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Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outs...

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Autores principales: Hussain, Iqram, Hossain, Md Azam, Jany, Rafsan, Bari, Md Abdul, Uddin, Musfik, Kamal, Abu Raihan Mostafa, Ku, Yunseo, Kim, Jik-Soo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028257/
https://www.ncbi.nlm.nih.gov/pubmed/35459064
http://dx.doi.org/10.3390/s22083079
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author Hussain, Iqram
Hossain, Md Azam
Jany, Rafsan
Bari, Md Abdul
Uddin, Musfik
Kamal, Abu Raihan Mostafa
Ku, Yunseo
Kim, Jik-Soo
author_facet Hussain, Iqram
Hossain, Md Azam
Jany, Rafsan
Bari, Md Abdul
Uddin, Musfik
Kamal, Abu Raihan Mostafa
Ku, Yunseo
Kim, Jik-Soo
author_sort Hussain, Iqram
collection PubMed
description Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.
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spelling pubmed-90282572022-04-23 Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages Hussain, Iqram Hossain, Md Azam Jany, Rafsan Bari, Md Abdul Uddin, Musfik Kamal, Abu Raihan Mostafa Ku, Yunseo Kim, Jik-Soo Sensors (Basel) Article Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system. MDPI 2022-04-17 /pmc/articles/PMC9028257/ /pubmed/35459064 http://dx.doi.org/10.3390/s22083079 Text en © 2022 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
Hussain, Iqram
Hossain, Md Azam
Jany, Rafsan
Bari, Md Abdul
Uddin, Musfik
Kamal, Abu Raihan Mostafa
Ku, Yunseo
Kim, Jik-Soo
Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
title Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
title_full Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
title_fullStr Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
title_full_unstemmed Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
title_short Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
title_sort quantitative evaluation of eeg-biomarkers for prediction of sleep stages
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028257/
https://www.ncbi.nlm.nih.gov/pubmed/35459064
http://dx.doi.org/10.3390/s22083079
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