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- W2753669349 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." @default.
- W2753669349 created "2017-09-15" @default.
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- W2753669349 date "2017-09-22" @default.
- W2753669349 modified "2023-09-25" @default.
- W2753669349 title "A Study on Automatic Sleep Staging Algorithm Based on Improved SVM" @default.
- W2753669349 cites W1978347377 @default.
- W2753669349 cites W2017689092 @default.
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- W2753669349 doi "https://doi.org/10.1145/3127404.3127449" @default.
- W2753669349 hasPublicationYear "2017" @default.
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