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| ■論文No. |
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| ■ページ数 |
8ページ |
■発行日
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2019/07/01 |
| ■タイトル |
A New Investigation of Automatic Sleep Stage Detection using Decision-Tree-Based Support Vector Machine and Spectral Features Extraction of ECG Signal |
| ■タイトル(英語) |
A New Investigation of Automatic Sleep Stage Detection using Decision-Tree-Based Support Vector Machine and Spectral Features Extraction of ECG Signal |
| ■著者名 |
Edita Rosana Widasari (Interdisciplinary Graduate School of Agriculture and Engineering, Dept. of Material and Informatics, University of Miyazaki), Koichi Tanno (University of Miyazaki), Hiroki Tamura (University of Miyazaki) |
| ■著者名(英語) |
Edita Rosana Widasari (Interdisciplinary Graduate School of Agriculture and Engineering, Dept. of Material and Informatics, University of Miyazaki), Koichi Tanno (University of Miyazaki), Hiroki Tamura (University of Miyazaki) |
| ■価格 |
会員 ¥550 一般 ¥770 |
| ■書籍種類 |
論文誌(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 |
| ■本誌 |
電気学会論文誌C（電子・情報・システム部門誌） Vol.139 No.7 （2019） 特集：平成30年電子・情報・システム部門大会
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| ■本誌掲載ページ |
820-827ページ |
| ■原稿種別 |
論文／英語 |
| ■電子版へのリンク |
https://www.jstage.jst.go.jp/article/ieejeiss/139/7/139_820/_article/-char/ja/
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| ■キーワード |
ECG，sleep stage，normalized LF band power，normalized HF band power，machine learning classifiers |
| ■要約(日本語) |
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| ■要約(英語) |
This research is proposed to investigate an easy, fast, and effective method for automatic sleep stage detection using spectral features extraction from electrocardiography (ECG) signal alone. Sleep stage detection is the gold standard for sleep analysis. A sleep physician may suspect a treatment and diagnosis of sleep diseases through sleep stage detection. Polysomnography (PSG) method that generally used for detecting sleep stage. This method is intrusive and difficult to be implemented for in-home and portable systems. Moreover, it is involved a lot of effort, time and cost. The automatic sleep stage detection that uses only ECG signal is expected can solve these problems. The proposed method was tested and evaluated on 51 subjects that consist of 16 healthy subjects, 9 patients with insomnia, 4 patients with sleep-disordered breathing, and 22 patients with REM behavior disorder. In this research also tried to apply a minimal feature for automatic sleep stage detection. The two features applied were the normalized Low Frequency, LF (n.u.) band power and normalized High Frequency, HF (n.u.) band power that obtained from spectral features extraction. These features were then used as inputs for sleep stage classification. Mostly commonly used learning classifiers is implemented to classify sleep stage, namely KNN, NN, DT, SVM, and proposed DTB-SVM. The proposed method using DTB-SVM and spectral features extraction of ECG achieved an average classification specificity, sensitivity, and overall accuracy of 98.31%, 91.84%, 95.06%, respectively. The proposed method is able to obtain all sleep stage condition on patients and non-patients subjects. However, it is feasible to implement in-home and portable system of automatic sleep stage detection instead of using a multichannel signal. |
| ■版 型 |
A4 |
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