Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals

Psychotic disorders are mental disorders that negatively affect human life. Diagnosis of psychotic patients is usually done in consultation with the patient, and this is a time-consuming process. In this study, a Computer Aided Diagnosis (CAD) system that will support expert opinion with automatic diagnosis of schizophrenia (SZ) disease, which is the leading psychotic disorder, is presented. In this study, Hilbert Huang Transform (HHT) method was used to analyze the non-stationary and non-periodic structure of EEG (Electroencephalograph) signals in the best way. The data set we used in our study includes 19-channel EEG signals from 28 (14 SZ and 14 healthy controls) participants, and the second data set includes 16-channel EEG signals from 84 (45 SZ and 39 healthy controls) participants. First of all, HS (Hilbert Spectrum) images of the first four Intrinsic Mode Functions (IMF) components obtained by applying Empirical Mode Decomposition (EMD) to EEG signals were created. These images were then classified with the VGG16 pre-trained Convolutional Neural Network (CNN) network. With our proposed method, the highest classification performance was obtained as 98.2% for Dataset I and 96.02% for Dataset II, respectively, by training the HS images obtained from the IMF 1 component with the VGG16 pre-trained CNN network. In the next step, classification performances were tested with VGG16, XCeption, DenseNet121, ResNet152 and Inception V3 pre-trained CNN networks. The high classification success achieved by the proposed method in our study demonstrates the accuracy of the model in distinguishing between SZ and healthy control.