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 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.
Highlights
The World Health Organization (WHO) cites Cardiovascular Diseases (CVDs) as the number one cause of death worldwide [1]
The MIT-BIH arrhythmia database is used for evaluating all compression algorithms
We discuss in this paper the lossy compression Method III, which is suitable for remote health monitoring systems
Summary
The World Health Organization (WHO) cites Cardiovascular Diseases (CVDs) as the number one cause of death worldwide [1]. An intense focus has been placed on studying the ways in which conventional cardiovascular diagnosis technologies can be improved for use in hospitals, clinics, and other related health care facilities. Each ECG heartbeat signal contains three prominent waves: the P wave, the QRS complex, and the T wave. The detection of these waves over a short period of time (
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