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Detection of different states of sleep in the rodents by the means of artificial neural networks
Author(s): Viacheslav Musatov; Viacheslav Dykin; Elena Pitsik; Alexander Pisarchik
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Paper Abstract

This paper considers the possibility of classification of electroencephalogram (EEG) and electromyogram (EMG) signals corresponding to different phases of sleep and wakefulness of mice by the means of artificial neural networks. A feed-forward artificial neural network based on multilayer perceptron was created and trained on the data of one of the rodents. The trained network was used to read and classify the EEG and EMG data corresponding to different phases of sleep and wakefulness of the same mouse and other mouse. The results show a good recognition quality of all phases for the rodent on which the training was conducted (80–99%) and acceptable recognition quality for the data collected from the same mouse after a stroke.

Paper Details

Date Published: 26 April 2018
PDF: 5 pages
Proc. SPIE 10717, Saratov Fall Meeting 2017: Laser Physics and Photonics XVIII; and Computational Biophysics and Analysis of Biomedical Data IV, 107171N (26 April 2018); doi: 10.1117/12.2314957
Show Author Affiliations
Viacheslav Musatov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Viacheslav Dykin, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Elena Pitsik, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Alexander Pisarchik, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Univ. Politecnica de Madrid (Spain)


Published in SPIE Proceedings Vol. 10717:
Saratov Fall Meeting 2017: Laser Physics and Photonics XVIII; and Computational Biophysics and Analysis of Biomedical Data IV
Vladimir L. Derbov; Dmitry Engelevich Postnov, Editor(s)

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