Experimental Study on Sleep Stages Based on Normal Inverse Gaussian and Characteristic Contribution

被引：0
|
作者：
You Y.-Y. [1 ]
You S.-K. [1 ]
Gao J.-K. [1 ]
Yang Z.-H. [2 ]
机构：
[1] School of Automation, Beijing Institute of Technology, Beijing
[2] Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2019年 / 39卷 / 08期
关键词：
Feature contribution; Multi-classifier combination; Normal inverse Gaussian; Sleep staging;
D O I：
10.15918/j.tbit1001-0645.2019.08.010
中图分类号：
学科分类号：
摘要：
An experimental framework based on normal inverse Gaussian and feature contribution was proposed for automatic classification of sleep stages. Features were extracted from the sleep EEG (electroencephalo-graph) signals. The signals were decomposed by tunable Q-factor wavelet transform (TQWT). The normal inverse Gaussian parameters were extracted from the TQWT sub-bands. The important features were selected and ranked according to the contribution degree based on the SVM model; according as the selected features of high contribution, the results of different classifiers were compared afterwards. A multi-classifier based automatic sleep staging algorithm was then designed. Results show that, the accuracy of sleep staging can reach 89.88% according to the validation on sleep-EDF dataset from PhysioBank. Compared with the single classifiers, the accuracy of staging can be improved greatly. Therefore, the proposed method is of great value for the clinical diagnosis and researches of sleep disorders. © 2019, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：833 / 838
页数：5
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