Implementation of compressive sensing in ECG and EEG signal processing

Abstract The purpose of this paper is to exploit compressive sensing (CS) method in dealing with electrocardiography (ECG) and electroencephalography (EEG) signals at a high compression ratio. In order to get sparse data of ECG and EEG signals before being compressed, a combined scheme was presented by using wavelet transform and iterative threshold method; then, compressive sensing is executed to make the data compressed. After doing compressive sensing, Bayesian compressive sensing (BCS) is used to reconstruct the original signals. The simulation results show that compressive sensing is an effective method to make data compressed for ECG and EEG signals with high compression ratio and good quality of reconstruction. Furthermore, it shows that the proposed scheme has good denoising effects.