Identification of epilepsy seizures using multi-resolution analysis and artificial neural networks. Gómez-Gil, P., Juárez-Guerra, E., Alarcón-Aquino, V., Ramírez-Cortés, M., & Rangel-Magdaleno, J. Volume 547 , 2014.
abstract   bibtex   
© Springer International Publishing Switzerland 2014. Finding efficient and effective automatic methods for the identification and prediction of epileptic seizures is highly desired, due to the relevance of this brain disorder. Despite the large amount of research going on in identification and prediction solutions, still it is required to find confident methods suitable to be used in real applications. In this paper, we discuss the principal challenges found in epilepsy identification, when it is carried on offline analyzing electro-encephalograms (EEG) recordings. Indeed, we present the results obtained so far in our research group, with a system based on multi-resolution analysis and feed-forward neural networks, which focus on tackling three important challenges found in this type of problems: noise reduction, feature extraction and pertinence of the classifier. A 3-fold validation of our strategy reported an accuracy of 99.26 ± 0.26%, a sensitive of 98.93%and a specificity of 99.59%, using data provided by the University of Bonn. Several combinations of filters and wavelet transforms were tested, found that the best results occurs when a Chebyshev II filter was used to eliminate noise, 5 characteristics were obtained using a Discrete Wavelet Transform (DWT) with a Haar wavelet and a feed-forward neural network with 18 hidden nodes was used for classification.
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 title = {Identification of epilepsy seizures using multi-resolution analysis and artificial neural networks},
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 abstract = {© Springer International Publishing Switzerland 2014. Finding efficient and effective automatic methods for the identification and prediction of epileptic seizures is highly desired, due to the relevance of this brain disorder. Despite the large amount of research going on in identification and prediction solutions, still it is required to find confident methods suitable to be used in real applications. In this paper, we discuss the principal challenges found in epilepsy identification, when it is carried on offline analyzing electro-encephalograms (EEG) recordings. Indeed, we present the results obtained so far in our research group, with a system based on multi-resolution analysis and feed-forward neural networks, which focus on tackling three important challenges found in this type of problems: noise reduction, feature extraction and pertinence of the classifier. A 3-fold validation of our strategy reported an accuracy of 99.26 ± 0.26%, a sensitive of 98.93%and a specificity of 99.59%, using data provided by the University of Bonn. Several combinations of filters and wavelet transforms were tested, found that the best results occurs when a Chebyshev II filter was used to eliminate noise, 5 characteristics were obtained using a Discrete Wavelet Transform (DWT) with a Haar wavelet and a feed-forward neural network with 18 hidden nodes was used for classification.},
 bibtype = {book},
 author = {Gómez-Gil, P. and Juárez-Guerra, E. and Alarcón-Aquino, V. and Ramírez-Cortés, M. and Rangel-Magdaleno, J.}
}

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