Accurate neural network classification model for schizophrenia disease based on electroencephalogram data

Abstract

Electroencephalogram is a useful interface system that translates the human electrical brain activity into voltage signals. By these means, the recorded brain waves can be employed to characterize, classify, or diagnose mental disorders. A novel neural network model to classify patients with schizophrenia based on electroencephalograms is presented. The proposed model decomposes the multichannel electroencephalogram records into a group of multivariate novel radial basis functions using a fuzzy means algorithm. The decomposition permits to extract different electroencephalogram channel information and distinguish between two sort of classes i.e., schizophrenic patients and healthy controls. Results show improved accuracy compared to classical algorithms reported in the literature i.e., Support Vector Machine, Bayesian Linear Discriminant Analysis, Decision Tree, Gaussian Naive Bayes, Random Forest, K-Nearest Neighbour, Convolutional Neuronal Network, or Adaboost. As a result, the method presented in this paper achieves the highest balanced accuracy, recall, precision and F1 score values, close to 93% in all cases. The model presented in this paper may be integrated in real time tools involved during the diagnostic of schizophrenia.