A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data

Sleep disturbances are common in Alzheimer’s disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve f...

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Bibliographic Details
Main Authors: Amelia A. Casciola, Sebastiano K. Carlucci, Brianne A. Kent, Amanda M. Punch, Michael A. Muszynski, Daniel Zhou, Alireza Kazemi, Maryam S. Mirian, Jason Valerio, Martin J. McKeown, Haakon B. Nygaard
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
deep learning
EEG headband
sleep staging
machine learning
neurodegenerative disease
sleep
Online Access:https://www.mdpi.com/1424-8220/21/10/3316

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