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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of biological rhythms Jg. 40; H. 4; S. 330
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	United States 01.08.2025
Schlagworte:	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-Zugang:	Weitere Angaben
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!