Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)

For citation:

Bogatenko T. R., Sergeev K. S., Strelkova G. I. Application of machine learning and statistics to anaesthesia detection from EEG data. Izvestiya of Saratov University. Physics , 2024, vol. 24, iss. 3, pp. 209-215. DOI: 10.18500/1817-3020-2024-24-3-209-215, EDN: HKYBMM

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
30.08.2024
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Language: 
English
Heading: 
Biophysics and medical physics
Article type: 
Article
UDC: 
577.35
DOI: 
10.18500/1817-3020-2024-24-3-209-215
EDN: 
HKYBMM

Application of machine learning and statistics to anaesthesia detection from EEG data

Autors: 
Bogatenko Tatiana Romanovna, Saratov State University
Sergeev Konstantin Sergeevich, Saratov State University
Strelkova Galina Ivanovna, Saratov State University
Abstract: 

Background and Objectives: The purpose of the research is to establish whether it is possible to determine the degree of anaesthesia that a laboratory animal is experiencing noninvasively. For this objective the usage of such methods of electroencephalogram (EEG) signal analysis as fast Fourier transform, K-Means machine learning method and statistical analysis is discussed. Models and Methods: The EEG data was obtained through an experiment where two groups of laboratory rats received different types of anaesthetic agent. The EEG data was normalised,then the power spectra were computed using fast Fouriertransform. Next, the K-Means method was applied to classify the data in accordance with the anaesthesia degree. Statistical analysis was also conducted to describe prominent characteristics of each stage. Results: It has been shown that the proposed data analysis methods allow to distinguish between normal state, anaesthesia, and death with increasing anaesthesia dosages in laboratory animals.

Key words: 
EEG signal
data analysis
statistical analysis
machine learning
Acknowledgments: 
The research was supported by IDEAS Research Centre scholarship (No. АСП-09-2021/I). The research was partially conducted within the Megagrant (project No. 075-15-2022 (075-15-2019-1885)).
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Received: 
17.05.2024
Accepted: 
15.06.2024
Published: 
30.08.2024
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