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Detection of Epileptic Seizures via Deep Long Short-Term Memory

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Current Trends in Biomedical Engineering and Bioimages Analysis (PCBEE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1033))

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Abstract

The paper deals with the designing and implementation of a computer-aided system capable to detect seizures by classification of EEG records. The system is based on deep learning method using a recurrent long short-term memory neural network. The main purpose of the system is to help neurologists in detecting seizures fast and reliably. The research was carried out using real EEG recordings of epileptic patients as well as healthy subjects prepared with the cooperation of the medical staff of the Clinical Ward of Neurology of the University Hospital of Zielona Góra, Poland.

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516, Zielona Góra, Poland

    Krzysztof Patan & Grzegorz Rutkowski

Authors
  1. Krzysztof Patan
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  2. Grzegorz Rutkowski
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Corresponding author

Correspondence to Krzysztof Patan .

Editor information

Editors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Gora, Zielona Góra, Poland

    Józef Korbicz

  2. Committee of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

    Roman Maniewski

  3. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

  4. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Marek Kowal

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Patan, K., Rutkowski, G. (2020). Detection of Epileptic Seizures via Deep Long Short-Term Memory. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_15

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  • DOI: https://doi.org/10.1007/978-3-030-29885-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29884-5

  • Online ISBN: 978-3-030-29885-2

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