Application of Fuzzy Similarity to Prediction of Epileptic Seizures Using EEG Signals | Zendy

Xiaoli Li | Zendy; Xin Yao | Zendy

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Application of Fuzzy Similarity to Prediction of Epileptic Seizures Using EEG Signals

Author(s) -

Xiaoli Li,

Xin Yao

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28312-9

DOI - 10.1007/11539506_80

Subject(s) - computer science , electroencephalography , epilepsy , pattern recognition (psychology) , artificial intelligence , similarity (geometry) , epileptic seizure , wavelet transform , fuzzy logic , entropy (arrow of time) , wavelet , similitude , psychology , neuroscience , physics , quantum mechanics , image (mathematics)

The prediction of epileptic seizures is a very attractive issue for all patients suffering from epilepsy in EEG (electroencephalograph) signals. It can assist to develop an intervention system to control / prevent upcoming seizures and change the current treatment method of epilepsy. This paper describes a new method based on wavelet transform and fuzzy similarity measurement to predict the seizures by using EEG signals. One part of the method is to calculate the energy and entropy of EEG data at the different scale; another part of this method is to calculate the similarity between the features set of the reference segment and the test segment using fuzzy measure. The test results of real rats show this method detect temporal dynamic changes prior to a seizure in real time.

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