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Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

Author

Listed:
  • Zhijiang Wan

    (Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0864, Japan)

  • Hao Zhang

    (College of Economics and Management, Nanjing Forestry University, Nanjing Jiangsu 210037, P. R. China)

  • Jiajin Huang

    (College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, P. R. China)

  • Haiyan Zhou

    (College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, P. R. China)

  • Jie Yang

    (Beijing Anding Hospital of Capital Medical University, Beijing 100088, P. R. China)

  • Ning Zhong

    (Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0864, Japan)

Abstract

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

Suggested Citation

  • Zhijiang Wan & Hao Zhang & Jiajin Huang & Haiyan Zhou & Jie Yang & Ning Zhong, 2019. "Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1579-1603, September.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:05:n:s0219622019500342
    DOI: 10.1142/S0219622019500342
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    References listed on IDEAS

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    1. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    2. Hanshu Cai & Jiashuo Han & Yunfei Chen & Xiaocong Sha & Ziyang Wang & Bin Hu & Jing Yang & Lei Feng & Zhijie Ding & Yiqiang Chen & Jürg Gutknecht, 2018. "A Pervasive Approach to EEG-Based Depression Detection," Complexity, Hindawi, vol. 2018, pages 1-13, February.
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    Cited by:

    1. Yuancheng Si, 2020. "Pivot Property in Weighted Least Regression Based on Single Repeated Observations," Annals of Data Science, Springer, vol. 7(2), pages 291-306, June.

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