Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

STUDY OBJECTIVES: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. METHODS: Using a clinical dataset of polysomnograms from 6,431 patients (MGH–PSG dataset), we trained a deep neural network to classify sleep...

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Dettagli Bibliografici
Pubblicato in:Sleep
Autori principali: Abou Jaoude, Maurice, Sun, Haoqi, Pellerin, Kyle R, Pavlova, Milena, Sarkis, Rani A, Cash, Sydney S, Westover, M Brandon, Lam, Alice D
Natura: Artigo
Lingua:Inglês
Pubblicazione: Oxford University Press 2020
Soggetti:
Big Data Approaches to Sleep and Circadian Science
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC7686563/
https://ncbi.nlm.nih.gov/pubmed/32478820
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/sleep/zsaa112
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