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EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-pro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142786/ https://www.ncbi.nlm.nih.gov/pubmed/30254690 http://dx.doi.org/10.1155/2018/6534041 |
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author | Zhang, Bingtao Lei, Tao Liu, Hong Cai, Hanshu |
author_facet | Zhang, Bingtao Lei, Tao Liu, Hong Cai, Hanshu |
author_sort | Zhang, Bingtao |
collection | PubMed |
description | Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments. |
format | Online Article Text |
id | pubmed-6142786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-61427862018-09-25 EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis Zhang, Bingtao Lei, Tao Liu, Hong Cai, Hanshu Comput Math Methods Med Research Article Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments. Hindawi 2018-09-04 /pmc/articles/PMC6142786/ /pubmed/30254690 http://dx.doi.org/10.1155/2018/6534041 Text en Copyright © 2018 Bingtao Zhang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Bingtao Lei, Tao Liu, Hong Cai, Hanshu EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis |
title | EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis |
title_full | EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis |
title_fullStr | EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis |
title_full_unstemmed | EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis |
title_short | EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis |
title_sort | eeg-based automatic sleep staging using ontology and weighting feature analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142786/ https://www.ncbi.nlm.nih.gov/pubmed/30254690 http://dx.doi.org/10.1155/2018/6534041 |
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