and Gynaecology, Department of
Citation
Diagnostics 8 (1): 10 (2018)
Publisher DOI
10.3390/diagnostics8010010
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0