Volume 79, Issue 10 (January 2022)
Tehran Univ Med J 2022, 79(10): 754-763 |
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Bagherzadeh S, Maghsoudi A, Shalbaf A. Detection of schizophrenia patients using convolutional neural networks from brain effective connectivity maps of electroencephalogram signals. Tehran Univ Med J 2022; 79 (10) :754-763
URL: http://tumj.tums.ac.ir/article-1-11465-en.html
URL: http://tumj.tums.ac.ir/article-1-11465-en.html
1- Department of Biomedical Engineering, Faculty of Medical Sciences and Technologies, Sciences and Research Branch, Islamic Azad University, Tehran, Iran.
2- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,Shalbaf@sbmu.ac.ir
2- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,
Abstract: (1672 Views)
Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced deep learning algorithms.
Methods: In this analytic study, which is done from April to October 2021 in Tehran, 19-channel electroencephalogram signals from 14 schizophrenia patients and 14 healthy individuals were recorded and pre-processed. Then, the effective connectivity measure using the transfer entropy method is estimated from them and a 19×19 asymmetric connectivity matrix is constructed and represented by a color map as an image. Then, these effective connectivity images are used as inputs to the five pre-trained neural networks of AlexNet, Resnet-50, Shufflenet, Inception, and Xception. Finally, the parameters of these networks are fine-tuned to diagnose schizophrenia patients. All models are fine-tuned based on newly constructed images using the adaptive moment estimation optimizer algorithm and cross-entropy as the loss function. 10-fold cross-validation and subject-independent validation methods are used to evaluate the proposed method.
Methods: In this analytic study, which is done from April to October 2021 in Tehran, 19-channel electroencephalogram signals from 14 schizophrenia patients and 14 healthy individuals were recorded and pre-processed. Then, the effective connectivity measure using the transfer entropy method is estimated from them and a 19×19 asymmetric connectivity matrix is constructed and represented by a color map as an image. Then, these effective connectivity images are used as inputs to the five pre-trained neural networks of AlexNet, Resnet-50, Shufflenet, Inception, and Xception. Finally, the parameters of these networks are fine-tuned to diagnose schizophrenia patients. All models are fine-tuned based on newly constructed images using the adaptive moment estimation optimizer algorithm and cross-entropy as the loss function. 10-fold cross-validation and subject-independent validation methods are used to evaluate the proposed method.
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Results: The results of the study showed that the highest average accuracy, precision, sensitivity and F-score for classification of two classes of schizophrenia and healthy using the connectivity images and the Inception model achieved equal to 96.52%, 95.89%, 97.22% and 96.55%, respectively, in subject-independent validation method and 98.51%, 98.51%, 98.51% and 98.51% for the 10-fold cross-validation method. Also, there was less effective connectivity between schizophrenic patients than healthy individuals and these patients generally have much less information flow.
Conclusion: Based on our results, the proposed new model can effectively analyze brain function and be useful for psychiatrists to accurately diagnose schizophrenia patients and reduce the possible error and subsequently inappropriate treatment. |
Type of Study: Original Article |
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