Key result
The proposed ECG signal compression algorithm using Haar wavelet and dual encoding achieved a compression ratio of 20.6 with a percent root mean square difference of 1% for record 100.
Why the study?
In medical practices, storing ECG records containing hours of data requires substantial storage space, a problem commonly addressed by ECG signal compression.
The proposed dual encoding technique with discrete wavelet transform efficiently compresses ECG signals while maintaining high reconstruction quality, which can significantly reduce storage space requirements.
DWT ECG compression may reduce storage demands; leaves open clinical validation before practice adoption.
In medical practices, the storage space of electrocardiogram (ECG) records is a major concern. These records can contain hours of recording, necessitating a large amount of storage space. This problem is commonly addressed by compressing the ECG signal. The proposed work deal with the ECG signal compression method for ECG signals using discrete wavelet transform (DWT). The DWT appeared as powerful tools to compact signals and shows a signal in another time-frequency representation. It is very appropriate in the elimination & removal of redundancy. The ECG signals are decomposed using DWT. After that, the coefficients that result from DWT are threshold depending on the energy packing efficiency (EPE) of the signal. The compression is achieved by the quantization and dual encoding techniques (run-length encoding & Huffman encoding). The dual encoding technique compresses data significantly. The result of the proposed method shows better performance with compression ratios and good quality reconstructed signals. For example, the compression ratio (CR)=20.6, 10.7 and 11.1 with percent root mean square difference (PRD)=1%, 0.9% and 1% for using different DWT (Haar, db2 and FK4) Respectively.
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Rijab et al. (2022) studied Electrocardiogram (ECG) signal compression (n=5). Discrete wavelet transform with dual encoding (Huffman and run-length encoding) vs. Other wavelet-based compression methods was evaluated on Compression Ratio (CR) and Percent Root Mean Square Difference (PRD). The proposed ECG signal compression algorithm using Haar wavelet and dual encoding achieved a compression ratio of 20.6 with a percent root mean square difference of 1% for record 100.
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