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Pre-processing of multi-channel EEG for improved compression performance using SPIHT.

Hoda Daou1, Fabrice Labeau

  • 1Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada. hoda.daou@mail.mcgill.ca

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study introduces a new Electroencephalogram (EEG) compression method using 2-D Discrete Wavelet Transform and Set Partitioning in Hierarchical Trees. The technique achieves high compression ratios with low distortion, proving stable across diverse patient recordings.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Electroencephalogram (EEG) data generates large volumes, necessitating efficient compression techniques for storage and transmission.
  • Existing EEG compression methods may struggle with balancing compression ratios and signal fidelity.
  • Inter-channel and intra-channel redundancies in EEG signals offer potential for improved compression algorithms.

Purpose of the Study:

  • To propose and evaluate a novel EEG compression technique.
  • To leverage both inter-channel and intra-channel redundancies for enhanced compression.
  • To assess the compression performance and stability of the proposed method.

Main Methods:

  • Utilized a 2-D Discrete Wavelet Transform (DWT) for signal decomposition.

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  • Employed Set Partitioning in Hierarchical Trees (SPIHT) for efficient coding of wavelet coefficients.
  • Incorporated smoothness transforms to optimize SPIHT performance in 2-D for EEG data.

    • Main Results:

    • Achieved high compression ratios (CRs) with low distortion values.
    • Demonstrated consistent performance across EEG recordings from different patients.
    • The method proved robust and stable, irrespective of recording characteristics.

    Conclusions:

    • The proposed 2-D DWT and SPIHT-based EEG compression technique is effective.
    • The method offers a favorable trade-off between compression efficiency and signal quality.
    • Its stability across diverse patient data makes it a promising solution for practical EEG data management.