A Comparison Study on Multidomain EEG Features for Sleep Stage Classification
Joint Authors
Zhang, Yu
Wang, Bei
Jing, Jin
Zhang, Jian
Zou, Junzhong
Nakamura, Masatoshi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-05
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging.
In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis.
Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure.
The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis.
The numbers of the characteristic activities were extracted as the features from time domain.
The contributions of features from different domains to the sleep stages were compared.
The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection.
The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested.
The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection.
Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.
American Psychological Association (APA)
Zhang, Yu& Wang, Bei& Jing, Jin& Zhang, Jian& Zou, Junzhong& Nakamura, Masatoshi. 2017. A Comparison Study on Multidomain EEG Features for Sleep Stage Classification. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1140962
Modern Language Association (MLA)
Zhang, Yu…[et al.]. A Comparison Study on Multidomain EEG Features for Sleep Stage Classification. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1140962
American Medical Association (AMA)
Zhang, Yu& Wang, Bei& Jing, Jin& Zhang, Jian& Zou, Junzhong& Nakamura, Masatoshi. A Comparison Study on Multidomain EEG Features for Sleep Stage Classification. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1140962
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140962