DocumentCode :
2501889
Title :
Wepilet, optimal orthogonal wavelets for epileptic seizure prediction with one single surface channel
Author :
Bandarabadi, Mojtaba ; Teixeira, Cesar A. ; Sales, Francisco ; Dourado, Antonio
Author_Institution :
Centre for Inf. & Syst., Univ. of Coimbra, Coimbra, Portugal
fYear :
2011
fDate :
Aug. 30 2011-Sept. 3 2011
Firstpage :
7059
Lastpage :
7062
Abstract :
Wepilet is a series of novel orthogonal wavelets optimized for Electroencephalogram (EEG) signals, specialized for epileptic seizure prediction. The main idea is to design a mother wavelet that when applied to EEG signal to create the feature space, should enable a better classification of the brain state. Wepilet is developed by an iterative optimization process, employing Genetic Algorithm (GA). Frequency sub-band features are first extracted using wepilet under design for the EEG signal captured by one single surface channel. These features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and inter-ictal classes. The results of the classification are then used to compute the Probability of Error Rate (PER), which in turn is the GA objective function to be minimized. Results in a group of four patients, indicate the efficiency of optimized mother wavelet compared to the well-known Daubechies wavelet in EEG processing.
Keywords :
electroencephalography; feature extraction; iterative methods; medical disorders; medical signal processing; optimisation; probability; support vector machines; EEG signal; SVM; cerebral state; electroencephalogram signals; epileptic seizure prediction; error rate probability; frequency subband feature extraction; genetic algorithm; iterative optimization process; optimal orthogonal wavelets; optimized mother wavelet; preictal classes; single surface channel; support vector machines; wepilet; Electroencephalography; Equations; Feature extraction; Genetic algorithms; Optimization; Support vector machines; Wavelet transforms; Algorithms; Area Under Curve; Electroencephalography; Epilepsy; Fourier Analysis; Humans; Models, Statistical; Models, Theoretical; Probability; Reproducibility of Results; Signal Processing, Computer-Assisted; Support Vector Machines; Time Factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2011.6091784
Filename :
6091784
Link To Document :
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