Prediction of Schizophrenia Using Feature Extraction Methods with EEG Data

Osman Küçük

Yildiz Technical University

https://orcid.org/0009-0002-1745-5952

İsmail Cantürk

Yildiz Technical University

https://orcid.org/0000-0003-0690-1873

DOI: https://doi.org/10.56038/oprd.v5i1.528

Keywords: Biomedical Enginnering, Machine Learning, Schizophrenia, EEG


Abstract

Schizophrenia is a mental disorder that causes some motor dysfunctions in individuals and causes psychotic symptoms. It is believed that machine learning algorithms offer support in the detection and treatment process of the disease. In this study, a system that predicts schizophrenia disease with machine learning algorithms is proposed using resting EEG data. Filtering process, feature extraction methods and cross-validation were performed before machine learning.


References

İ. Cantürk, "A computerized method to assess Parkinson’s disease severity from gait variability based on gender," Biomedical Signal Processing and Control, vol. 66, p. 102497, 2021. DOI: https://doi.org/10.1016/j.bspc.2021.102497

İ. Cantürk and O. Günay, "Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease," Cognitive Computation, pp. 1-12, 2024. DOI: https://doi.org/10.1007/s12559-024-10254-8

İ. Cantürk and L. Özyılmaz, "A Deep Feature Driven Expert System to Estimate the Postmortem Interval From Corneal Opacity Development," Expert Systems, p. e13757, 2024. DOI: https://doi.org/10.1111/exsy.13757

M. M. Ahsan, S. A. Luna, and Z. Siddique, "Machine-learning-based disease diagnosis: A comprehensive review," in Healthcare, 2022, vol. 10, no. 3: MDPI, p. 541. DOI: https://doi.org/10.3390/healthcare10030541

İ. Cantürk, "Parkinson hastalığının derecesi ile yürüyüş değişkenliği arasındaki ilişkinin bulanık tekrarlılık grafiğine göre araştırılması," Avrupa Bilim ve Teknoloji Dergisi, no. 19, pp. 410-419, 2020. DOI: https://doi.org/10.31590/ejosat.699099

İ. Cantürk, "A Feature Driven Intelligent System for Neurodegenerative Disorder Detection: An Application on Speech Dataset for Diagnosis of Parkinson’s Disease," International Journal on Artificial Intelligence Tools, vol. 30, no. 03, p. 2150011, 2021. DOI: https://doi.org/10.1142/S0218213021500111

J. Li et al., "A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals," Brain Sciences, vol. 14, no. 10, p. 987, 2024. DOI: https://doi.org/10.3390/brainsci14100987

S. Bagherzadeh and A. Shalbaf, "EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning," Cognitive Neurodynamics, pp. 1-12, 2024. DOI: https://doi.org/10.2139/ssrn.4411795

J. R. De Miras, A. J. Ibáñez-Molina, M. F. Soriano, and S. Iglesias-Parro, "Schizophrenia classification using machine learning on resting state EEG signal," Biomedical Signal Processing and Control, vol. 79, p. 104233, 2023. DOI: https://doi.org/10.1016/j.bspc.2022.104233

M. Nieuwenhuis, N. E. van Haren, H. E. H. Pol, W. Cahn, R. S. Kahn, and H. G. Schnack, "Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples," Neuroimage, vol. 61, no. 3, pp. 606-612, 2012. DOI: https://doi.org/10.1016/j.neuroimage.2012.03.079

J. Sun et al., "A hybrid deep neural network for classification of schizophrenia using EEG Data," Scientific Reports, vol. 11, no. 1, p. 4706, 2021. DOI: https://doi.org/10.1038/s41598-021-83350-6

D. Ahmedt-Aristizabal et al., "Identification of children at risk of schizophrenia via deep learning and EEG responses," IEEE Journal of biomedical and health informatics, vol. 25, no. 1, pp. 69-76, 2020. DOI: https://doi.org/10.1109/JBHI.2020.2984238