An Image Processing Approach for Compression of ECG Signals Based on 2D RLE and SPIHT

This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).