Chaos, Solitons and Fractals, volume 173, pages 113608
Simple method for detecting sleep episodes in rats ECoG using machine learning
Sergeev Konstantin S.
1
,
Runnova Anastasiya
1, 2, 3
,
Zhuravlev Maksim
1, 2, 3
,
Sitnikova Evgenija Yu
4
,
Rutskova Elizaveta
4
,
Smirnov Kirill S
4
,
Slepnev Andrei V
1
,
Publication type: Journal Article
Publication date: 2023-08-01
Journal:
Chaos, Solitons and Fractals
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 7.8
ISSN: 09600779
General Physics and Astronomy
Statistical and Nonlinear Physics
General Mathematics
Applied Mathematics
Abstract
In this paper we propose a new method for the automatic recognition of the state of behavioral sleep (BS) and waking state (WS) in freely moving rats using their electrocorticographic (ECoG) data. Three-channels ECoG signals were recorded from frontal left, frontal right and occipital right cortical areas. We employed a simple artificial neural network (ANN), in which the mean values and standard deviations of ECoG signals from two or three channels were used as inputs for the ANN. Results of wavelet-based recognition of BS/WS in the same data were used to train the ANN and evaluate correctness of our classifier. We tested different combinations of ECoG channels for detecting BS/WS. Our results showed that the accuracy of ANN classification did not depend on ECoG-channel. For any ECoG-channel, networks were trained on one rat and applied to another rat with an accuracy of at least 80~\%. Itis important that we used a very simple network topology to achieve a relatively high accuracy of classification. Our classifier was based on a simple linear combination of input signals with some weights, and these weights could be replaced by the averaged weights of all trained ANNs without decreases in classification accuracy. In all, we introduce a new sleep recognition method that does not require additional network training. It is enough to know the coefficients and the equations suggested in this paper. The proposed method showed very fast performance and simple computations, therefore it could be used in real time experiments. It might be of high demand in preclinical studies in rodents that require vigilance control or monitoring of sleep-wake patterns.
Citations by journals
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Biomolecules
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Biomolecules
1 publication, 25%
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BioSystems
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BioSystems
1 publication, 25%
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IEEE Access
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IEEE Access
1 publication, 25%
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Citations by publishers
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IEEE
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IEEE
2 publications, 50%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 25%
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Elsevier
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Elsevier
1 publication, 25%
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1
2
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Sergeev K. S. et al. Simple method for detecting sleep episodes in rats ECoG using machine learning // Chaos, Solitons and Fractals. 2023. Vol. 173. p. 113608.
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Sergeev K. S., Runnova A., Zhuravlev M., Sitnikova E. Yu., Rutskova E., Smirnov K. S., Slepnev A. V., Semenova N. Simple method for detecting sleep episodes in rats ECoG using machine learning // Chaos, Solitons and Fractals. 2023. Vol. 173. p. 113608.
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TY - JOUR
DO - 10.1016/j.chaos.2023.113608
UR - https://doi.org/10.1016%2Fj.chaos.2023.113608
TI - Simple method for detecting sleep episodes in rats ECoG using machine learning
T2 - Chaos, Solitons and Fractals
AU - Sergeev, Konstantin S.
AU - Runnova, Anastasiya
AU - Zhuravlev, Maksim
AU - Sitnikova, Evgenija Yu
AU - Rutskova, Elizaveta
AU - Smirnov, Kirill S
AU - Slepnev, Andrei V
AU - Semenova, Nadezhda
PY - 2023
DA - 2023/08/01 00:00:00
PB - Elsevier
SP - 113608
VL - 173
SN - 0960-0779
ER -
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@article{2023_Sergeev,
author = {Konstantin S. Sergeev and Anastasiya Runnova and Maksim Zhuravlev and Evgenija Yu Sitnikova and Elizaveta Rutskova and Kirill S Smirnov and Andrei V Slepnev and Nadezhda Semenova},
title = {Simple method for detecting sleep episodes in rats ECoG using machine learning},
journal = {Chaos, Solitons and Fractals},
year = {2023},
volume = {173},
publisher = {Elsevier},
month = {aug},
url = {https://doi.org/10.1016%2Fj.chaos.2023.113608},
pages = {113608},
doi = {10.1016/j.chaos.2023.113608}
}
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