Design of an Adaptive ECG Signal Processing System Based on Compressed Sensing

With the rapid development of modern mobile communication technologies, the wireless body sensor network (WBSN) becomes more and more important in medical treatment, especially for non-hospital patients. In general, the data amount transmitted in the WBSN system is large. Hence, developing low- complexity signal processing methods is important. In this paper, we investigate the electrocardiogram (ECG) signal processing based on the compressed sensing (CS) technique. The performances of four typical recovery algorithms in CS, namely, basis pursuit algorithm, orthogonal matching pursuit algorithm, compressive sampling MP algorithm, and block sparse Bayesian learning algorithm, are evaluated by simulation. Based on the evaluation results, we design an adaptive CS-based ECG signal processing system, which can achieve satisfactory performances while adaptively adjusting the data amount transited according to the channel state.