ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds
Computers in Biology and Medicine
Subband-adaptive shrinkage for denoising of ECG signals
EURASIP Journal on Applied Signal Processing
Optimal selection of wavelet basis function applied to ECG signal denoising
Digital Signal Processing
Parametrization of compactly supported orthonormal wavelets
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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This paper presents an ECG compression algorithm based on the optimal selection of wavelet filters and threshold levels in different subbands that achieve maximum data volume reduction while guaranteeing reconstruction quality. The proposed algorithm starts by segmenting the ECG signal into frames; where each frame is decomposed into m subbands through optimized wavelet filters. The resulting wavelet coefficients are thresholded and those having absolute values below specified threshold levels in all subbands are deleted and the remaining coefficients are appropriately encoded with a modified version of the run-length coding scheme. The threshold levels to use, before encoding, are adjusted in an optimum manner, until predefined compression ratio and signal quality are achieved. Extensive experimental tests were made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database. The compression ratio (CR), the root-mean-square difference (PRD) and the zero-mean percent root-mean-square difference (PRD"1) measures are used for measuring the algorithm performance (high CR with excellent reconstruction quality). From the obtained results, it can be deduced that the performance of the optimized signal dependent wavelet outperforms that of Daubechies and Coiflet standard wavelets. However, the computational complexity of the proposed technique is the price paid for the improvement in the compression performance measures.