학술논문

Information Analysis of Resting-state EEG and Genetic Polymorphism of People for Searching of Markers of Mental Pathology
Document Type
Conference
Source
2022 IEEE 23rd International Conference of Young Professionals in Electron Devices and Materials (EDM) Young Professionals in Electron Devices and Materials (EDM), 2022 IEEE 23rd International Conference of. :322-325 Jun, 2022
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Pathology
Sequential analysis
Psychology
Genomics
Pressing
Electroencephalography
Environmental factors
EEG
depression
polymorphism 5-HTTLPR
migrants
anxiety
personal characteristics
Language
ISSN
2325-419X
Abstract
Medical informatics is a scientific and applied field aimed at using computer technologies for the diagnosis and treatment of diseases. A promising area of medical informatics is the search for disease markers in healthy people. If successful, such a direction in the future can predict and prevent diseases before they begin to develop. EEG rhythms reflect individual features of people, the variability of functional states under the influence of the external environment at the time of examination, and they are also largely determined genetically. We focus on depressive disorder due to comorbidity of the disease, late diagnosis and complexity of targeted treatment. The relationship of depressive EEG markers to molecular genetic and environmental factors that increase or reduce the risk of developing this pathology is still a pressing issue. Computer processing of EEG data together with genome sequencing data may reveal individual features of healthy people indicating an increased risk of developing mental pathologies. In the future, the application of this approach can significantly expand the prognostic capabilities of medical informatics.