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Related Experiment Video

Updated: Jun 29, 2026

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Machine Learning-Based Depression Recognition With Preserved Efficacy From Compressed EEG Signals Using Wavelet

Marjan Rezakhani Taleghani1, Hadi Grailu1

  • 1Electrical Engineering Department Shahrood University of Technology Shahrood Iran.

Healthcare Technology Letters
|May 13, 2026
PubMed
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This study introduces a novel lossy compression method for electroencephalogram (EEG) signals, significantly reducing storage needs while maintaining high accuracy for diagnosing depression. The technique enhances telemedicine capabilities in resource-limited environments.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Neuroscience

Background:

  • Electroencephalogram (EEG) signals are vital for diagnosing neurological disorders like depression.
  • High storage requirements for EEG data challenge telemedicine applications.
  • Efficient data compression is crucial for widespread EEG-based diagnostics.

Purpose of the Study:

  • To develop a novel lossy compression method for EEG signals.
  • To minimize storage requirements while preserving diagnostic accuracy for depression.
  • To evaluate the effectiveness of the proposed method in telemedicine scenarios.

Main Methods:

  • EEG signals were converted into 2D matrices and compressed using a combination of zigzag/spiral rearrangement, bior4.4 wavelet transform, and adaptive filtering.
Keywords:
adaptive filtercompressiondiagnosis of depression disorderelectroencephalogram (EEG)machine learningwavelet transform

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  • Four wavelet encoders (SPIHT, STW, EZW, LVL-MMC) were employed for compression.
  • A feedforward artificial neural network (ANN) utilizing relative wavelet energy and entropy features classified depression.

    • Main Results:

    • The zigzag and STW method achieved a compression ratio of 89.30%, PRD of 0.23, and PSNR of 58.80 dB.
    • Depression recognition accuracy reached 90.2% using compressed signals.
    • Compressed signals occasionally showed improved diagnostic performance over original signals, potentially due to noise reduction.

    Conclusions:

    • The proposed lossy compression method offers an efficient solution for EEG storage.
    • The method effectively preserves, and sometimes enhances, depression diagnostic accuracy.
    • This approach is suitable for telemedicine and resource-constrained settings requiring accurate neurological disorder diagnosis.