Combining Energy and Wavelet Transform for Epileptic Seizure Prediction in an Advanced Computational System



Seizure prediction in epileptic patients will allow a deep improvement in their quality of life. In the paper a new method using energy relative measures in wavelet coefficients is proposed and tested in several patients. The results show the potential of the technique, but also its limitations, stressing the needs for further work in a larger number of patients, using multimodal information and an advanced database with a large features set to be used in seizure prediction An advanced computational framework is under development, using multisensorial information to build a large set of features to be used in a classification system supporting seizure prediction. This system is composed of two main parts the algorithms base and the database, briefly described


data mining; seizure prediction; classification; wavelets; nonlinear analysys


Data mining; seizure prediction;


International Conference on Biomedical Engineering and informatics, May 2008

Cited by

Year 2015 : 1 citations

 Real-time mining of epileptic seizure precursors via nonlinear mapping and dissimilarity features
S Nesaei, AR Sharafat - IET Signal Processing, 2015 - IET
We propose a novel approach for detecting precursors to epileptic seizures in intracranial
electroencephalograms (iEEGs), which is based on the analysis of system dynamics. In the
proposed scheme, the largest Lyapunov exponent (LLE) of wavelet entropy of the ...

Year 2014 : 1 citations

 Real-time Detection of Precursors to Epileptic Seizures: Non-Linear Analysis of System Dynamics
S Nesaei, AR Sharafat - Journal of medical signals and sensors, 2014 -

Year 2012 : 1 citations

 Manohar Ayinala, "Low-Power Architectures for Signal Processing and Classi?cation Systems", University of Minnesota, USA, August 2012.

Year 2010 : 2 citations

 Shao-Hang Hung, Chih-Feng Chao, Shu-Kai Wang, Bor-Shyh Lin, Chin-Teng Lin, "VLSI implementation for Epileptic Seizure Prediction System based on wavelet and chaos theory", IEEE Region 10 Conference 2010, TENCON 2010, Fukuoka, Japan, November 21-24, 2010.

 Nesaei, S., Nesaei, S., "Comparison of phase synchrony information flow in human EEG through wavelet phase synchronization analysis", 2010 IEEE 10th International Conference on Signal Processing, ICSP 2010, Beijing, China, October 2010.

Year 2009 : 1 citations

 Hu, S., Stead, M., Liang, H., Worrell, G. A., "Reference Signal Impact on EEG Energy?, 6th international Symposium on Neural Networks: Advances in Neural Networks - Part III, Wuhan, China, May 2009.