- Research Article
- Open access
- Published:
Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network
EURASIP Journal on Advances in Signal Processing volume 2009, Article number: 638534 (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.
Publisher note
To access the full article, please see PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Liu, D., Pang, Z. & Wang, Z. Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network. EURASIP J. Adv. Signal Process. 2009, 638534 (2009). https://doi.org/10.1155/2009/638534
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1155/2009/638534