eprintid: 15947
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/59/47
datestamp: 2023-11-10 03:30:34
lastmod: 2023-11-10 03:30:34
status_changed: 2023-11-10 02:00:49
type: article
metadata_visibility: show
creators_name: Khan, D.M.
creators_name: Yahya, N.
creators_name: Kamel, N.
creators_name: Faye, I.
title: Automated Diagnosis of Major Depressive Disorder Using Brain Effective Connectivity and 3D Convolutional Neural Network
ispublished: pub
keywords: Convolution; Electroencephalography; Electrophysiology; Surveys, Default mode network (DMN); Diagnosis algorithms; Effective connectivities; Major depressive disorder (MDD); Psychiatric evaluation; Quantitative diagnosis; Sensitivity and specificity; Threedimensional (3-d), Convolutional neural networks
note: cited By 25
abstract: Major depressive disorder (MDD), which is also known as unipolar depression, is one of the leading sources of functional frailty. MDD is mostly a chronic disorder that requires a long duration of treatment and clinical management. One of the critical issues in MDD treatment is the need for it's early diagnosis. Conventional tools in MDD diagnosis are based on questionnaires and other forms of psychiatric evaluations. However, the subjective nature of these tools may lead to misleading inferences. Recently, brain electroencephalography (EEG) signals have been used for the quantitative diagnosis of MDD. Nevertheless, a further improvement of the proposed methods in terms of accuracy and clinical utility is required. In this study, EEG signals from 30 MDD and 30 healthy control (HC) are used to estimate the effective connectivity within the brain default mode network (DMN). Then, effective connections between the major six regions of the DMN are used to train and test a three-dimensional (3D) convolutional neural network. Here, connectivity samples generated from half of the subjects are used for training while the rest are used for testing. The results show that the proposed MDD diagnosis algorithm achieved 100 accuracy,sensitivity and specificity in classifying MDD and HC test subjects. © 2013 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099189167&doi=10.1109%2fACCESS.2021.3049427&partnerID=40&md5=e51b0b1f1f66bb3f77497048899680f3
id_number: 10.1109/ACCESS.2021.3049427
full_text_status: none
publication: IEEE Access
volume: 9
pagerange: 8835-8846
refereed: TRUE
issn: 21693536
citation:   Khan, D.M. and Yahya, N. and Kamel, N. and Faye, I.  (2021) Automated Diagnosis of Major Depressive Disorder Using Brain Effective Connectivity and 3D Convolutional Neural Network.  IEEE Access, 9.  pp. 8835-8846.  ISSN 21693536