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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Vydané v:Journal of biological rhythms Ročník 40; číslo 4; s. 330
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.08.2025
Predmet:
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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1552-4531
1552-4531
DOI:10.1177/07487304251336649