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Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook.

Shaou-Gang Miaou1, Heng-Lin Yen, Chih-Lung Lin

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

IEEE Transactions on Bio-Medical Engineering
|June 27, 2002
PubMed
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This study introduces a new wavelet-domain vector quantizer for compressing electrocardiogram (ECG) signals. The novel tree vector approach achieves excellent compression with high-quality ECG reconstruction at very low bit rates.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Electrocardiogram (ECG) signal compression is crucial for efficient data storage and transmission.
  • Traditional wavelet transform-based vector quantization (WT-VQ) methods often require multiple codebooks, increasing complexity.
  • Existing methods may struggle to maintain high reconstruction quality at very low bit rates.

Purpose of the Study:

  • To propose a novel vector quantizer (VQ) in the wavelet domain for enhanced ECG signal compression.
  • To develop a unified codebook approach for WT-VQ, simplifying the compression process.
  • To ensure reliable quality of reconstructed ECG waveforms even at extremely low bit rates.

Main Methods:

  • A novel tree vector (TV) structure is proposed, arranging wavelet transformed (WT) coefficients hierarchically.

Related Experiment Videos

  • A single codebook is utilized for TVs extracted from various WT subbands, unlike traditional multi-codebook methods.
  • A distortion-constrained codebook replenishment mechanism dynamically updates codevectors to maintain waveform quality.

    • Main Results:

    • The proposed WT-VQ method achieves excellent visual and objective quality (low percent of root-mean-square difference - PRD).
    • An average PRD of 7.3% at 146 bits per second was obtained for the MIT/BIH Lead II ECG database.
    • The method demonstrates superior performance compared to recently published compression techniques, including set partitioning in hierarchical trees.

    Conclusions:

    • The novel tree vector quantizer in the wavelet domain offers a significant advancement in ECG signal compression.
    • The unified codebook and dynamic replenishment mechanism ensure high-fidelity ECG reconstruction at low bit rates.
    • This approach provides an efficient and effective solution for medical data compression, outperforming existing state-of-the-art methods.