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

  • Bing-Tao ZhangEmail author
  • Xiao-Peng Wang
  • Yu Shen
  • Tao Lei
Research Article

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.

Keywords

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

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Notes

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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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhouChina
  2. 2.Key Laboratory of Opto-technology and Intelligent Conrtol Ministry of EducationLanzhou Jiaotong UniverstiyLanzhouChina
  3. 3.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  4. 4.College of Electronical and Information EngineeringShaanxi University of Science and TechnologyXi’anChina

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