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A gated temporal-separable attention network for EEG-based depression recognition.

Lijun Yang1, Yixin Wang2, Xiangru Zhu3

  • 1School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.

Computers in Biology and Medicine
|March 17, 2023
PubMed
Summary

A new deep learning model, the gated temporal-separable attention network (GTSAN), effectively recognizes depression using EEG data. This advanced system aids in early diagnosis by analyzing historical and multilevel EEG features for high accuracy.

Keywords:
Attention mechanismDepression recognitionElectroencephalography (EEG)Gated recurrent unitTemporal convolution network

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Depression is a prevalent global mental illness requiring early diagnosis and treatment.
  • Electroencephalography (EEG) offers a promising avenue for objective depression assessment.

Purpose of the Study:

  • To introduce a novel deep learning framework, the gated temporal-separable attention network (GTSAN), for enhanced depression recognition using EEG signals.
  • To leverage advanced deep learning techniques for improved accuracy in diagnosing depression.

Main Methods:

  • The GTSAN model integrates a gated recurrent unit (GRU) for capturing temporal EEG dynamics.
  • A temporal-separable convolution network (TSCN) extracts multilevel features from fine to coarse scales.
  • An attention mechanism assigns differential weights to GRU and TSCN features for effective depression identification.

Main Results:

  • Experiments on two depression datasets validated the GTSAN model's capability.
  • The model successfully identified potential depression patterns within EEG data.
  • High recognition accuracies were achieved, demonstrating the model's efficacy.

Conclusions:

  • The proposed GTSAN model demonstrates significant potential for EEG-based depression diagnosis.
  • This deep learning approach offers a viable tool to assist clinicians in identifying depression.
  • Further development could lead to more accessible and objective mental health assessments.