Why the study?
Continuous heart monitoring generates large amounts of ECG data, creating a need for suitable compression techniques to minimize bandwidth and memory requirements.
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A novel ECG compression technique using tunable-Q wavelet transform and optimized thresholding achieves efficient data compression while maintaining signal quality.
The electrocardiogram (ECG) is a salient signal that is commonly utilized to diagnose heart patients. The recording of ECG signals generates a large amount of data when continuous monitoring of the heart is necessary. Hence, there is a strong motivation to develop a suitable compression technique to minimize bandwidth and memory requirements. In this context, this work proposes a compression technique using tunable-Q wavelet transform (TQWT) and an optimized dead-zone quantizer (ODZQ). The TQWT is used for the decomposition of ECG signal and DZQ for thresholding and quantization. The swarm-based method, particle swarm optimization (PSO) is used to obtain the optimized threshold values. The compressed signal is obtained by thresholding, quantization, and encoding of quantized coefficients. Encoding is performed by utilizing run-length encoding (RLE), which helps to achieve further compression. The proposed method is assessed using percentage-root-mean square difference (PRD), compression ratio (CR), and quality score (QS). The obtained results from the proposed method are CR=17.2553, PRD=2.9360, and QS=6.4354.
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Pal et al. (2021) studied this question.
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