Comparative Analysis of Compressive Sensing Reconstruction Algorithms for ECG Signals

In the era of the Internet of Medical Things (IoMT), modern healthcare devices generate vast amounts of data, necessitating enhanced data processing, storage, transmission bandwidth, and increased power consumption, especially in sensing applications. Compressive Sensing (CS) addresses these challenges by enabling signal acquisition with fewer samples than the traditional Nyquist rate, thereby conserving power. This is particularly beneficial for mobile healthcare applications, such as Electrocardiogram (ECG) monitoring, which require continuous monitoring and substantial power and bandwidth for signal transmission and reconstruction. Despite various CS strategies and reconstruction algorithms explored for ECG signals, achieving high accuracy with a high compression ratio remains a challenge. This research analyzes six sensing strategies two sparse bases nine reconstruction algorithms, to identify the most efficient method for ECG signal processing within the CS framework. From our analysis it shows that RL1 with db2 sparse basis shows the averagely superior performance with RGM, RBMB and RBMS and RLDPC sensing matrices and CVX-L1 shows the good parformance with SFM sensing matrix.