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.
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Li, X. (2006). Wavelet Spectral Entropy for Indication of Epileptic Seizure in Extracranial EEG. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_8
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DOI: https://doi.org/10.1007/11893295_8
Publisher Name: Springer, Berlin, Heidelberg
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