Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Using Machine Learning Methods to Search for EEG and Genetic Markers of Depressive Disorder. / Zorina, Kseniya; Kriveckiy, Andrey; Klemeshova, Darya и др.
International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2025. стр. 1790-1793 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Using Machine Learning Methods to Search for EEG and Genetic Markers of Depressive Disorder
AU - Zorina, Kseniya
AU - Kriveckiy, Andrey
AU - Klemeshova, Darya
AU - Bocharov, Andrey
AU - Karmanov, Vitaliy
N1 - Conference code: 26
PY - 2025/8/8
Y1 - 2025/8/8
N2 - Depression is one of the most common mental disorders. Therefore, the development of new methods for early diagnosis of depression is a highly relevant task. It is well known that depression has both a genetic predisposition and greatly depends on the patient's life background. Therefore, the analysis of only genetic markers of depression is usually unsuccessful, because it does not take into account the physiological state of a person at the time of examination. The aim of our study was to develop an algorithm for the joint analysis of a collection of genetic and neurophysiological data collected from healthy people and patients with depression to identify genetic markers and neurophysiological correlates of pathology. As EEG indicators, we considered the amplitudes of evoked potentials under the conditions of performing tasks in the stop-signal paradigm. We applied machine learning algorithms that allow us to identify single nucleotide polymorphisms associated with the risk of depression.
AB - Depression is one of the most common mental disorders. Therefore, the development of new methods for early diagnosis of depression is a highly relevant task. It is well known that depression has both a genetic predisposition and greatly depends on the patient's life background. Therefore, the analysis of only genetic markers of depression is usually unsuccessful, because it does not take into account the physiological state of a person at the time of examination. The aim of our study was to develop an algorithm for the joint analysis of a collection of genetic and neurophysiological data collected from healthy people and patients with depression to identify genetic markers and neurophysiological correlates of pathology. As EEG indicators, we considered the amplitudes of evoked potentials under the conditions of performing tasks in the stop-signal paradigm. We applied machine learning algorithms that allow us to identify single nucleotide polymorphisms associated with the risk of depression.
KW - EEG
KW - analysis of variance (ANOVA)
KW - depressive disorder
KW - event-related potentials (ERPs)
KW - genetic markers
KW - machine learning
KW - singlenucleotide polymorphisms (SNPs)
KW - stopsignal paradigm (SSP)
UR - https://www.scopus.com/pages/publications/105014194893
UR - https://www.mendeley.com/catalogue/8ad6c28d-0cad-3333-a69b-b4fd11d9b171/
U2 - 10.1109/EDM65517.2025.11096763
DO - 10.1109/EDM65517.2025.11096763
M3 - Conference contribution
SN - 9781665477376
T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
SP - 1790
EP - 1793
BT - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
PB - IEEE Computer Society
T2 - 2025 IEEE 26th International Conference of Young Professionals in Electron Devices and Materials (EDM)
Y2 - 27 June 2025 through 1 July 2025
ER -
ID: 68949560