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Automated detection of depression using wavelet scattering networks.

Nishant Sharma1, Manish Sharma1, Jimit Tailor1

  • 1Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.

Medical Engineering & Physics
|February 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system using electroencephalogram (EEG) signals and a Deep Wavelet Scattering Network (DWSN) for depression detection. The novel method achieves high accuracy, offering a superior alternative to manual analysis for clinical and home use.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Depression is a prevalent global mental health issue impacting mood and quality of life.
  • Manual analysis of electroencephalogram (EEG) signals for depression detection is complex, time-consuming, and requires specialized skills.
  • Modern lifestyles contribute to the increasing prevalence of depression, necessitating efficient diagnostic tools.

Purpose of the Study:

  • To develop an automated system for detecting depression using EEG signals.
  • To evaluate the efficacy of a novel Deep Wavelet Scattering Network (DWSN) for this automated detection.
  • To compare the performance of machine learning algorithms for classifying depression based on EEG features.

Main Methods:

  • Utilized clinically available datasets (GMC, MODMA) comprising EEG signals from depressed patients and healthy subjects.
  • Developed and applied a novel Deep Wavelet Scattering Network (DWSN) for feature extraction from EEG signals.
  • Fed extracted features into various machine learning algorithms to identify the best-performing classifier for depression detection.

Main Results:

  • For the GMC dataset, Medium Neural Network (MNN) achieved 99.95% accuracy, with precision, recall, and F1-score of 1.
  • For the MODMA dataset, Wide Neural Network (WNN) achieved 99.3% accuracy, with precision, recall, and F1-score of 0.99.
  • The proposed DWSN method demonstrated superior performance compared to existing methodologies.

Conclusions:

  • The developed automated system using DWSN and machine learning effectively detects depression from EEG signals.
  • The proposed method offers a highly accurate and efficient alternative to traditional manual EEG analysis.
  • This automated approach holds potential for widespread application in both clinical settings and remote home-based diagnosis.