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A novel fast detection algorithm for depression based on 3-channel EEG signals.

XiWu Guo1, ZiHan Guo2, TaoLi Xie1

  • 1Department of the People's Hospital of Taihe County, Fuyang, Anhui, China.

Frontiers in Neuroscience
|October 15, 2025
PubMed
Summary

This study introduces a new method using electroencephalogram (EEG) signals to quickly identify depression, a common cause of medically unexplained symptoms (MUS). The model achieved 97.42% accuracy, aiding early diagnosis and treatment.

Keywords:
EEG signalsLightGBMVMDdepressionmedically unexplained symptoms

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Psychiatry

Background:

  • Medically unexplained symptoms (MUS) present diagnostic challenges, particularly in distinguishing early depression. Delayed diagnosis of depression in MUS patients can lead to prolonged suffering and ineffective treatments.
  • Depression is a significant underlying factor for many MUS cases in middle-aged and elderly populations, yet early detection is often hindered by symptom presentation not meeting standard diagnostic criteria.

Purpose of the Study:

  • To develop and validate a rapid auxiliary diagnostic model for depression using electroencephalogram (EEG) signals.
  • To address the delay in depression diagnosis for patients with medically unexplained symptoms (MUS) by enabling timely identification.
  • To explore the potential of portable, real-time EEG devices for depression screening and pre-triage.

Main Methods:

  • Utilized 3-channel resting-state EEG signals from the prefrontal lobe.
  • Applied variational mode decomposition (VMD) for signal decomposition.
  • Selected intrinsic mode function (IMF) components using power spectrum analysis.
  • Extracted energy features through sample entropy.
  • Employed the LightGBM algorithm for classification.

Main Results:

  • Achieved a high classification accuracy of 97.42% for depression identification.
  • Demonstrated a balance between high accuracy and timeliness in the diagnostic model.
  • Comparative experiments validated the model's effectiveness.

Conclusions:

  • The proposed EEG-based model offers a promising, accurate, and timely solution for depression detection.
  • This approach supports the development of portable real-time EEG systems for depression screening.
  • It provides a valuable tool for the pre-triage of patients presenting with medically unexplained symptoms (MUS).