Optimized VCG signal compression using sparse PSO

Vectorcardiogram (VCG) signal compression is very much in demand in the present-day scenario due to the increasing number of cardiac patients. Hence, in this paper, a new technique is proposed that compresses VCG signal by optimizing the tunable quality wavelet transform (TQWT) parameters. The noise in VCG signal is firstly removed by applying a Savitzky-Golay filter, and then passing noise-free signal to an optimization algorithm that optimizes the TQWT parameters, and obtains the frequency domain signal. This signal is then quantized through dead-zone quantization and processed by a lossless compression mechanism: run-length encoding (RLE) to improve the compression ratio & encode the signal. This compressed signal is reconstructed by Inverse RLE to obtain the decoded signal. Inverse of TQWT is applied to get the reconstructed signal back from the transformed frequency domain to time domain. The parameters of TQWT, especially theQandR, are optimized to get the highestCRat lowest percent root-mean-square-difference(PRD)with best reconstruction quality and least distortions, along with acceptable values of signal-to-noise-ratio(SNR), quality score(QS), andSimilaritywith lowest mean-square-error(MSE). The comparative analysis of different optimization methods indicates that the sparse-particle swarm optimization is best among all the approaches for the tuning of parameters in TQWT for VCG signal compression and reconstruction achieving aCRof 48.18 at aPRDof 3.68,SNRof 29.39,QSof 15.71, similarity of 0.99845,MSEof 0.00016, withQvalue of 2.04307 andRvalue of 1.20568 withcomputational timeof 4.48508 s.