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Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network.

Faezeh Ghasemi1, Ahmad Shalbaf2, Ali Esteki1

  • 1Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Physical and Engineering Sciences in Medicine
|August 28, 2025
PubMed
Summary

This study introduces a novel deep learning approach using Electroencephalography (EEG) signals for early Obsessive-compulsive disorder (OCD) detection. An ensemble of Convolutional Neural Network (CNN) models achieved high accuracy in identifying OCD patients.

Keywords:
Deep ConvNetDeep learningEEGNetElectroencephalographyObsessive–compulsive disorderShallow ConvNet

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Obsessive-compulsive disorder (OCD) significantly impacts individuals' lives due to intrusive thoughts and compulsive behaviors.
  • Early diagnosis of OCD is crucial for effective management and treatment.
  • Current diagnostic methods can be time-consuming and may not capture the full spectrum of the disorder.

Purpose of the Study:

  • To develop and evaluate deep learning models for the early diagnosis of OCD using Electroencephalography (EEG) signals.
  • To compare the performance of individual pre-trained Convolutional Neural Network (CNN) models with an ensemble approach.
  • To investigate the efficacy of transfer learning and optimization algorithms in EEG-based OCD detection.

Main Methods:

  • Utilized raw scalp-EEG data from OCD patients.
  • Developed and fine-tuned three pre-trained CNN models: EEGNet, Shallow ConvNet, and Deep ConvNet.
  • Implemented an ensemble of these CNN models using weighted majority voting, with weights optimized by the Differential Evolution (DE) algorithm.

Main Results:

  • Shallow ConvNet demonstrated strong individual performance with 85.91% accuracy.
  • The ensemble model achieved superior results, reaching 87.03% accuracy, 82.21% sensitivity, and 96.69% specificity.
  • The proposed hybrid model effectively extracted distinctive EEG features for accurate OCD identification.

Conclusions:

  • Deep learning models, particularly an ensemble of CNNs, show significant promise for the early and accurate diagnosis of OCD using EEG signals.
  • Transfer learning and optimization techniques enhance the diagnostic capabilities of EEG-based systems.
  • The developed hybrid model offers a potential non-invasive tool for clinical OCD screening.