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Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data

Carlos S. Caldart, Raymond E. A. Sanchez, Miriam Ben-Hamo, Asad I. Beck, Tenley A. Weil, Jazmine G. Perez, Franck Kalume, Bingni W. Brunton, Horacio O. de la Iglesia
doi: https://doi.org/10.1101/2020.07.06.186940
Carlos S. Caldart
1Department of Biology, University of Washington, Seattle, WA
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Raymond E. A. Sanchez
1Department of Biology, University of Washington, Seattle, WA
2Graduate Program in Neuroscience, University of Washington, Seattle, WA
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Miriam Ben-Hamo
1Department of Biology, University of Washington, Seattle, WA
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Asad I. Beck
1Department of Biology, University of Washington, Seattle, WA
2Graduate Program in Neuroscience, University of Washington, Seattle, WA
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Tenley A. Weil
1Department of Biology, University of Washington, Seattle, WA
3National Institute of Mental Health, Bethesda, MD
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Jazmine G. Perez
1Department of Biology, University of Washington, Seattle, WA
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Franck Kalume
2Graduate Program in Neuroscience, University of Washington, Seattle, WA
4Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA
5Department of Neurological Surgery, University of Washington, Seattle WA
6Department of Pharmacology, University of Washington, Seattle WA
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Bingni W. Brunton
1Department of Biology, University of Washington, Seattle, WA
2Graduate Program in Neuroscience, University of Washington, Seattle, WA
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Horacio O. de la Iglesia
1Department of Biology, University of Washington, Seattle, WA
2Graduate Program in Neuroscience, University of Washington, Seattle, WA
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  • For correspondence: horaciod@uw.edu
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Abstract

The temporal distribution of sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. This temporal distribution is typically determined polysomnographically. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually, where 5-10 second epochs are categorized as one of three specific stages: wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 hours.

To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these three main behavioral stages in mice. Our supervised machine learning algorithm extracts features from the ECoG and EMG signals, then automatically scores recordings with a hierarchical classifier based on Bagging Random Forest approaches. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from a mutant mouse line with abnormal sleep phenotypes. To validate its performance on test data, we compared SIESTA with manually scored data and obtained F1 scores of 0.92 for wakefulness, 0.81 for REM, and 0.93 for NREM.

SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all open-source and freely available resources, and our aim is that SIESTA becomes a useful tool that facilitates further research of sleep in rodent models.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 06, 2020.
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Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data
Carlos S. Caldart, Raymond E. A. Sanchez, Miriam Ben-Hamo, Asad I. Beck, Tenley A. Weil, Jazmine G. Perez, Franck Kalume, Bingni W. Brunton, Horacio O. de la Iglesia
bioRxiv 2020.07.06.186940; doi: https://doi.org/10.1101/2020.07.06.186940
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Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data
Carlos S. Caldart, Raymond E. A. Sanchez, Miriam Ben-Hamo, Asad I. Beck, Tenley A. Weil, Jazmine G. Perez, Franck Kalume, Bingni W. Brunton, Horacio O. de la Iglesia
bioRxiv 2020.07.06.186940; doi: https://doi.org/10.1101/2020.07.06.186940

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