Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 638534 10 pages doi 2009 638534 Research Article Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network Derong Liu 1 Zhongyu Pang 2 and Zhuo Wang2 1 The Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 China 2Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago IL 60607-7053 USA Correspondence should be addressed to Derong Liu Received 3 December 2008 Revised 5 March 2009 Accepted 28 April 2009 Recommended by Jose Principe 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 minutes before seizure onset. The sensitivity is about and the specificity false prediction rate is approximately 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. Copyright 2009 Derong Liu et al. This is an open .