Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based...

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Autores principales:	Afshin Shoeibi, Delaram Sadeghi, Parisa Moridian, Navid Ghassemi, Jónathan Heras, Roohallah Alizadehsani, Ali Khadem, Yinan Kong, Saeid Nahavandi, Yu-Dong Zhang, Juan Manuel Gorriz
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	schizophrenia neuroimaging EEG signals diagnosis deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/1600f8bfca8542d1a2ffc6f46ab008c8
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spelling	oai:doaj.org-article:1600f8bfca8542d1a2ffc6f46ab008c82021-12-01T02:45:08ZAutomatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models1662-519610.3389/fninf.2021.777977https://doaj.org/article/1600f8bfca8542d1a2ffc6f46ab008c82021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.777977/fullhttps://doaj.org/toc/1662-5196Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.Afshin ShoeibiDelaram SadeghiParisa MoridianNavid GhassemiJónathan HerasRoohallah AlizadehsaniAli KhademYinan KongSaeid NahavandiYu-Dong ZhangJuan Manuel GorrizFrontiers Media S.A.articleschizophrenianeuroimagingEEG signalsdiagnosisdeep learningNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	schizophrenia neuroimaging EEG signals diagnosis deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	schizophrenia neuroimaging EEG signals diagnosis deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Afshin Shoeibi Delaram Sadeghi Parisa Moridian Navid Ghassemi Jónathan Heras Roohallah Alizadehsani Ali Khadem Yinan Kong Saeid Nahavandi Yu-Dong Zhang Juan Manuel Gorriz Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
description	Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.
format	article
author	Afshin Shoeibi Delaram Sadeghi Parisa Moridian Navid Ghassemi Jónathan Heras Roohallah Alizadehsani Ali Khadem Yinan Kong Saeid Nahavandi Yu-Dong Zhang Juan Manuel Gorriz
author_facet	Afshin Shoeibi Delaram Sadeghi Parisa Moridian Navid Ghassemi Jónathan Heras Roohallah Alizadehsani Ali Khadem Yinan Kong Saeid Nahavandi Yu-Dong Zhang Juan Manuel Gorriz
author_sort	Afshin Shoeibi
title	Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
title_short	Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
title_full	Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
title_fullStr	Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
title_full_unstemmed	Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
title_sort	automatic diagnosis of schizophrenia in eeg signals using cnn-lstm models
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/1600f8bfca8542d1a2ffc6f46ab008c8
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_version_	1718405887351062528

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

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