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Vectorcardiogram signal compression: A hybrid approach using discrete wavelet transform and singular vector sparse

Deepak Mishra¹, Anil Kumar¹

¹Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, India.

Journal of Electrocardiology

|June 10, 2025

Summary

This study introduces an efficient vectorcardiography (VCG) data compression method using Discrete Wavelet Transform and Singular Vector Sparse Reconstruction. The technique significantly reduces data size while preserving crucial cardiac information for improved storage and transmission.

Keywords:

Compression Discrete wavelet transform Interpolation Singular vector sparse reconstruction (SVSR)Sparse sampling

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Area of Science:

Biomedical Engineering
Signal Processing
Medical Informatics

Background:

Continuous cardiac monitoring generates large volumes of vectorcardiography (VCG) data.
Managing and transmitting VCG data presents storage and bandwidth challenges, especially in resource-limited or remote settings.
Efficient data compression is crucial for effective cardiac data handling and tele-transmission.

Purpose of the Study:

To develop an efficient data compression technique for vectorcardiography (VCG) signals.
To reduce the storage space and bandwidth requirements for VCG data.
To enhance the speed of data transmission for cardiac monitoring applications.

Main Methods:

A two-stage compression and reconstruction technique is proposed.
Discrete Wavelet Transform (DWT) with Haar wavelet decomposes VCG signals into frequency components.
Singular Vector Sparse Reconstruction (SVSR) is applied to subbands for enhanced compression, followed by interpolation and inverse DWT (IDWT) for reconstruction.

Main Results:

The proposed method was evaluated using the PTB diagnostic ECG database.
Quantitative metrics including compression ratio (CR), signal-to-noise ratio (SNR), and percent root mean square difference (PRD) were used.
The method demonstrated a 55.67% higher CR and 57.12% improved PRD compared to existing methods.

Conclusions:

The developed VCG data compression technique is efficient and adaptable.
It offers control over the quality of reconstructed data.
The method is a promising solution for VCG data storage and tele-transmission, particularly in remote healthcare scenarios.