Key result
The proposed wavelet transform-based ECG compression algorithm achieved a PRD of 1.65% at a compression ratio of 10.43 for record 117, outperforming other wavelet-based methods.
The proposed wavelet-based ECG compression algorithm achieves high compression ratios while maintaining good signal reconstruction quality, which is essential for efficient storage and transmission of ECG data.
May support efficient ECG signal handling in monitoring; leaves open clinical validation before adoption.
ECG data compression has been one of the active research areas in biomedical engineering. In this paper a compression method for electrocardiogram (ECG) signals using wavelet transform is proposed. Wavelet transform compact the energy of signal in fewer samples and has a good localization property in time and frequency domain. The MIT-BIH ECG signals are decomposed using discrete wavelet transform (DWT).The DWT provide powerful capability to remove frequency components at specific time in the data. The thresholding of the resulted DWT coefficients are done in a manner such that a predefined goal percent root mean square difference (GPRD) is achieved. The compression is achieved by the quantization technique, run-length encoding, Huffman and binary encoding methods. The proposed method, for fixed GPRD shows better performance with high compression ratios and good quality reconstructed signals. Keywords—Compression, discrete wavelet transform Electrocardiogram (ECG), PRD, quantization, thresholding.
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Patel et al. (2014) studied ECG Data Compression. Wavelet transform-based compression algorithm vs. Other compression algorithms was evaluated on Compression Ratio (CR) and Percent Root Mean Square Difference (PRD). The proposed wavelet transform-based ECG compression algorithm achieved a PRD of 1.65% at a compression ratio of 10.43 for record 117, outperforming other wavelet-based methods.
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