Depression diagnosis using a hybrid residual neural network
by Mahsa Ofoghi Rezaei; Somayeh Makouei; Sebelan Danishvar
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 42, No. 3, 2023

Abstract: Depression is one of the most widespread psychiatric disorders. EEG signals can be utilised as a tool to diagnose depression objectively. This paper employs a hybrid method to classify healthy and depressed signals, which uses a pre-trained ResNet101 to extract features automatically. Thereby, the problem of designing and training deep networks for automatic feature extraction is solved. The hypothesis in the present study is that feature-extraction layers in ResNet101 also perform desirably in detecting depressed signals. In hybrid structures, SVM, KNN, and DT classifiers are used for final classification purposes. ResNet101-SVM, ResNet101-KNN, and ResNet101-DT structures have reached accuracy of 93.8%, 90.1%, and 82.1%, respectively. Moreover, for the ResNet101-SVM structure, which has shown the best performance among all structures, the accuracy, sensitivity, and specificity are 94.7%, 94.0%, and 95.2% after applying the ten-fold cross-validation method. The results indicate the proper performance of all structures, especially the ResNet101-SVM structure, in diagnosing depression.

Online publication date: Fri, 28-Jul-2023

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