Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning

Naira, Carlos Alberto Torres; Alamo, Cristian Jos ́e L ́opez Del

doi:10.14569/ijacsa.2019.0101067

Cited by 38 publications

(18 citation statements)

References 18 publications

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“…In recent years, various investigations have provided automated SZ diagnosis via EEG signals using artificial intelligence (AI) methods (Prasad et al, 2013 ; Shim et al, 2016 ; Chu et al, 2017 ; Alimardani et al, 2018 ; Devia et al, 2019 ; Jahmunah et al, 2019 ; Li et al, 2019 ; Naira and Alamo, 2019 ; Oh et al, 2019 ; Phang et al, 2019a , b ; Aristizabal et al, 2020 ; Luo et al, 2020 ; Prabhakar et al, 2020 ; Shalbaf et al, 2020 ; Siuly et al, 2020 ; Sharma et al, 2021 ; Singh et al, 2021 ; Sun et al, 2021 ). The AI investigations in this field include conventional machine learning (ML) and deep learning (DL) methods (Khodatars et al, 2020 ; Shoeibi et al, 2020 , 2021 , a , b ).…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have studied other convolutional network (CNN) models utilization in SZ diagnosis via EEG signals. CNN models have been used in Naira and Alamo ( 2019 ) and Oh et al ( 2019 ) for SZ diagnosis, resulting in satisfactory achievements. CNN-recurrent neural network (RNN) models are an important group of DL networks and are significantly popular for their capability of various brain diseases diagnoses via EEG signals.…”

Section: Introductionmentioning

confidence: 99%

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

Shoeibi

Sadeghi

Moridian

et al. 2021

Front. Neuroinform.

117

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shoeibi

Sadeghi

Moridian

et al. 2021

Front. Neuroinform.

117

View full text Add to dashboard Cite

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“…Schizophrenia is one of the most common and severe mental disorders which is affecting more than 21 million people around the globe [1] and almost 50% of the total population of men [2] are suffering from this mental disease than women. This mental disorder directly affects the thinking pattern of human beings if it is not treated properly and can cause discrimination, stigmatization, and disobedience of human rights [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

Hussain¹,

Pirzada²,

Saba³

et al. 2021

IJACSA

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Over the recent years, Schizophrenia has become a serious mental disorder that is affecting almost 21 million people globally. There are different symptoms to recognize schizophrenia from healthy people. It can affect the thinking pattern of the brain. Delusions, hallucinations, and disorganized speech are the common symptoms of Schizophrenia. In this study, we have used electroencephalography (EEG) signals to analyze and diagnose Schizophrenia using machine learning algorithms and found that temporal features performed well as compared to statistical features. EEG signals are the best way to analyze this disorder as they are intimately linked with human thinking patterns and provide information about brain activities. The present work proposes a Machine Learning (ML) model based on Logistic Regression (LR) along with two feature extraction libraries Time Series Feature Extraction Library (TSFEL) and MNE Python toolkit to diagnose Schizophrenia from EEG signals. The results are analyzed based on 5 different sampling techniques. The dataset was cross-validated using leave one subject out cross-validation (LOSOCV) using Scikit learn and achieve greater accuracy, sensitivity, specificity, macro average recall, and macro f1 score on temporal features respectively.

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“…Deep-learning methods along with random forest and voting classifiers was performed by Chu et al for the individual recognition in schizophrenia using resting-state EEG streams [ 26 ]. Convolution Neural Networks (CNNs) along with the Pearson Correlation Coefficient (PCC) to represent the EEG channel relationships were used to classify the schizophrenic and healthy patients using EEG signals by Naira and Alamo [ 27 ]. Random Forest Machine learning algorithm was used to identify and diagnose the schizophrenia EEG signals by Zhang [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

Prabhakar

Rajaguru²,

Kim

2020

Computational Intelligence and Neuroscience

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One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area. For general cognitive activity analysis, electroencephalography (EEG) signals are widely used as a low-resolution diagnosis tool. The EEG signals are a great boon to understand the abnormality of the brain disorders, especially schizophrenia. In this work, schizophrenia EEG signal classification is performed wherein, initially, features such as Detrend Fluctuation Analysis (DFA), Hurst Exponent, Recurrence Quantification Analysis (RQA), Sample Entropy, Fractal Dimension (FD), Kolmogorov Complexity, Hjorth exponent, Lempel Ziv Complexity (LZC), and Largest Lyapunov Exponent (LLE) are extracted initially. The extracted features are, then, optimized for selecting the best features through four types of optimization algorithms here such as Artificial Flora (AF) optimization, Glowworm Search (GS) optimization, Black Hole (BH) optimization, and Monkey Search (MS) optimization, and finally, it is classified through certain classifiers. The best results show that, for normal cases, a classification accuracy of 87.54% is obtained when BH optimization is utilized with Support Vector Machine-Radial Basis Function (SVM-RBF) kernel, and for schizophrenia cases, a classification accuracy of 92.17% is obtained when BH optimization is utilized with SVM-RBF kernel.

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Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning

Cited by 38 publications

References 18 publications

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

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

Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

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