Wavelet spectral entropy for indication of epileptic seizure in extracranial EEG

Authors:
Xiaoli Li
Affiliations:
Cercia, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, UK
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 1
Cited 1

Chaotic time series analyses of epileptic seizures

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Abstract

Automated detection of epileptic seizures is very important for an EEG monitoring system. In this paper, a continuous wavelet transform is proposed to calculate the spectrum of scalp EEG data, the entropy and a scale-averaged wavelet power are extracted to indicate the epileptic seizures by using a moving window technique. The tests of five patients with different seizure types show wavelet spectral entropy and scale-averaged wavelet power are more efficiency than renormalized entropy and Kullback_Leiler (K-L) relative entropy to indicate the epileptic seizures. We suggest that the measures of wavelet spectral entropy and scale-averaged wavelet power should be contained to indicate the epileptic seizures in a new EEG monitoring system.