A new chaotic feature for EEG classification based seizure diagnosis

Su Yang; Anqin Zhang; Jiulong Zhang; Weishan Zhang

ICASSP 2017accepted0 citations

A new chaotic feature for EEG classification based seizure diagnosis

Su Yang, Anqin Zhang, Jiulong Zhang, Weishan Zhang

Abstract

Seeking effective measures to characterize the chaotic patterns of EEG signals for seizure diagnosis is a long-term endeavor in the literature. We propose to count the number of zero-crossing (ZC) points on Poincaré surface as a feature when the time series of interest is embedded into the reconstructed state space. The experiments show that Poincaré surface can act as a platform to observe the chaotic patterns of EEG signals and the ZC feature on Poincaré surface is a promising pattern descriptor to discriminate different categories of EEG signals. When used alone for EEG classification, the ZC feature achieves 100%, 99.27%, and 94.68% accuracy in 2-class, 3-class, and 5-class classification on a widely used benchmark.

BibTeX

@inproceedings{icassp2017_anewchaoticfeatu,
  title = {A new chaotic feature for EEG classification based seizure diagnosis},
  author = {Su Yang and Anqin Zhang and Jiulong Zhang and Weishan Zhang},
  booktitle = {ICASSP 2017},
  year = {2017}
}