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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151443/ https://www.ncbi.nlm.nih.gov/pubmed/34064694 http://dx.doi.org/10.3390/s21103316 |
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author | Casciola, Amelia A. Carlucci, Sebastiano K. Kent, Brianne A. Punch, Amanda M. Muszynski, Michael A. Zhou, Daniel Kazemi, Alireza Mirian, Maryam S. Valerio, Jason McKeown, Martin J. Nygaard, Haakon B. |
author_facet | Casciola, Amelia A. Carlucci, Sebastiano K. Kent, Brianne A. Punch, Amanda M. Muszynski, Michael A. Zhou, Daniel Kazemi, Alireza Mirian, Maryam S. Valerio, Jason McKeown, Martin J. Nygaard, Haakon B. |
author_sort | Casciola, Amelia A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8151443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81514432021-05-27 A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data Casciola, Amelia A. Carlucci, Sebastiano K. Kent, Brianne A. Punch, Amanda M. Muszynski, Michael A. Zhou, Daniel Kazemi, Alireza Mirian, Maryam S. Valerio, Jason McKeown, Martin J. Nygaard, Haakon B. Sensors (Basel) Article 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. MDPI 2021-05-11 /pmc/articles/PMC8151443/ /pubmed/34064694 http://dx.doi.org/10.3390/s21103316 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Casciola, Amelia A. Carlucci, Sebastiano K. Kent, Brianne A. Punch, Amanda M. Muszynski, Michael A. Zhou, Daniel Kazemi, Alireza Mirian, Maryam S. Valerio, Jason McKeown, Martin J. Nygaard, Haakon B. A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data |
title | A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data |
title_full | A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data |
title_fullStr | A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data |
title_full_unstemmed | A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data |
title_short | A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data |
title_sort | deep learning strategy for automatic sleep staging based on two-channel eeg headband data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151443/ https://www.ncbi.nlm.nih.gov/pubmed/34064694 http://dx.doi.org/10.3390/s21103316 |
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