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Related Experiment Videos

Modified SPIHT wavelet compression for ECG signal.

M Singh1, V Kumar, S C Saxena

  • 1Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala-147004, Punjab, India.

Journal of Medical Engineering & Technology
|March 17, 2007
PubMed
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This study enhances the Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression for electrocardiogram (ECG) signals. The modified method achieves better compression ratios and accuracy without sacrificing efficiency.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Electrocardiogram (ECG) signal compression is crucial for efficient data storage and transmission.
  • Existing wavelet compression methods like SPIHT offer a foundation but can be improved for ECG applications.

Purpose of the Study:

  • To enhance the Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression algorithm for improved ECG signal compression.
  • To increase the compression ratio (CR) and reduce the percentage root-mean-square difference (PRD) of ECG signals.

Main Methods:

  • Modification of the existing SPIHT algorithm by incorporating two additional steps.
  • Application of the enhanced method to ECG records from the MIT-BIH arrhythmia database.

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Main Results:

  • Achieved higher compression ratios (CR) compared to the original SPIHT method.
  • Demonstrated a lower percentage root-mean-square difference (PRD), indicating better signal fidelity.
  • Maintained computational efficiency, simplicity, and self-adaptiveness.

Conclusions:

  • The modified SPIHT method offers a superior approach for ECG signal compression.
  • The enhancements provide a favorable trade-off between compression performance and signal reconstruction quality.
  • The technique is suitable for practical implementation in ECG data management systems.