Cargando…

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccu...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Shanguang, Long, Fangfang, Wei, Xin, Ni, Xiaoli, Wang, Hui, Wei, Bokun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8910622/
https://www.ncbi.nlm.nih.gov/pubmed/35270548
http://dx.doi.org/10.3390/ijerph19052845
_version_ 1784666537732341760
author Zhao, Shanguang
Long, Fangfang
Wei, Xin
Ni, Xiaoli
Wang, Hui
Wei, Bokun
author_facet Zhao, Shanguang
Long, Fangfang
Wei, Xin
Ni, Xiaoli
Wang, Hui
Wei, Bokun
author_sort Zhao, Shanguang
collection PubMed
description Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.
format Online
Article
Text
id pubmed-8910622
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-89106222022-03-11 Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm Zhao, Shanguang Long, Fangfang Wei, Xin Ni, Xiaoli Wang, Hui Wei, Bokun Int J Environ Res Public Health Article Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring. MDPI 2022-03-01 /pmc/articles/PMC8910622/ /pubmed/35270548 http://dx.doi.org/10.3390/ijerph19052845 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
Zhao, Shanguang
Long, Fangfang
Wei, Xin
Ni, Xiaoli
Wang, Hui
Wei, Bokun
Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm
title Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm
title_full Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm
title_fullStr Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm
title_full_unstemmed Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm
title_short Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm
title_sort evaluation of a single-channel eeg-based sleep staging algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8910622/
https://www.ncbi.nlm.nih.gov/pubmed/35270548
http://dx.doi.org/10.3390/ijerph19052845
work_keys_str_mv AT zhaoshanguang evaluationofasinglechanneleegbasedsleepstagingalgorithm
AT longfangfang evaluationofasinglechanneleegbasedsleepstagingalgorithm
AT weixin evaluationofasinglechanneleegbasedsleepstagingalgorithm
AT nixiaoli evaluationofasinglechanneleegbasedsleepstagingalgorithm
AT wanghui evaluationofasinglechanneleegbasedsleepstagingalgorithm
AT weibokun evaluationofasinglechanneleegbasedsleepstagingalgorithm