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Author:

Wan, Zhijiang (Wan, Zhijiang.) | Zhang, Hao (Zhang, Hao.) | Chen, Jianhui (Chen, Jianhui.) | Zhou, Haiyan (Zhou, Haiyan.) | Yang, Jie (Yang, Jie.) | Zhong, Ning (Zhong, Ning.)

Indexed by:

EI Scopus PubMed

Abstract:

Background: Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log. Results: The Leave-One-Participant-Out Cross-Validation is adopted to estimate the performance of the quantitative model, and the result shows that the model outcomes have a moderate uphill relationship (the average coefficient equals 0.6556 and the P value less than 0.01) with the FPC values of the Q-Log. Furthermore, an exemplary application of knowledge sharing, which is developed by using ontology technology and Jena inference subsystem, is given to illustrate the preliminary work for annotating data and facilitating clinical users to understand the meaning of the quantitative analysis results. Conclusions: This method combining physiological sensor data with psychological self-rating data could provide new insights into the pervasive and objective depression evaluation processes in daily life. © 2018, The Author(s).

Keyword:

Regression analysis Physiological models Decision trees Electroencephalography Ontology

Author Community:

  • [ 1 ] [Wan, Zhijiang]Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi; 3710864, Japan
  • [ 2 ] [Zhang, Hao]College of Economics and Management, Nanjing Forestry University, Nanjing; 210037, China
  • [ 3 ] [Chen, Jianhui]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Chen, Jianhui]International WIC Institute, Beijing University of Technology, Beijing; 100088, China
  • [ 5 ] [Zhou, Haiyan]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Zhou, Haiyan]International WIC Institute, Beijing University of Technology, Beijing; 100088, China
  • [ 7 ] [Yang, Jie]Beijing Anding Hospital of Capital Medical University, Beijing; 100088, China
  • [ 8 ] [Zhong, Ning]Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi; 3710864, Japan
  • [ 9 ] [Zhong, Ning]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Zhong, Ning]International WIC Institute, Beijing University of Technology, Beijing; 100088, China

Reprint Author's Address:

  • 钟宁

    [zhong, ning]international wic institute, beijing university of technology, beijing; 100088, china;;[zhong, ning]college of electronic information and control engineering, beijing university of technology, beijing; 100124, china;;[zhong, ning]department of life science and informatics, maebashi institute of technology, maebashi; 3710864, japan

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Source :

Brain Informatics

ISSN: 2198-4018

Year: 2018

Issue: 2

Volume: 5

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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