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ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds.

Gülay Tohumoglu1, K Erbil Sezgin

  • 1Department of Electrical and Electronics Engineering, University of Gaziantep, 27310 Gaziantep, Turkey. g_tohumoglu@gantep.edu.tr

Computers in Biology and Medicine
|February 4, 2006
PubMed
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The modified embedded zero-tree wavelet (MEZW) algorithm offers superior compression and computational efficiency for electrocardiogram (ECG) signals compared to existing methods. It achieves better compression ratios for a given error, especially with biorthogonal wavelets.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Electrocardiogram (ECG) signal compression is crucial for efficient data storage and transmission in healthcare.
  • Existing ECG compression schemes often face limitations in efficiency and computational cost.
  • The Embedded Zero-Tree Wavelet (EZW) algorithm, originally for image compression, has been adapted for signal processing.

Purpose of the Study:

  • To introduce and evaluate the Modified Embedded Zero-Tree Wavelet (MEZW) compression algorithm for one-dimensional signals, specifically ECG.
  • To compare the performance of MEZW against the traditional EZW algorithm and other ECG compression schemes.
  • To assess the impact of different wavelet types (biorthogonal vs. orthogonal) and threshold definitions on compression performance.

Main Methods:

Related Experiment Videos

  • Implementation of the MEZW algorithm, adapted from Shapiro's EZW, for ECG signal compression.
  • Application of two threshold definitions: percentage and dyadic thresholds.
  • Quantitative comparison of MEZW and EZW using Compression Ratio (CR) and Percentage Root Mean Square Difference (PRD).
  • Testing on selected records from the MIT-BIH arrhythmia database and an original ECG signal.

Main Results:

  • The MEZW algorithm demonstrates significantly higher compression efficiency and computational speed compared to previously proposed ECG compression schemes.
  • MEZW provides exact bit rate control and generates a progressive bit stream (quality or rate).
  • MEZW achieves a clear advantage in CR for a given PRD over the traditional EZW algorithm.
  • MEZW yields superior results with biorthogonal wavelets compared to orthogonal wavelets.

Conclusions:

  • The MEZW algorithm is a highly efficient and effective method for ECG signal compression.
  • MEZW offers improved performance over traditional EZW, particularly when utilizing biorthogonal wavelets.
  • The algorithm's ability for exact bit rate control and progressive bit stream generation enhances its practical applicability.