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Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks

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Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 547))

Abstract

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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Acknowledgments

The first author gratefully acknowledges the financial support from the “Universidad Autónoma de Tlaxcala” and PROMEP by scholarship No. UATLX-244. This research has been partially supported by CONACYT, project grant No. CB-2010-155250.

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Authors and Affiliations

  1. Department of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, PUE, México

    Pilar Gómez-Gil

  2. Department of Electronics, Universidad de las Américas, Cholula, PUE, México

    Ever Juárez-Guerra & Vicente Alarcón-Aquino

  3. Department of Electronics, Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, PUE, México

    Manuel Ramírez-Cortés & José Rangel-Magdaleno

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  1. Pilar Gómez-Gil
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  2. Ever Juárez-Guerra
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  3. Vicente Alarcón-Aquino
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  4. Manuel Ramírez-Cortés
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  5. José Rangel-Magdaleno
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Correspondence to Pilar Gómez-Gil .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Gómez-Gil, P., Juárez-Guerra, E., Alarcón-Aquino, V., Ramírez-Cortés, M., Rangel-Magdaleno, J. (2014). Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-05170-3_23

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  • Online ISBN: 978-3-319-05170-3

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