A Study on Automatic Sleep Staging Algorithm Based on Improved SVM
Authors: 
Song Zhang, Bin Hu, Xiangwei ZhengAuthors Info & Claims
Pages 225 - 228
Abstract
With the progress of science and technology and social development, public health becomes the focus of social concern. The study of sleep staging is an inevitable trend in the development of medical technology and the results can be used as an effective means of adjuvant therapy for some diseases such as insomnia, epilepsy, anxiety and so on. After analyzing least squares support vector machine (LSSVM) and decision tree, this paper proposes an improved SVM based on tree structure (DLSVM), which is applied to automatic sleep staging. After preprocessing the main components of the EEG signal waves at various stages of sleep, DLSVM applies different feature subsets to automatically classify the sleep stage. The simulation experiments show that DLSVM can reach 86.47% overall accuracy of sleep staging and is superior to other similar related algorithm.
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- A Study on Automatic Sleep Staging Algorithm Based on Improved SVM
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Information
Published In
September 2017
269 pages
ISBN:9781450353526
DOI:10.1145/3127404
- General Chairs:
- Zili Zhang,
- Ning Gu,
- Program Chairs:
- Shaozi Li,
- Tun Lu,
- Li Li
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 September 2017
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- Research
- Refereed limited
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Conference
ChineseCSCW '17
ChineseCSCW '17: Chinese Conference on Computer Supported Cooperative Work and Social Computing
September 22 - 23, 2017
Chongqing, China
Acceptance Rates
ChineseCSCW '17 Paper Acceptance Rate 21 of 84 submissions, 25%;
Overall Acceptance Rate 21 of 84 submissions, 25%
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