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Dual-modal Physiological Feature Fusion-based sleep Recognition Using CFS and RF Algorithm

International Journal of Automation and computing 2019年第3期16卷 286-296页

作者： Bing-Tao Zhang Xiao-Peng Wang Yu Shen Tao Lei School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China Key Laboratory of Opto-technology and Intelligent Conrtol Ministry of Education Lanzhou Jiaotong UniverstiyLanzhou 730070China School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Electronical and Information Engineering Shaanxi University of Science and TechnologyXi'an 710021China

Research has demonstrated a significant overlap between sleep issues and other medical *** this paper,we consider mild difficulty in falling asleep(MDFA).Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical *** issue in the diagnosis of MDFA lies in *** address this issue,a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this *** attention is given to the problem of how to extract candidate features and fuse dual-modal *** the identification of the optimal feature set,this study considers the correlations between each feature and class and evaluates correlations between the inter-modality ***,the recognition accuracy was measured using 10-fold cross *** experimental results for our method demonstrate improved *** highest recognition rate of MDFA using the optimal feature set can reach 96.22%.Based on the results of current study,the authors will,in projected future research,develop a real-time MDFA recognition system.

关键词： Feature fusion mild difficulty in falling asleep(MDFA) decision support tool sleep issues optimal feature set

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