Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials. / Шанарова, Надежда Леонидовна; Пронина, Марина Владимировна; Липкович, Михаил; Пономарев, Валерий; Muller, Andreas; Кропотов, Юрий Дмитриевич.
в: Diagnostics, Том 13, № 3, 30.01.2023.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials
AU - Шанарова, Надежда Леонидовна
AU - Пронина, Марина Владимировна
AU - Липкович, Михаил
AU - Пономарев, Валерий
AU - Muller, Andreas
AU - Кропотов, Юрий Дмитриевич
PY - 2023/1/30
Y1 - 2023/1/30
N2 - Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.
AB - Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.
KW - electroencephalogram; event-related potential; schizophrenia; machine learning models; support vector machine
KW - electroencephalogram
KW - event-related potential
KW - machine learning models
KW - schizophrenia
KW - support vector machine
UR - https://www.mendeley.com/catalogue/6af1acc8-caeb-3bb1-9534-739e5e35c870/
U2 - https://doi.org/10.3390/diagnostics13030509
DO - https://doi.org/10.3390/diagnostics13030509
M3 - Article
C2 - 36766614
VL - 13
JO - Diagnostics
JF - Diagnostics
SN - 2075-4418
IS - 3
ER -
ID: 107088981