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A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.

Jiawen Li1,2, Guanyuan Feng1, Jujian Lv1

  • 1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.

Brain Sciences
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Electroencephalography (EEG) analysis method for early multi-mental disorder detection. By analyzing entropy features, it achieves high accuracy with minimal data, improving diagnosis for conditions like schizophrenia, epilepsy, and depression.

Keywords:
electroencephalography (EEG)entropymachine learningmental disorders detection

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomedical Engineering

Background:

  • Mental health disorders present a growing global challenge, necessitating improved diagnostic tools.
  • Current diagnostic methods for mental health conditions are often subjective and time-consuming.
  • There is a critical need for objective, efficient, and early detection methods for multi-mental disorders.

Purpose of the Study:

  • To develop a lightweight, data-efficient method for early detection of multiple mental disorders.
  • To enhance diagnostic procedures and enable timely intervention for affected individuals.
  • To explore the utility of Electroencephalography (EEG) signal analysis for mental health assessment.

Main Methods:

  • Utilized Electroencephalography (EEG) signals as the primary data source.
  • Applied Discrete Wavelet Decomposition (DWT) to acquire brain rhythms.
  • Extracted various entropy measures (approximate, fuzzy, permutation, sample entropy) to create an entropy-based matrix.
  • Employed machine learning classifiers (SVM, kNN, NB, GAM, LDA, DT) for disorder detection.
  • Validated the method using public datasets for schizophrenia, epilepsy, and depression.

Main Results:

  • Identified representative single-channel EEG signals for each disorder (O1 for schizophrenia, F3 for epilepsy, O2 for depression).
  • Achieved high classification accuracies: 88.10% for schizophrenia, 75.47% for epilepsy, and 89.92% for depression.
  • Demonstrated effective multi-mental disorder detection with minimal data input.

Conclusions:

  • The proposed lightweight EEG analysis method offers a reliable approach for early multi-mental disorder detection.
  • The method enhances the interpretability of entropy features in EEG signals.
  • This approach advances understanding of the underlying mechanisms and pathological states of mental disorders, paving the way for improved patient outcomes.