ECG signals compression using dynamic compressive sensing technique toward IoT applications

This paper provides a novel compressive sensing (CS) technique to compress electrocardiogram (ECG) signals. The proposed technique extends a dynamic compressed sensing method, originally based on a single lead signal, to multiple lead signals. The sensing matrix used by the dynamic sensing technique receives its components dynamically from the compressed signal. In this way, a single sensing matrix is recommended to be used for an application of many leads, for which its components are obtained from the fusion of many leads. The CS technique is tested using a variety of signals that are collected from both healthy people and those with various diseases, including bundle branch block, cardiomyopathy, and myocardial infarction. The experiment's results show that the suggested CS technique can be applied to achieve a compression ratio (CR) of 16 without affecting the metrics of the signal.