Neural Computing and Applications

, Volume 31, Issue 12, pp 9335–9348 | Cite as

FuzzyEn-based features in FrFT-WPT domain for epileptic seizure detection

  • Mingyang Li
  • Wanzhong ChenEmail author
  • Tao Zhang
Original Article


In this paper, a hybrid method using fuzzy entropy (FuzzyEn)-based features obtained from EEG signals in the FrFT-WPT domain is proposed for seizure detection. We have explored the ability of fractional Fourier transform (FrFT) in EEG processing and make an attempt to calculate FuzzyEn in the combined FrFT and wavelet packet decomposition (WPT) domain. In order to increase the discriminating ability of features, the FuzzyEn is extracted in FrFT-WPT domain when different fractional orders are set. And the principal component analysis (PCA) is employed to produce uncorrelated variables and also to reduce dimensionality. These feature vectors are passed through three classifiers including support vector machine (SVM), k-nearest neighbor (KNN) and linear discriminant analysis (LDA) for classification. In the experiment, we not only analyze the influence of different fractional orders but also compare the performance of various feature extractors. The current model has yielded superior performance for two 3-class classification problems, which are considered difficult, yet crucial, in clinical services. The experimental results also indicate that developed methodology is able to be a valuable diagnostic tool that can aid the doctors in providing better and timely care for the patients suffering from epilepsy.


EEG FrFT Fuzzy entropy WPT 



This work is supported by the Fundamental Research Funds for the Central University (Grant No. 451170301193) and Natural Science Foundation for Science and Technology Development Plan of Jilin Province, China (Grant No. 20150101191JC).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.College of Communication EngineeringJilin UniversityChangchunChina

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