Key result
Wavelet transform algorithm achieves ~45-fold ECG compression with minimal signal distortion.
The proposed wavelet transform-based coding technique effectively compresses ECG signals while maintaining signal quality.
May facilitate compact ECG storage and transmission; leaves open prospective validation in clinical workflows.
This paper introduces an effective technique for the compression of one-dimensional signals using wavelet transforms. It is based on generating a binary stream of 1s and 0s that encodes the wavelet coefficients structure (i.e., encodes the locations of zero and nonzero coefficients). A new coding algorithm, similar to the run length encoding, has been developed for the compression of the binary stream. The compression performances of the technique are measured using compression ratio (CR) and percent root-mean square difference (PRD) measures. To assess the technique properly we have evaluated the effect of signal length, threshold levels selection and wavelet filters on the quality of the reconstructed signal. The effect of finite word length representation on the compression ratio and PRD is also discussed. The technique is tested for the compression of normal and abnormal electrocardiogram (ECG) signals. The performance parameters of the proposed coding algorithm are measured and compression ratios of 19:1 and 45:1 with PRDs of 1% and 2.8% are achieved, respectively. At the receiver end, the received signal is decoded and inverse transformed before being processed. Finally, the merits and demerits of the technique are discussed.
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Abo‐Zahhad et al. (2002) studied Normal and abnormal electrocardiogram (ECG) signals. Coding technique for compression using wavelet transforms was evaluated on Compression ratio (CR) and percent root-mean square difference (PRD). The proposed coding algorithm using wavelet transforms achieved compression ratios of 19:1 and 45:1 with percent root-mean square differences of 1% and 2.8%, respectively, for ECG signals.
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