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On selecting features for classifying sleep-wake cycles of rodents by means of a bioradar


L.N. Anishchenko - Ph.D. (Eng.), Senior Research Scientist, Research Section of Scientific and Educational Complex «Basic Sciences», Bauman Moscow State Technical University
E.M. Rutskova - Ph.D. (Biol.), IHNA&NPh RAS (Moscow)

At present sleep-wake cycle studies in laboratory animals are based on electroencephalogram and electrimiorgam signals analysis, which are registered by implanted electrodes. The main drawback of such method is the necessity to implant them, which is time and labor consuming task involving ethical issues. That is why development of new noncontact methods for remote monitoring of laboratory animals vital signs and corresponding algorithms for sleep-wake cycle stages classification are up-to-date tasks. Such methods would not have an impact on the animal, unlike the implanted ones, which would allow increasing the informative value of the registered data.
In this paper we propose to use the method of bioradiolocation for remote monitoring of laboratory animals sleep-wake cycle during prolonged period (e.g. for several days). The algorithm for bioradar signal pre-processing is described, and list features for each epoch classification is proposed. Analysis of feature vector with the help of sequential forward selection algorithm allows to reduce the used features number from 24 to 10 while maintaining sleep-stages classification quality at the same level with Kohen's kappa of 55 % and accuracy of 77 %. As a ground truth we used classified data of a standard biology method based on analysis of signals registered by means of implanted EEG and EMG. Classifier performance was estimated using data gathered during sleep experiments conducted in a laboratory of Neuroontogenesis of Institute of Higher Nervous Activity and Neurophysiology of RAS.

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June 24, 2020
May 29, 2020

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