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EEG Signal Feature Selection Algorithm and Support Vector Machine Model in Patient's Fatigue Recognition

  • Chitti, Sridevi1
  • Kumar, J. Tarun2
  • Kumar, V. Sandeep2
  • 1 Department of Electronics and Communication Engineering, KITS Warangal, Warangal, India
  • 2 Department of Electronics and Communication Engineering, SR University, Warangal, India
Published Article
Arabian Journal for Science and Engineering
Publication Date
Oct 05, 2021
DOI: 10.1007/s13369-021-06206-1
PMCID: PMC8490845
PubMed Central
  • Research Article-Electrical Engineering


In March 2020, a cohort of 26 is treated critically ill hospitalized SARS-CoV-2 infected patients who received EEGs to assess unexplained altered mental status, loss of consciousness, or poor arousal and responsiveness. The objective of the present work is to develop a method that is able to automatically determine mental status of vigilance, i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state of mind. Aiming at the EEG feature selection and classification model in the identification of fatigue driving, the discretization algorithm using rough set theory is proposed to select the channel and EEG signal feature quantities. The support vector machine (SVM) is selected as the fatigue driving recognition model, and the risk of fatigue misjudgment is taken as SVM model parameters for model optimization. The experimental results of subjects show that compared with the principal component method, the rough set discretization algorithm selects fewer features, and the compatibility threshold 0.8. The number of features selected among the candidate features is 208. The features selected by different subjects are different and have an impact on the establishment of the support vector machine recognition model. Fatigue misjudgment risk control parameters can adjust the support vector machine recognition model error judgment risk. Even if the present approach is costly in computation time, it allows constructing a decision rule that provides an accurate and fast prediction of the alertness state of an unseen individual.

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