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

1
Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
3
Department of Psychology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
4
Center for Mind and Brain, Department of Psychology, University of California, Davis, CA 95618, USA
*
Authors to whom correspondence should be addressed.
Equal contribution.
Academic Editor: Jinseok Lee
Sensors 2021, 21(10), 3316; https://doi.org/10.3390/s21103316
Received: 15 April 2021 / Revised: 4 May 2021 / Accepted: 6 May 2021 / Published: 11 May 2021
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 formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person’s home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders. View Full-Text
Keywords: deep learning; EEG headband; sleep staging; machine learning; neurodegenerative disease; sleep deep learning; EEG headband; sleep staging; machine learning; neurodegenerative disease; sleep
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MDPI and ACS Style

Casciola, A.A.; Carlucci, S.K.; Kent, B.A.; Punch, A.M.; Muszynski, M.A.; Zhou, D.; Kazemi, A.; Mirian, M.S.; Valerio, J.; McKeown, M.J.; Nygaard, H.B. A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data. Sensors 2021, 21, 3316. https://doi.org/10.3390/s21103316

AMA Style

Casciola AA, Carlucci SK, Kent BA, Punch AM, Muszynski MA, Zhou D, Kazemi A, Mirian MS, Valerio J, McKeown MJ, Nygaard HB. A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data. Sensors. 2021; 21(10):3316. https://doi.org/10.3390/s21103316

Chicago/Turabian Style

Casciola, Amelia A., Sebastiano K. Carlucci, Brianne A. Kent, Amanda M. Punch, Michael A. Muszynski, Daniel Zhou, Alireza Kazemi, Maryam S. Mirian, Jason Valerio, Martin J. McKeown, and Haakon B. Nygaard 2021. "A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data" Sensors 21, no. 10: 3316. https://doi.org/10.3390/s21103316

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