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Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features

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Computer Science and Engineering—Theory and Applications

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 143))

Abstract

Seizures caused by epilepsy are unprovoked, they disrupt the mantel activity of the patient and impair their normal motor and sensorial functions, endangering the patient’s well being. Exploiting today’s technology it is possible toe create automatic systems to monitor and evaluate patients. An area of special interest is the automatic analysis of EEG signals. This paper presents extensive analysis of feature extraction and classification methods that have reported good results in other EEG based problems. Several methods are detailed to extract 52 features from the time, frequency and time-frequency domains in order to characterize the EEG signals. Additionally, 10 different classification models, together with a feature selection method, are implemented using these features to identify if a signal corresponds to an epileptic state. The experiments were performed using the standard BONN and the proposed method achieve results comparable to those in the state-of-the-art for the three and four classes problems.

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Acknowledgements

This work was founded through the project “Clasificador de emociones 2.0 utilizando medios fisiológicos, cerebrales y conductuales” under the PEI 2017 program, with the collaboration of ITT and Neuroaplicaciones y Tecnologías S.A. de C.V.

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Authors and Affiliations

  1. TecNM—Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, 22500, Tijuana, Baja California, Mexico

    D. E. Hernández, L. Trujillo & E. Z-Flores

  2. Neuroaplicaciones y Tecnologías S.A.P.I. de C.V, Blvd. Diaz Ordaz 12415, OF. M5-7, Fracc. El Paraiso, Tijuana, Baja California, Mexico

    O. M. Villanueva & O. Romo-Fewell

Authors
  1. D. E. Hernández
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  2. L. Trujillo
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  3. E. Z-Flores
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  4. O. M. Villanueva
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  5. O. Romo-Fewell
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Corresponding author

Correspondence to D. E. Hernández .

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Editors and Affiliations

  1. Calzada Universidad, Tijuana, Baja California, Mexico

    Mauricio A. Sanchez

  2. Calzada Universidad, Tijuana, Baja California, Mexico

    Leocundo Aguilar

  3. Calzada Universidad, Tijuana, Baja California, Mexico

    Manuel Castañón-Puga

  4. Calzada Universidad, Tijuana, Baja California, Mexico

    Antonio Rodríguez-Díaz

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Hernández, D.E., Trujillo, L., Z-Flores, E., Villanueva, O.M., Romo-Fewell, O. (2018). Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features. In: Sanchez, M., Aguilar, L., Castañón-Puga, M., Rodríguez-Díaz, A. (eds) Computer Science and Engineering—Theory and Applications. Studies in Systems, Decision and Control, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-74060-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-74060-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74059-1

  • Online ISBN: 978-3-319-74060-7

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