Key result
Optimised Kaiser-Blackman wavelet filter banks outperform existing filters for ECG compression and fidelity.
Why the study?
An optimised wavelet filter bank methodology for ECG signal compression was developed to improve compression ratio and signal fidelity compared to existing wavelet filters.
An optimized wavelet filter bank methodology improves ECG signal compression ratio and fidelity compared to existing filters.
Optimised wavelet filter bank may enhance ECG compression; extends prior techniques but leaves open clinical validation.
In this paper, an optimised wavelet filter bank based methodology is presented for compression of Electrocardiogram (ECG) signal. The methodology employs new wavelet filter bank whose coefficients are derived with different window techniques such as Kaiser and Blackman windows using simple linear optimisation. A comparative study of performance of different existing wavelet filters and the proposed wavelet filter is made in terms of Compression Ratio (CR), Percent Root mean square Difference (PRD), Mean Square Error (MSE) and Signal-to-Noise Ratio (SNR). When compared, the developed wavelet filter gives better CR and also yields good fidelity parameters as compared to other wavelet filters. The simulation result included in this paper shows the clearly increased efficacy and performance in the field of biomedical signal processing.
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Kumar et al. (2012) studied Electrocardiogram (ECG) signal compression. Optimised wavelet filter bank methodology vs. Existing wavelet filters was evaluated on Compression Ratio (CR), Percent Root mean square Difference (PRD), Mean Square Error (MSE) and Signal-to-Noise Ratio (SNR). An optimised wavelet filter bank using Kaiser and Blackman windows provided better compression ratio and fidelity parameters compared to existing wavelet filters for ECG signal compression.
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