Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia.

Objective: Electroencephalographic analysis (EEG) has emerged as a powerful tool for brain state interpretation. Studies have shown distinct deviances of patients with schizophrenia in EEG activation at specific frequency bands.
Methods: Evidence is presented for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. First, we trained a CNN on EEG data of 41 schizophrenia patients (SCZ) and 31 healthy controls (HC). Secondly, we used a pretrained model for training. Both models were tested in an external validation set of 15 SCZ, 16 HC, and 12 first-degree relatives.
Results: Using the layer-wise relevance propagation on the classification decision, a heatmap was produced for each subject, specifying the pixel-wise relevance. The CNN model resulted in the first case in a balanced accuracy of 63.7% and 81.5% in the second case, on the external validation test 64.5% and 83.2%, respectively.
Conclusions: The theta and alpha frequency bands of the EEG signals had significant relevance to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances.
Significance: The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)