Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach
1
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC V6H 3N1, Canada
3
Department of Computer Science & Engineering, University of Qatar, Doha 2713, Qatar
*
Author to whom correspondence should be addressed.
Diagnostics 2018, 8(1), 10; https://doi.org/10.3390/diagnostics8010010
Received: 30 November 2017 / Revised: 11 January 2018 / Accepted: 12 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Novel Point-of-Care Technologies in Diagnostics 2018)
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 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 ( and ) 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.
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Keywords:
wearable sensors; telemedicine; digital medicine; smart healthcare; wireless systems; remote healthcare; mobile health; e-Health
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MDPI and ACS Style
Elgendi, M.; Al-Ali, A.; Mohamed, A.; Ward, R. Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach. Diagnostics 2018, 8, 10.
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