Compressive Sensing Algorithm based on DCT and DWT for ECG Signal Reconstruction using VariousWavelet Kernels.*

Compressive Sensing (CS) is a form of lossy compression that enables signal reconstruction using a reduced number of samples. This technique for ECG signal reconstruction offers the advantage of improved reconstruction quality while minimizing noise in the recovered signal. This study presents a comprehensive study of CS for ECG signal reconstruction using two well known fast transform, Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). Two different cases are examined: case (1) DCT as the sensing operator and DWT as the sparsifying operator and case (2) DWT as the sensing operator and DCT as the sparsifying operator, for both the cases 5 different various wavelet kernel such as Haar, Daubechies 3 (db3), Symlet 6 (Sym6), Coiflet 5 (coif5) and Biorthogonal (bior) wavelets are utilized. A detailed study was carried out to determine the most preferable wavelet kernel using 5 different performance measures, Root Mean Square Error (RMSE), Percent Root Mean Square Difference (PRD), Signal-to-Noise Ratio (SNR), Structural Similarity Index Measure (SSIM) and the correlation coefficient (r). Based on the findings, the bior wavelet demonstrated superior performance compared to all other wavelets, achieving a lower PRD value of 0.087373 and a higher SNR value of 55.873135 dB.