Diagnosis of Schizophrenia from EEG signals Using ML Algorithms

Tariq Qayyum; Zouheir Trabelsi; Assadullah Tariq; Abdelkader Nasreddine Belkacem; Mohamed Serhani

doi:10.1109/BIBM58861.2023.10385709

Diagnosis of Schizophrenia from EEG signals Using ML Algorithms

Tariq Qayyum, Zouheir Trabelsi, Assadullah Tariq, Abdelkader Nasreddine Belkacem, Mohamed Serhani

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment.

Original language	English
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2564-2570
Number of pages	7
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385709
Publication status	Published - 2023
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: Dec 5 2023 → Dec 8 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	12/5/23 → 12/8/23

Keywords

CNN
EEG
KNN
SVM
Schizophrenia
logistic Regression

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Automotive Engineering
Modelling and Simulation
Health Informatics

Access to Document

10.1109/BIBM58861.2023.10385709

Cite this

Qayyum, T., Trabelsi, Z., Tariq, A., Belkacem, A. N., & Serhani, M. (2023). Diagnosis of Schizophrenia from EEG signals Using ML Algorithms. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 2564-2570). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385709

Diagnosis of Schizophrenia from EEG signals Using ML Algorithms. / Qayyum, Tariq; Trabelsi, Zouheir; Tariq, Assadullah et al.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2564-2570 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Qayyum, T, Trabelsi, Z, Tariq, A, Belkacem, AN & Serhani, M 2023, Diagnosis of Schizophrenia from EEG signals Using ML Algorithms. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 2564-2570, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 12/5/23. https://doi.org/10.1109/BIBM58861.2023.10385709

Qayyum T, Trabelsi Z, Tariq A, Belkacem AN , Serhani M. Diagnosis of Schizophrenia from EEG signals Using ML Algorithms. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2564-2570. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385709

Qayyum, Tariq ; Trabelsi, Zouheir ; Tariq, Assadullah et al. / Diagnosis of Schizophrenia from EEG signals Using ML Algorithms. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2564-2570 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

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title = "Diagnosis of Schizophrenia from EEG signals Using ML Algorithms",

abstract = "Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment.",

keywords = "CNN, EEG, KNN, SVM, Schizophrenia, logistic Regression",

author = "Tariq Qayyum and Zouheir Trabelsi and Assadullah Tariq and Belkacem, {Abdelkader Nasreddine} and Mohamed Serhani",

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year = "2023",

doi = "10.1109/BIBM58861.2023.10385709",

language = "English",

series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

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editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

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AU - Trabelsi, Zouheir

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AU - Serhani, Mohamed

PY - 2023

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AB - Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment.

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KW - KNN

KW - SVM

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KW - logistic Regression

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Diagnosis of Schizophrenia from EEG signals Using ML Algorithms

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