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A machine learning based depression screening framework using temporal domain features of the electroencephalography
Sheharyar Khan1, Sanay Muhammad Umar Saeed1, Jaroslav Frnda2,3
1Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan.
Plos One
|March 27, 2024
Summary
Electroencephalography (EEG) effectively detects depression using a novel feature selection method. This approach achieved 96.36% accuracy, offering a promising tool for mental health diagnosis.
Area of Science:
- Neuroscience
- Psychiatry
- Biomedical Engineering
Background:
- Depression is a prevalent mental health disorder requiring accurate diagnosis.
- Electroencephalography (EEG) shows potential for identifying neural markers of depression.
- Early detection is crucial for effective depression treatment and management.
Purpose of the Study:
- To develop and validate an EEG-based mechanism for detecting mental depressive disorders.
- To utilize the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA) for depression detection.
- To enhance the accuracy and efficiency of depression classification using EEG data.
Main Methods:
- EEG data from 55 participants were analyzed using 3 electrodes in a resting-state condition.
- Twelve temporal domain features were extracted and processed through a novel feature selection algorithm.
- Classification was performed using Best-First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost algorithms.
Main Results:
- A novel feature selection mechanism identified optimal attributes with high discriminative power.
- The highest classification accuracy of 96.36% was achieved using the BF-Tree classifier.
- The proposed method demonstrated superior performance compared to existing state-of-the-art depression classification schemes.
Conclusions:
- The developed EEG-based framework offers a highly accurate method for detecting mental depressive disorders.
- The approach utilizes a minimal number of electrodes and a concise feature vector, enhancing practicality.
- This framework has the potential to significantly support psychiatrists in clinical settings for depression diagnosis.
