ECG Signal Compression based on Wavelet Parameterization and Thresholding using PSO

This paper presents an electrocardiogram (ECG) compression algorithm based on discrete wavelet transform (DWT) and particle swarm optimization (PSO). This technique uses PSO to find the optimal values of wavelet design parameters and threshold levels for ECG compression. In this method, ECG signal is segmented into different sub-bands to obtain the wavelet coefficients. Then, threshold values for each sub- band are selected using PSO. The coefficients with value below the threshold level are left, and the significant coefficients are encoded using modified run length encoding. The performance measures used in this paper are quality score (QS), compression ratio (CR), signal-to-noise ratio (SNR) and percentage root-mean-square difference (PRD). This method is capable of providing higher compression ratio with minimum distortion in ECG signal. Compression ratio obtained using this algorithm is 28.43 at PRD=2.63.