Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC V6H 3N1, Canada
Department of Computer Science & Engineering, University of Qatar, Doha 2713, Qatar
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 11 January 2018 / Accepted: 12 January 2018 / Published: 16 January 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 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.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Share & Cite This Article
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.
Elgendi M, Al-Ali A, Mohamed A, Ward R. Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach. Diagnostics. 2018; 8(1):10.
Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab. 2018. "Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach." Diagnostics 8, no. 1: 10.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
Multiple requests from the same IP address are counted as one view.