TY  - JOUR
VL  - 9
A1  - Khan, D.M.
A1  - Yahya, N.
A1  - Kamel, N.
A1  - Faye, I.
JF  - IEEE Access
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099189167&doi=10.1109%2fACCESS.2021.3049427&partnerID=40&md5=e51b0b1f1f66bb3f77497048899680f3
Y1  - 2021///
ID  - scholars15947
KW  - Convolution; Electroencephalography; Electrophysiology; Surveys
KW  -  Default mode network (DMN); Diagnosis algorithms; Effective connectivities; Major depressive disorder (MDD); Psychiatric evaluation; Quantitative diagnosis; Sensitivity and specificity; Threedimensional (3-d)
KW  -  Convolutional neural networks
N2  - 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.
EP  - 8846
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 21693536
SP  - 8835
TI  - Automated Diagnosis of Major Depressive Disorder Using Brain Effective Connectivity and 3D Convolutional Neural Network
N1  - cited By 25
AV  - none
ER  -