ECG Signal Compression using Optimum Wavelet Filter Bank Based on Kaiser Window

AbstractIn this paper, an optimized 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 window techniques such as Kaiser Windows using simple linear optimization and run-length encoding (RLE). The Wavelet based compression techniques minimize the compression distortion, while RLE further increases the compression without any loss of relevant signal information. The developed technique employs a modified thresholding, which improves the compression of signal as compared to earlier existing thresholding technique. A comparative study of performance of different existing methods and the proposed wavelet filter is made in terms of compression ratio (CR), percent root mean square difference (PRD) and signal-to-noise ratio (SNR). When compared, the developed wavelet filter gives better compression ratio and also yields good fidelity parameters as compared to other methods. The simulation result included in this paper shows the clearly increased efficacy and performance in the field of biomedical signal processing