Epilepsy Detection Using Artificial Neural Networks

• Matheus Adler Soares Pinto

  State University of Maranhão

  Author

• Bruno Rocha Gomes

  State University of Maranhão

  Author

• João Pedro Moreno Vale

  State University of Maranhão

  Author

• André Luis Rolim de Castro Silva

  State University of Maranhão

  Author

• Teixeira Castro das Chagas

  State University of Maranhão

  Author

• Wellison Silva Santos

  State University of Maranhão

  Author

• Victor Hugo Silva Alves

  State University of Maranhão

  Author

• Davi Costa Nascimento

  Federal University of Maranhão

  Author

• Marta de Oliveira Barreiros

  State University of Maranhão

  Author

Epilepsy is a neurological disorder, where there is a cluster of brain cells that behave in a hyperexcitable manner, the individual can promote injuries, trauma or, in more severe cases, sudden death. Electroencephalogram (EEG) is the most used way to detect epileptic seizures. Therefore, more simplified methods of analysis of the EEG can help in the diagnosis and treatment of these individuals more quickly. In this study, we extracted pertinent EEG characteristics to assess the epileptic seizure period. We use Perceptron Multilayer artificial neural networks to classify the period of the crisis, obtaining a more efficient diagnosis. The multilayer neural network obtained an accuracy of 98%. Thus, the strategy of extracting characteristics and the architecture of the assigned network were sufficient for a rapid and accurate diagnosis of epilepsy.

1. Matheus Adler Soares Pinto, State University of Maranhão

   Department of Computer Engineering

2. Bruno Rocha Gomes, State University of Maranhão

   Department of Computer Engineering

3. João Pedro Moreno Vale, State University of Maranhão

   Department of Computer Engineering

4. André Luis Rolim de Castro Silva, State University of Maranhão

   Department of Computer Engineering

5. Teixeira Castro das Chagas, State University of Maranhão

   Department of Computer Engineering, State University of Maranhão, São Luiz, MA, Brazil

6. Wellison Silva Santos, State University of Maranhão

   Department of Computer Engineering

7. Victor Hugo Silva Alves, State University of Maranhão

   Department of Computer Engineering

8. Davi Costa Nascimento, Federal University of Maranhão

   Department of Electrical Engineering

9. Marta de Oliveira Barreiros, State University of Maranhão

   Department of Computer Engineering

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Copyright (c) 2020 Matheus Adler Soares Pinto, Bruno Rocha Gomes, João Pedro Moreno Vale, André Luis Rolim de Castro Silva, Teixeira Castro das Chagas, Wellison Silva Santos, Victor Hugo Silva Alves, Davi Costa Nascimento, Marta de Oliveira Barreiros

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