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This paper presents and analyzes nonlinear transform-based method electrocardiogram (ECG) compression. The procedure used is similar to that used in linear transform-based method. The ECG signal is first transformed using (i) linear transform: discrete cosine transforms (DCT), Laplacian pyramid (LP), wavelet transform (WT) and it is transformed using (ii) nonlinear transform: essentially nonoscillatory cell average (ENOCA). The transformed coefficients (TC) are thresholded using the bisection algorithm in order to match the predefined user-specified percentage root mean square difference (PRD) within the tolerance. Then, the binary lookup table is made to store the position map for zero and nonzero coefficients (NZCs). The NZCs are quantized by Max–Lloyd quantizer followed by arithmetic coding. Lookup table is encoded by Huffman coding. The results are presented on different ECG signals of varying characteristics. The results show that nonlinear transform (ENOCA) gives better performance at high PRD where as at low PRD, DCT performs better.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.