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Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. / Merkulova, Ekaterina A.; Kozulin, Igor A.; Savostyanov, Alexandr N. et al.

24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1740-1745.

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

Harvard

Merkulova, EA, Kozulin, IA, Savostyanov, AN, Bocharov, AV & Privodnova, EY 2023, Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. in 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023. Institute of Electrical and Electronics Engineers (IEEE), pp. 1740-1745. https://doi.org/10.1109/edm58354.2023.10225096

APA

Merkulova, E. A., Kozulin, I. A., Savostyanov, A. N., Bocharov, A. V., & Privodnova, E. Y. (2023). Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. In 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023 (pp. 1740-1745). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/edm58354.2023.10225096

Vancouver

Merkulova EA, Kozulin IA, Savostyanov AN, Bocharov AV, Privodnova EY. Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. In 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023. Institute of Electrical and Electronics Engineers (IEEE). 2023. p. 1740-1745 doi: 10.1109/edm58354.2023.10225096

Author

Merkulova, Ekaterina A. ; Kozulin, Igor A. ; Savostyanov, Alexandr N. et al. / Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. pp. 1740-1745

BibTeX

@inproceedings{1b95aeadef514157bfa19bbca712cecd,
title = "Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder",
abstract = "Statistical method Principal Component Analysis (PCA) based on psychological questionnaires and EEG spectral characteristics was used to reduce the complex data by finding the most important variables to separate the healthy participants and participants with major depressive disorder from clinie of the State Researeh Institute of Neuroseienees and Medieine. The basic idea of PCA is to identify data patterns that are difficult to see otherwise. In this article it was used PCA for disease classification. We applied PCA to reduce the data dimensionality, for uncovering hidden patterns or relationships between the features. Finally, we trained the classification model on the reduced data to distinguish between healthy participants and participants with major depressive disorder. The study aimed to investigate the relationship between psychological factors and EEG spectral characteristics in participants with major depressive disorder. The study found that there was a significant correlation between the severity of depression and the EEG spectral characteristics. These findings suggest that the combination of psychological questionnaires and EEG spectral characteristics could be a useful tool for the diagnosis and classification of major depressive disorder.",
author = "Merkulova, {Ekaterina A.} and Kozulin, {Igor A.} and Savostyanov, {Alexandr N.} and Bocharov, {Andrey V.} and Privodnova, {Evgeniya Yu.}",
note = "The study was supported by the Foundation (RSF) No 22-25-00735. Публикация для корректировки.",
year = "2023",
doi = "10.1109/edm58354.2023.10225096",
language = "English",
isbn = "9798350336870",
pages = "1740--1745",
booktitle = "24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

RIS

TY - GEN

T1 - Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder

AU - Merkulova, Ekaterina A.

AU - Kozulin, Igor A.

AU - Savostyanov, Alexandr N.

AU - Bocharov, Andrey V.

AU - Privodnova, Evgeniya Yu.

N1 - The study was supported by the Foundation (RSF) No 22-25-00735. Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - Statistical method Principal Component Analysis (PCA) based on psychological questionnaires and EEG spectral characteristics was used to reduce the complex data by finding the most important variables to separate the healthy participants and participants with major depressive disorder from clinie of the State Researeh Institute of Neuroseienees and Medieine. The basic idea of PCA is to identify data patterns that are difficult to see otherwise. In this article it was used PCA for disease classification. We applied PCA to reduce the data dimensionality, for uncovering hidden patterns or relationships between the features. Finally, we trained the classification model on the reduced data to distinguish between healthy participants and participants with major depressive disorder. The study aimed to investigate the relationship between psychological factors and EEG spectral characteristics in participants with major depressive disorder. The study found that there was a significant correlation between the severity of depression and the EEG spectral characteristics. These findings suggest that the combination of psychological questionnaires and EEG spectral characteristics could be a useful tool for the diagnosis and classification of major depressive disorder.

AB - Statistical method Principal Component Analysis (PCA) based on psychological questionnaires and EEG spectral characteristics was used to reduce the complex data by finding the most important variables to separate the healthy participants and participants with major depressive disorder from clinie of the State Researeh Institute of Neuroseienees and Medieine. The basic idea of PCA is to identify data patterns that are difficult to see otherwise. In this article it was used PCA for disease classification. We applied PCA to reduce the data dimensionality, for uncovering hidden patterns or relationships between the features. Finally, we trained the classification model on the reduced data to distinguish between healthy participants and participants with major depressive disorder. The study aimed to investigate the relationship between psychological factors and EEG spectral characteristics in participants with major depressive disorder. The study found that there was a significant correlation between the severity of depression and the EEG spectral characteristics. These findings suggest that the combination of psychological questionnaires and EEG spectral characteristics could be a useful tool for the diagnosis and classification of major depressive disorder.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85171976361&origin=inward&txGid=226d273a5a1dded49d4bcddb5450ca4a

UR - https://www.mendeley.com/catalogue/62311260-acac-3956-a5ce-3a67a6648db1/

U2 - 10.1109/edm58354.2023.10225096

DO - 10.1109/edm58354.2023.10225096

M3 - Conference contribution

SN - 9798350336870

SP - 1740

EP - 1745

BT - 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

ID: 59175141