Application of Compressed Sensing (CS) for ECG Signal Compression: A Review

Compressed Sensing (CS) is a fast growing signal processing technique that compresses the signal while sensing and enables exact reconstruction of the signal if the signal is sparse with a few numbers of measurements only. This scheme results in reduction of storage requirement and low power consumption of system compared to Nyquist sampling theorem, where the sampling frequency must be at least double the maximum frequency present in the signal for the exact reconstruction of the signal. This paper presents an in-depth study on recent trends in CS focused on ECG compression. Compression Ratio (CR), % Root-mean-squared Difference (% PRD), Signal-to-Noise Ratio (SNR), Root-Mean Square Error (RMSE), Sparsity and power consumption are used as the performance evaluation parameters. Finally, we have presented the conclusions based on the literature review and discussed the major challenges in CS ECG implementation.

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