TY - JOUR
T1 - A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders
AU - Lei, Yu
AU - Belkacem, Abdelkader Nasreddine
AU - Wang, Xiaotian
AU - Sha, Sha
AU - Wang, Changming
AU - Chen, Chao
N1 - Funding Information:
This work was financially supported by the National Natural Science Foundation of China (61806146, 61673391), National Key R&D Program of China (2018YFC1314500), Anti Coronavirus Project of Tianjin City (20ZXGBSY00060), Fundamental Research Funds for the Central Universities (JBF201903), Beijing Municipal Administration of Hospitals Incubating Program (PX2018063), Research Plan for Innovation in Clinical Technology by Beijing Hospitals Authority (XMLX201805), and Young and Middle-Aged Innovation Talents Cultivation Plan of Higher Institutions in Tianjin.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/2
Y1 - 2022/2
N2 - Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders.
AB - Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders.
KW - Bipolar disorder
KW - Electroencephalography
KW - Major depressive disorder
KW - Neural networks
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U2 - 10.1016/j.bspc.2021.103370
DO - 10.1016/j.bspc.2021.103370
M3 - Article
AN - SCOPUS:85119590701
SN - 1746-8094
VL - 72
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 103370
ER -