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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.

2023. 1740-1745 Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM).

Research output: Contribution to conference › Paper › 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', Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM), 29.06.2023 - 03.07.2023 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. 1740-1745. Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM). 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. 2023. Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM). 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. Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM).6 p.

BibTeX

@conference{8c247a967fad4e89b1311a724e9120a0,
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.",
keywords = "PCA, electroencephalogram, hypothesis testing, machine learning, processing of experimental data",
author = "Merkulova, {Ekaterina a.} and Kozulin, {Igor a.} and Savostyanov, {Alexandr n.} and Bocharov, {Andrey v.} and Privodnova, {Evgeniya yu.}",
year = "2023",
month = jun,
day = "29",
doi = "10.1109/EDM58354.2023.10225096",
language = "English",
pages = "1740--1745",
note = "2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM) ; Conference date: 29-06-2023 Through 03-07-2023",

}

RIS

TY - CONF

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.

PY - 2023/6/29

Y1 - 2023/6/29

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.

KW - PCA

KW - electroencephalogram

KW - hypothesis testing

KW - machine learning

KW - processing of experimental data

UR - https://www.mendeley.com/catalogue/4708804c-ff9c-33d5-94ae-805a0621adbc/

U2 - 10.1109/EDM58354.2023.10225096

DO - 10.1109/EDM58354.2023.10225096

M3 - Paper

SP - 1740

EP - 1745

T2 - 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM)

Y2 - 29 June 2023 through 3 July 2023

ER -

ID: 61403819