Résumé: Schizophrenia is a complex psychiatric disorder characterized by delusions, hallucinations, disorganized speech, mood disturbances, and abnormal behavior. Early diagnosis of schizophrenia depends on the manifestation of the disorder, its symptoms are complex, heterogeneous and cannot be clearly separated from other neurological categories. Therefore, its early diagnosis is quite difficult. An objective, effective and simple diagnostic model and procedure are essential for diagnosing schizophrenia. Electroencephalography (EEG)-based models are a strong candidate to overcome these limits. In this study, we proposed an EEG-based solution for the diagnosis of schizophrenia using 1D-convolutional neural network deep learning approach and multitaper method. Firstly, the raw EEG signals were segmented and denoised using multiscale principal component analysis. Then, three different feature sets were extracted using leading feature extraction methods such as periodogram, welch, and multitaper. The performance of each feature extraction method was compared. Finally, classification performance of support vector machine, decision trees, k-nearest neighbors, and 1D-convolutional neural network algorithms were tested according to model evaluation criteria. The highest performance was obtained with the multitaper and 1D-convolutional neural network approach, and the highest accuracy was 98.76%. The results of the model were found to be 0.991 sensitivity, 0.984 precision, 0.983 specificity, 0.975 Matthews correlation coefficient, 0.987 f1-score, and 0.975 kappa statistic. This study presents the multitaper and 1D-convolutional neural network approach framework for the first time in the diagnosis of schizophrenia. Moreover, this study achieved satisfactorily high classification performance for the diagnosis of schizophrenia compared to methods in the relevant literature.

Langue: Anglais
Thème Informatique

Mots clés:
Deep learning
Feature extraction
Signal processing
EEG
1D-convolutional neural network
Schizophrenia