TY - GEN

T1 - Machine learning based diagnostics of schizophrenia patients

AU - Shanarova, Nadezhda

AU - Pronina, Marina

AU - Lipkovich, Mikhail

AU - Kropotov, Juri

N1 - Publisher Copyright: © 2022 IEEE.

PY - 2022/10/21

Y1 - 2022/10/21

N2 - Schizophrenia is a major psychiatric disorder which significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. Because of that, reliable diagnosis of schizophrenia is of big interest. In this paper, machine learning based diagnostics of schizophrenia is designed. Classification models are applied to event-related potentials (ERPs) calculated from electroencephalo-gram (EEG) records of patients and healthy subjects performing modification of the visual cued Go-NoGo task. The sample consisted of 200 adult individuals, with an age ranging between 18 and 50 years. In order to apply machine learning models various features are extracted from ERPs. Process of feature extraction is parametrized through a special procedure and parameters of this procedure are selected through a grid-search technique along with model hyperparameters. Feature extraction is followed by Sequential Feature Selection transformation in order to prevent overtitting and reduce computational complexity. Support vector machines and Random Forest models are trained on the resulting feature set. Sensitivity and specificity of the best model are 91% and 91.7% respectively.

AB - Schizophrenia is a major psychiatric disorder which significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. Because of that, reliable diagnosis of schizophrenia is of big interest. In this paper, machine learning based diagnostics of schizophrenia is designed. Classification models are applied to event-related potentials (ERPs) calculated from electroencephalo-gram (EEG) records of patients and healthy subjects performing modification of the visual cued Go-NoGo task. The sample consisted of 200 adult individuals, with an age ranging between 18 and 50 years. In order to apply machine learning models various features are extracted from ERPs. Process of feature extraction is parametrized through a special procedure and parameters of this procedure are selected through a grid-search technique along with model hyperparameters. Feature extraction is followed by Sequential Feature Selection transformation in order to prevent overtitting and reduce computational complexity. Support vector machines and Random Forest models are trained on the resulting feature set. Sensitivity and specificity of the best model are 91% and 91.7% respectively.

KW - Electroencephalogram

KW - event-related potential

KW - random forest

KW - schizophrenia

KW - support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85141614005&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/79f621c1-00a4-3207-ae66-36fc2b8496f6/

U2 - 10.1109/dcna56428.2022.9923292

DO - 10.1109/dcna56428.2022.9923292

M3 - Conference contribution

AN - SCOPUS:85141614005

SN - 9781665461740

T3 - Proceedings - 6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

SP - 252

EP - 255

BT - Proceedings - 6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

Y2 - 14 September 2022 through 16 September 2022

ER -