A Study on ECG Signal Compression Techniques Using Discrete Wavelet
Keywords:
ECG signal compression, Discrete Wavelet Transform (DWT), Compression Ratio (CR), Percentage Root Mean Square Difference (PRD), Telemedicine.Abstract
Electrocardiogram (ECG) signal compression has become an essential area of research due to the exponential growth of digital healthcare, wearable monitoring devices, and telemedicine applications that generate and transmit large volumes of ECG data. Efficient compression techniques are required to reduce storage and transmission demands while preserving the diagnostic quality of clinically significant features such as the P wave, QRS complex, and T wave. Among various approaches, the Discrete Wavelet Transform (DWT) has emerged as one of the most effective methods, owing to its capability for multi-resolution analysis, energy compaction, and superior time-frequency localization. Numerous DWT-based techniques have been developed, including wavelet thresholding, embedded zerotree coding, SPIHT, and hybrid entropy-based methods. This survey provides a comprehensive overview of these techniques, focusing on their principles, performance metrics such as Compression Ratio (CR), Percentage Root Mean Square Difference (PRD), and Signal-to-Noise Ratio (SNR), while also highlighting challenges, clinical relevance, and potential future research directions.
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