Epilepsy Detection Using Artificial Neural Networks

Matheus Adler  Soares Pinto; Bruno  Rocha Gomes; João Pedro Moreno Vale; André Luis  Rolim de Castro Silva; Joadson  Teixeira Castro das Chagas; Wellison  Silva Santos; Victor Hugo  Silva Alves; Davi  Costa Nascimento; Marta de Oliveira Barreiros

doi:10.31686/ijier.vol8.iss4.2292

Authors

Matheus Adler Soares Pinto State University of Maranhão
Bruno Rocha Gomes State University of Maranhão
João Pedro Moreno Vale State University of Maranhão
André Luis Rolim de Castro Silva State University of Maranhão
Teixeira Castro das Chagas State University of Maranhão
Wellison Silva Santos State University of Maranhão
Victor Hugo Silva Alves State University of Maranhão
Davi Costa Nascimento Federal University of Maranhão
Marta de Oliveira Barreiros State University of Maranhão

DOI:

https://doi.org/10.31686/ijier.vol8.iss4.2292

Keywords:

Epilepsy, Electroencephalogram, Artificial neural networks, Multilayer Perceptron, Seizure detection

Abstract

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.

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Author Biographies

Matheus Adler Soares Pinto, State University of Maranhão

Department of Computer Engineering
Bruno Rocha Gomes, State University of Maranhão

Department of Computer Engineering
João Pedro Moreno Vale, State University of Maranhão

Department of Computer Engineering
André Luis Rolim de Castro Silva, State University of Maranhão

Department of Computer Engineering
Teixeira Castro das Chagas, State University of Maranhão

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

Department of Computer Engineering
Victor Hugo Silva Alves, State University of Maranhão

Department of Computer Engineering
Davi Costa Nascimento, Federal University of Maranhão

Department of Electrical Engineering
Marta de Oliveira Barreiros, State University of Maranhão

Department of Computer Engineering

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