Key result
Sparse-PSO optimization outperforms other methods for VCG signals, achieving a ~48 compression ratio.
Why the study?
Vectorcardiogram signal compression is in high demand due to an increasing number of cardiac patients.
Sparse-PSO optimization of TQWT parameters provides highly efficient VCG signal compression with minimal distortion, potentially facilitating better data storage and transmission for cardiac monitoring.
Answers are grounded in this paper and cite their sources.
Meets demand for efficient VCG handling amid rising cardiac volumes; extends TQWT optimization via Sparse PSO for superior compression fidelity.
Vectorcardiogram (VCG) signal compression is very much in demand by 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 (DZQ) and processed by a lossless compression mechanism: Run-Length Encoding (RLE) to improve the compression ratio (CR) & encode the signal. This compressed signal is reconstructed by Inverse Run-Length Encoding (IRLE) 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 the Q and R, are optimized to get the highest CR at 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), and Similarity with lowest Mean-Square-Error (MSE). The comparative analysis of different optimization methods indicates that the Sparse-PSO is best among all the approaches for the tuning of parameters in TQWT for VCG signal compression and reconstruction achieving a CR of 48.18 at a PRD of 3.68, SNR of 29.39, QS of 15.71, similarity of 0.99845, MSE of 0.00016, with Q value of 2.04307 and R value of 1.20568 with computational time of 4.48508 seconds.
No takes yet. Share an insight, caveat, or question.
Tiwari et al. (2026) studied Vectorcardiogram (VCG) signal compression. Sparse-PSO optimized Tunable Quality Wavelet Transform (TQWT) vs. Other optimization methods was evaluated on Compression ratio (CR) and Percent Root-Mean-Square-Difference (PRD). Sparse-PSO optimization for VCG signal compression achieved a compression ratio of 48.18 at a PRD of 3.68, outperforming other optimization methods.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: