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

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Abstract

Research has demonstrated a significant overlap between sleep issues and other medical conditions. In 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 conditions. An issue in the diagnosis of MDFA lies in subjectivity. To 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 study. Special attention is given to the problem of how to extract candidate features and fuse dual-modal features. Following the identification of the optimal feature set, this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features. Finally, the recognition accuracy was measured using 10-fold cross validation. The experimental results for our method demonstrate improved performance. The 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.

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Acknowledgements

This work has been supported by National Natural Science Foundation of China (Nos. 61761027 and 61461025), the Yong Scholar Fund of Lanzhou Jiaotong University (No. 2016004) and the Teaching Reform Project of Lanzhou Jiaotong University (No. JGY201841).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Bing-Tao Zhang, Xiao-Peng Wang & Yu Shen

  2. Key Laboratory of Opto-technology and Intelligent Conrtol Ministry of Education, Lanzhou Jiaotong Universtiy, Lanzhou, 730070, China

    Bing-Tao Zhang

  3. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Bing-Tao Zhang

  4. College of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi’an, 710021, China

    Tao Lei

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Correspondence to Bing-Tao Zhang.

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Recommended by Associate Editor Hong Qiao

Bing-Tao Zhang received the M. Sc. degree in computer software and theory from Lanzhou University of Technology, China in 2011. He is currently a Ph. D. degree candidate in computer application at Lanzhou University. Since 2014, he has been a lecturer with the School of Electronic and Information Engineering, Lanzhou Jiaotong University, China.

His research interests include the intersection between computer science and sleep staging, data mining, ontology-based knowledge base modeling of multimodal physiological signals.

Xiao-Peng Wang received the Ph. D. degree in signal and information processing from Northwestern Polytechnical University, China in 2005. He has published about 80 papers in peer reviewed journals and conferences.

His research interests include intelligent information processing and computer application.

Yu Shen received the Ph. D. degree in intelligent transportation and information system engineering from Lanzhou Jiaotong University, China in 2017. She has published about 40 papers in peer reviewed journals and conferences.

Her research interests include digital image processing and communication and information system engineering.

Tao Lei received the Ph. D. degree in information and communication engineering from Northwestern Polytechnical University, China in 2011. He has published about 70 papers in peer reviewed journals and conferences including Image and Vision Computing, IET Image Processing, Science China Information Sciences, Multimedia Tools and Applications, etc.

His research interests include pattern recognition and artificial intelligence.

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Zhang, BT., Wang, XP., Shen, Y. et al. Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm. Int. J. Autom. Comput. 16, 286–296 (2019). https://doi.org/10.1007/s11633-019-1171-1

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