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Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats

  • Basic Science • Original Article
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Abstract

Purpose

During the last decade, the reported prevalence of sleep-disordered breathing in adults has been rapidly increasing. Therefore, automatic methods of sleep assessment are of particular interest. In a framework of translational neuroscience, this study introduces a reliable automatic detection system of behavioral sleep in laboratory rats based on the signal recorded at the cortical surface without requiring electromyography.

Methods

Experimental data were obtained in 16 adult male WAG/Rij rats at the age of 9 months. Electrocorticographic signals (ECoG) were recorded in freely moving rats during the entire day (22.5 ± 2.2 h). Automatic wavelet-based assessment of behavioral sleep (BS) was proposed. The performance of this wavelet-based method was validated in a group of rats with genetic predisposition to absence epilepsy (n=16) based on visual analysis of their behavior in simultaneously recorded video.

Results

The accuracy of automatic sleep detection was 98% over a 24-h period. An automatic BS assessment method can be adjusted for detecting short arousals during sleep (microarousals) with various duration.

Conclusions

These findings suggest that automatic wavelet-based assessment of behavioral sleep can be used for assessment of sleep quality. Current analysis indicates a temporal relationship between microarousals, sleep, and epileptic discharges in genetically prone subjects.

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Funding

This work has been supported by the RF Government Grant No. 075-15-2019-1885 in part of the biological interpretation. In the part of the development of new method of data analysis this work has been supported by the Council for Grants of the President of the Russian Federation for the State Support of Young Russian Scientists (project no. MD-645.2020.9). Experimental data has been obtained and visually analyzed with the financial support of Russian Foundation for Basic Research (Grant No. 19-015-00242).

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Authors and Affiliations

  1. Saratov State Medical University, Saratov, Russia

    Anastasiya Runnova, Maksim Zhuravlev, Anton Kiselev & Anatoly Karavaev

  2. Saratov State University, Saratov, Russia

    Anastasiya Runnova, Maksim Zhuravlev, Anton Kiselev, Rodion Ukolov & Anatoly Karavaev

  3. National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia

    Anton Kiselev

  4. Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia

    Kirill Smirnov & Evgenia Sitnikova

  5. Saratov Branch of the Institute of RadioEngineering and Electronics of Russian Academy of Sciences, Moscow, Russia

    Anatoly Karavaev

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  1. Anastasiya Runnova
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Correspondence to Anton Kiselev.

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Runnova, A., Zhuravlev, M., Kiselev, A. et al. Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats. Sleep Breath 25, 2251–2258 (2021). https://doi.org/10.1007/s11325-021-02357-5

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  • DOI: https://doi.org/10.1007/s11325-021-02357-5

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