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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
An Accurate Sleep Stages Classification Method Based on State Space Model
IEEE access, v 7, pp 125268-125279
2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The classification of sleep stages is the process which helps to evaluate the quality of sleep and detect the sleep related disorders. Through analyzing the electroencephalography, the sleep stages can be discriminated manually by specialists. However, this can be a laboriousness work because of the huge datasets. Until now, several studies have been conducted based on the automatic analysis of electroencephalography. Still, as the development of wearable technology, there is a need for an accurate and single-channel electroencephalography based sleep stages identification system. In this paper, a state-space based sleep stages classification method is proposed using the proposed model based essence features extraction method. This method employed the state-space model to establish the intrinsic models based on the single-channel electroencephalography, from which the features used for further classification can be extracted. For 2-stage to 6-stage classification of sleep states, the verification system can achieve 98.6%, 94.9%, 93.0%, 92.3%, 91.8% accuracy on the Sleep-EDF database, and also reach 94.9%, 87.7%, 82.7%, 80.9%, 78.2% on Dreams Subjects database.
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Details
- Title
- An Accurate Sleep Stages Classification Method Based on State Space Model
- Creators
- Huaming Shen - Shanghai UniversityMeihua Xu - Shanghai UniversityAllon Guez - Drexel UniversityAng Li - Shanghai UniversityFeng Ran - Shanghai University
- Publication Details
- IEEE access, v 7, pp 125268-125279
- Publisher
- IEEE
- Grant note
- 61674100 / National Natural Science Foundation of China (10.13039/501100001809)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Urban Health Collaborative
- Web of Science ID
- WOS:000487838400002
- Scopus ID
- 2-s2.0-85077955194
- Other Identifier
- 991019168056704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications