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Diagnostics (Basel). 2018 Jan 16;8(1). pii: E10. doi: 10.3390/diagnostics8010010.

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

Author information

1
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. moe.elgendi@gmail.com.
2
Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC V6H 3N1, Canada. moe.elgendi@gmail.com.
3
Department of Computer Science & Engineering, University of Qatar, Doha 2713, Qatar. rababw@ece.ubc.ca.
4
Department of Computer Science & Engineering, University of Qatar, Doha 2713, Qatar. amrm@qu.edu.qa.
5
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. abdulla.alali@qu.edu.qa.

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.

KEYWORDS:

digital medicine; e-Health; mobile health; remote healthcare; smart healthcare; telemedicine; wearable sensors; wireless systems

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