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Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted

S G Miaou1, J H Larn

  • 1Department of Electronic Engineering, Chung-Yuan Christian University, Taiwan, Republic of China. miaou@wavelet.el.cycu.edu.tw

Medical & Biological Engineering & Computing
|November 30, 2000
PubMed
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This study introduces an adaptive vector quantization (AVQ) method for compressing electrocardiogram (ECG) signals. The novel approach improves efficiency by addressing signal variations and redundancy, offering a practical solution for ECG data compression.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Discrete electrocardiogram (ECG) signals exhibit semi-periodic characteristics with variations in period (delta T) and amplitude (delta x).
  • These variations, linked to heart rate and baseline drift, reduce coding efficiency in traditional vector quantization (VQ) methods.
  • Standard VQ struggles with ECG data due to codevector redundancy in 2D codebooks and poor adaptability to signal changes.

Purpose of the Study:

  • To develop an efficient ECG signal compression technique addressing the limitations of traditional VQ.
  • To propose an adaptive VQ (AVQ) scheme that overcomes data redundancy and improves adaptability to signal variations.
  • To enhance compression performance by separately encoding amplitude variations (delta x) before applying the AVQ method.

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Main Methods:

  • A novel adaptive VQ (AVQ) scheme utilizing a one-dimensional (1D) codebook with overlapped and linearly shifted codevectors was developed.
  • The amplitude variation (delta x) component of the ECG signal was extracted and encoded independently.
  • The proposed 1D-AVQ method was tested on ECG records from the MIT/BIH arrhythmic database without prior codebook training.

Main Results:

  • The AVQ scheme achieved a compressed data rate of 265.2 +/- 92.3 bits s-1 with a peak-to-root-mean-square-difference (PRD) of 10.0 +/- 4.1%.
  • No codebook storage or transmission was required, with only minimal temporary storage needed during the coding process.
  • The linear shifting of codevectors facilitates easier hardware implementation compared to existing AVQ methods.

Conclusions:

  • The proposed 1D-AVQ method offers an efficient and adaptable solution for ECG signal compression.
  • The technique effectively reduces data redundancy and improves adaptability to signal variations, outperforming traditional VQ.
  • The method's hardware implementability and minimal storage requirements make it a practical choice for real-world ECG compression applications.