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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Optimized VCG signal compression using sparse PSO.

Aditya Tiwari1, Ronak Vimal Vimal1, Anil Kumar1

  • 1PDPM-Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, M.P., India.

Biomedical Physics & Engineering Express
|April 16, 2026
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Summary
This summary is machine-generated.

A new method optimizes Tunable Quality Wavelet Transform (TQWT) parameters for Vectorcardiogram (VCG) signal compression. This approach achieves high compression ratios with excellent signal reconstruction quality, addressing the growing need for efficient cardiac data management.

Keywords:
DZQIRLERLETQWTVCGdata compressionmeta heuristic optimization

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Increasing cardiac patient numbers necessitate efficient Vectorcardiogram (VCG) signal compression.
  • Existing compression techniques may not meet the demand for high compression ratios (CR) and quality.

Purpose of the Study:

  • To propose and evaluate a novel VCG signal compression technique.
  • To optimize Tunable Quality Wavelet Transform (TQWT) parameters for improved VCG compression and reconstruction.

Main Methods:

  • Noise reduction using Savitzky-Golay filter.
  • TQWT parameter optimization via Sparse-PSO algorithm.
  • Compression using Dead-Zone Quantization (DZQ) and Run-Length Encoding (RLE).

Main Results:

  • Sparse-PSO optimization achieved a CR of 48.18 with PRD of 3.68.
  • High signal reconstruction quality indicated by SNR of 29.39, QS of 15.71, and similarity of 0.99845.
  • Optimized TQWT parameters (Q=2.04307, R=1.20568) yielded minimal MSE (0.00016).

Conclusions:

  • The proposed TQWT optimization method effectively compresses VCG signals.
  • Sparse-PSO is superior for tuning TQWT parameters in VCG compression.
  • The technique offers a promising solution for managing large VCG datasets efficiently.