Automated EEG-based screening of depression using deep convolutional neural network

By Usha R. Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli, D. Subha. Comput. Methods Programs Biomed., 2018

526 citations recorded by the source corpus.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI).

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5636c447f92d01d2fa27702aebff20dbf99c621d

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

A multi-modal open dataset for mental-disorder analysis (2020, methodology): “[3] focused on the analysis of EEG alpha frequency and suggested burnout was associated with alpha power, whereas depression was linked to individual alpha frequency.”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, background): “The tasks presented within these studies fell into six groups: emotion recognition (16%), motor imagery (22%), mental workload (16%), seizure detection (14%), and sleep stage scoring (9%), event related potential detection (10%), and other studies (13%), which include Alzheimer’s classification [16–”
Automated assessment of psychiatric disorders using speech: A systematic review (2019, unknown): “153”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “[207] proposed a CNN approach for depression screening”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “The EEG signals are widely preferred as they are economical, portable, and show clear rhythms in the frequency domain [8,9].”