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Kumar, S.P., Sriraam, N. and Benakop, P.G. (2008) Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier. TENCON IEEE, 1-5. doi:10.1109/CICSyN.2010.84

has been cited by the following article:

• TITLE: Statistical analysis of Epileptic activities based on Histogram and Wavelet-Spectral entropy

  AUTHORS: Ahmad Mirzaei, Ahmad Ayatollahi, Hamed Vavadi

  KEYWORDS: electroencephalogram (EEG), EEG sub-bands, epileptic seizures, discrete wavelet transform (DWT), histogram, spectral entropy (SEN)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.3, March 8, 2011

  ABSTRACT: Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analysed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose the EEG into its sub-bands. Applying histogram and Spectral entropy approaches to the EEG sub-bands, normal and abnormal states of brain can be distinguished with more than 99% probability.