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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| Vydáno v: | Journal of biological rhythms Ročník 40; číslo 4; s. 330 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
01.08.2025
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| ISSN: | 1552-4531, 1552-4531 |
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| Abstract | 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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| AbstractList | 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. 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 F1 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.Statement of Significance We present a fully open-source and user-friendly sleep-scoring application for the classification of sleep stages in rodents.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 F1 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.Statement of Significance We present a fully open-source and user-friendly sleep-scoring application for the classification of sleep stages in rodents. |
| Author | Kalume, Franck de la Iglesia, Horacio O Caldart, Carlos S Sanchez, Raymond E A Perez, Jazmine G Brunton, Bingni W Weil, Tenley A Beck, Asad I Ben-Hamo, Miriam |
| Author_xml | – sequence: 1 givenname: Asad I orcidid: 0000-0002-8491-0567 surname: Beck fullname: Beck, Asad I organization: Graduate Program in Neuroscience, University of Washington, Seattle, Washington – sequence: 2 givenname: Carlos S surname: Caldart fullname: Caldart, Carlos S organization: Department of Biology, University of Washington, Seattle, Washington – sequence: 3 givenname: Miriam surname: Ben-Hamo fullname: Ben-Hamo, Miriam organization: Department of Biology, University of Washington, Seattle, Washington – sequence: 4 givenname: Tenley A orcidid: 0000-0001-7253-6638 surname: Weil fullname: Weil, Tenley A organization: Department of Biology, University of Washington, Seattle, Washington – sequence: 5 givenname: Jazmine G surname: Perez fullname: Perez, Jazmine G organization: Department of Biology, University of Washington, Seattle, Washington – sequence: 6 givenname: Franck surname: Kalume fullname: Kalume, Franck organization: Department of Pharmacology, University of Washington, Seattle, Washington – sequence: 7 givenname: Bingni W surname: Brunton fullname: Brunton, Bingni W organization: Graduate Program in Neuroscience, University of Washington, Seattle, Washington – sequence: 8 givenname: Horacio O orcidid: 0000-0003-0855-6807 surname: de la Iglesia fullname: de la Iglesia, Horacio O organization: Graduate Program in Neuroscience, University of Washington, Seattle, Washington – sequence: 9 givenname: Raymond E A surname: Sanchez fullname: Sanchez, Raymond E A organization: Allen Institute for Brain Science, Seattle, Washington |
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| SubjectTerms | Algorithms Animals Circadian Rhythm Electrocorticography - methods Electromyography - methods Male Mice Mice, Inbred C57BL Rats Sleep - physiology Sleep Stages - physiology Wakefulness - physiology |
| Title | Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data |
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