Selecting the Most Favorable Wavelet for Compressing ECG Signals Using Compressive Sensing Approach

Abstract

Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed ECG signal based on different wavelet families, by evaluating the performance measures as MSE (Mean Square Error), PSNR (Peak Signal To Noise Ratio), PRD (Percentage Root Mean Square Difference) and CoC (Correlation Coefficient). Reconstruction of the ECG signal is a linear optimization process which consider the sparsity in the wavelet domain, perceived by the fact that higher the sparsity, more better the recovery. L1 minimization is used as the recovery algorithm. The reconstruction results are comprehensively analyzed for five compression ratios, i.e. 2:1, 4:1, 6:1, 8:1 and 10:1. The results indicate that reverse biorthogonal wavelet family can give better results for all (Compression Ratio's)CRs compared to other families.