{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:06:05Z","timestamp":1777734365866,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sleep disturbances are common in Alzheimer\u2019s 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\u2019s home environment. However, na\u00efve 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% (\u00b110%) validation accuracy on low-quality two-channel EEG headband data and 77% (\u00b110%) 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.<\/jats:p>","DOI":"10.3390\/s21103316","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T11:30:16Z","timestamp":1620732616000},"page":"3316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6014-3626","authenticated-orcid":false,"given":"Amelia A.","family":"Casciola","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Sebastiano K.","family":"Carlucci","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Brianne A.","family":"Kent","sequence":"additional","affiliation":[{"name":"Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada"},{"name":"Department of Psychology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada"}]},{"given":"Amanda M.","family":"Punch","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Michael A.","family":"Muszynski","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Daniel","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8944-1459","authenticated-orcid":false,"given":"Alireza","family":"Kazemi","sequence":"additional","affiliation":[{"name":"Center for Mind and Brain, Department of Psychology, University of California, Davis, CA 95618, USA"}]},{"given":"Maryam S.","family":"Mirian","sequence":"additional","affiliation":[{"name":"Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada"}]},{"given":"Jason","family":"Valerio","sequence":"additional","affiliation":[{"name":"Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-0817","authenticated-orcid":false,"given":"Martin J.","family":"McKeown","sequence":"additional","affiliation":[{"name":"Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada"}]},{"given":"Haakon B.","family":"Nygaard","sequence":"additional","affiliation":[{"name":"Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101902","DOI":"10.1016\/j.pneurobio.2020.101902","article-title":"Sleep and Its Regulation: An Emerging Pathogenic and Treatment Frontier in Alzheimer\u2019s Disease","volume":"197","author":"Kent","year":"2021","journal-title":"Prog. 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