Why the study?
Numerous ECG compression and reconstruction methods have been developed to balance compression ratio and signal quality for telehealth care and monitoring, requiring evaluation across various performance metrics.
This systematic review highlights the trade-off between compression ratio and signal quality in ECG data compression algorithms for telehealth applications.
In present era, technological advancements make it possible to develop better telehealth care and monitoring system for those patients who are at remote locations. It became imaginable because of highly efficient biomedical signal (like ECG) data compression and reconstruction algorithms. In past two decades many researchers develop and implement numerous ECG compression and reconstruction methods. Data compression is done to save storage space, speed up data transfers, and conserve network bandwidth while maintaining acceptable signal quality. These methods were evaluated and compared for performance metrics like percentage root mean square difference (PRD), user defined percentage root mean square difference (UPRD), percentage root mean square difference normalized (PRDN), percentage root mean square difference without base (PRDB), quality score (QS), signal to noise ratio (SNR), root mean square error (RMSE), compression ratio (CR) and many more. High CR results in more savings but degrades the quality of the compressed signal. High PRD will reduce the CR for high-quality signals. As a result, CR and PRD must be balanced.
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Aggarwal et al. (2023) studied this question.
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