Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data

Accurately capturing the temporal distribution of polysomnographic sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manua...

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Bibliographic Details
Published in:Journal of biological rhythms Vol. 40; no. 4; p. 330
Main Authors: Beck, Asad I, Caldart, Carlos S, Ben-Hamo, Miriam, Weil, Tenley A, Perez, Jazmine G, Kalume, Franck, Brunton, Bingni W, de la Iglesia, Horacio O, Sanchez, Raymond E A
Format: Journal Article
Language:English
Published: United States 01.08.2025
Subjects:
Algorithms
Animals
Circadian Rhythm
Electrocorticography - methods
Electromyography - methods
Male
Mice
Mice, Inbred C57BL
Rats
Sleep - physiology
Sleep Stages - physiology
Wakefulness - physiology
sleep scoring
automated
web application
machine learning
rodent model
ISSN:1552-4531, 1552-4531
Online Access:Get more information
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Summary:Accurately capturing the temporal distribution of polysomnographic sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually by categorizing 5- to 10-sec epochs as 1 of 3 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 h, which are critical for the study of the circadian regulation of sleep. 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 3 main behavioral stages in mice. We used a supervised machine learning algorithm that extracts features from the ECoG and EMG signals and autonomously scores recordings with a hierarchical classifier based on using logistic regression. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from mutant mouse lines with abnormal sleep phenotypes and from rats. We obtained mean F scores 0.94 for wakefulness, 0.94 for NREM, and 0.74 for REM, and followed up by validating SIESTA with manually scored data from 3 other laboratories. 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. Our aim is that SIESTA becomes a useful tool that facilitates further research in sleep on rodent models.
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ISSN:1552-4531
1552-4531
DOI:10.1177/07487304251336649