A novel ECG signal compression using wavelet and discrete anamorphic stretch transforms

In this paper, a one-dimensional complex Discrete Anamorphic Stretch Transform (DAST) is proposed for pre compression of the ECG signal. Three 1D kernels (Linear, Sublinear, and Superlinear) and a two-tap phase recovery filter are proposed for efficient computation of 1D DAST and inverse DAST. Wavelet transform and Run Length Encoding (RLE) are used as secondary compression schemes. To evaluate the efficacy of this approach, the bandwidth compression (BWC) obtained using the three kernels, and the Percentage root mean square difference (PRD) between the original ECG and reconstructed signal using inverse DAST are computed for the ECG records in the MIT-BIH database. From this, it is found that the BWC factor varies from (1.37-10.6) and it is dependent on the ECG record and the type of kernel and kernel size. The performance of the ECG compression using DAST, Wavelet Transform, and RLE is evaluated next using all the MIT-BIH ECG records for different kernels, kernel sizes, different levels of Wavelet decomposition and various Energy Packing Efficiency. From this study, it is found that the maximum factor by which the average Compression Ratio increases is 3.25, and this is obtained for DAST with Sublinear kernel and 3 level DWT for EPE of 99.9%. The proposed compression scheme combining both DAST and DWT is also found to have a better Compression Ratio and PRD compared to those reported in the literature. DAST is well suited for the precompression of the ECG signal for real-time transmission using channels with limited bandwidth.