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《Transactions of Beijing Institute of Technology》 2019-08
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Experimental Study on Sleep Stages Based on Normal Inverse Gaussian and Characteristic Contribution

YOU Yu-yang;YOU Shu-kai;GAO Jian-kai;YANG Zhi-hong;School of Automation, Beijing Institute of Technology;Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College;  
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.
【Fund】: 国家自然科学基金资助项目(81473579 81273654);; 北京理工大学基础研究基金资助项目(20140642006)
【CateGory Index】: R740;TN911.7
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