EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis
Joint Authors
Zhang, Bingtao
Lei, Tao
Liu, Hong
Cai, Hanshu
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-04
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Bingtao& Lei, Tao& Liu, Hong& Cai, Hanshu. 2018. EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1132095
Modern Language Association (MLA)
Zhang, Bingtao…[et al.]. EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1132095
American Medical Association (AMA)
Zhang, Bingtao& Lei, Tao& Liu, Hong& Cai, Hanshu. EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1132095
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132095