title: The detection of epileptic seizure signals based on fuzzy entropy.
abstract: BACKGROUND
  Entropy is a nonlinear index that can reflect the degree of chaos within a system. It is often used to analyze epileptic electroencephalograms (EEG) to detect whether there is an epileptic attack. Much research into the state inspection of epileptic seizures has been conducted based on sample entropy (SampEn). However, the study of epileptic seizures based on fuzzy entropy (FuzzyEn) has lagged behind.
  
  
  METHODS
  We propose a method of state inspection of epileptic seizures based on FuzzyEn. The method first calculates the FuzzyEn of EEG signals from different epileptic states, and then feature selection is conducted to obtain classification features. Finally, we use the acquired classification features and a grid optimization method to train support vector machines (SVM).
  
  
  RESULTS
  The results of two open-EEG datasets in epileptics show that there are major differences between seizure attacks and non-seizure attacks, such that FuzzyEn can be used to detect epilepsy, and our method obtains better classification performance (accuracy, sensitivity and specificity of classification of the CHB-MIT are 98.31%, 98.27% and 98.36%, and of the Bonn are 100%, 100%, 100%, respectively).
  To verify the performance of the proposed method, a comparison of the classification performance for epileptic seizures using FuzzyEn and SampEn is conducted. Our method obtains better classification performance, which is superior to the SampEn-based methods currently in use.
  
  
  CONCLUSIONS
  The results indicate that FuzzyEn is a better index for detecting epileptic seizures effectively. The FuzzyEn-based method is preferable, exhibiting potential desirable applications for medical treatment.
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China; The International WIC Institute, Beijing University of Technology, Beijing 100022, People's Republic of China; Graduate School of Natural Science and Technology, Okayama University, 700-8530 Okayama, Japan. Electronic address: xiangjie@tyut.edu.cn.
authors.forename: Jie
authors.initials: J
authors.lastname: Xiang
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
authors.forename: Conggai
authors.initials: C
authors.lastname: Li
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
authors.forename: Haifang
authors.initials: H
authors.lastname: Li
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
authors.forename: Rui
authors.initials: R
authors.lastname: Cao
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China; Graduate School of Natural Science and Technology, Okayama University, 700-8530 Okayama, Japan.
authors.forename: Bin
authors.initials: B
authors.lastname: Wang
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China; Key Laboratory of Advanced Transducers and Intelligent Control Systems, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
authors.forename: Xiaohong
authors.initials: X
authors.lastname: Han
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China. Electronic address: chenjj@tyut.edu.cn.
authors.forename: Junjie
authors.initials: J
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D001921:Brain / Q000503:physiopathology*; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D017711:Nonlinear Dynamics; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D055815:Young Adult
keywords: Epilepsy detection; Fuzzy entropy; SVM; Sample entropy