Open Access

Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network

EURASIP Journal on Advances in Signal Processing20092009:638534

https://doi.org/10.1155/2009/638534

Received: 3 December 2008

Accepted: 28 April 2009

Published: 7 June 2009

Abstract

None of the current epileptic seizure prediction methods can widely be accepted, due to their poor consistency in performance. In this work, we have developed a novel approach to analyze intracranial EEG data. The energy of the frequency band of 4–12 Hz is obtained by wavelet transform. A dynamic model is introduced to describe the process and a hidden variable is included. The hidden variable can be considered as indicator of seizure activities. The method of particle filter associated with a neural network is used to calculate the hidden variable. Six patients' intracranial EEG data are used to test our algorithm including 39 hours of ictal EEG with 22 seizures and 70 hours of normal EEG recordings. The minimum least square error algorithm is applied to determine optimal parameters in the model adaptively. The results show that our algorithm can successfully predict 15 out of 16 seizures and the average prediction time is 38.5 minutes before seizure onset. The sensitivity is about 93.75% and the specificity (false prediction rate) is approximately 0.09 FP/h. A random predictor is used to calculate the sensitivity under significance level of 5%. Compared to the random predictor, our method achieved much better performance.

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Authors’ Affiliations

(1)
The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences
(2)
Department of Electrical and Computer Engineering, University of Illinois at Chicago

Copyright

© Derong Liu et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.