Epilepsy Detection Using Artificial Neural Networks

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.

Downloads

Download data is not yet available.

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

References

[1] OMS, “Organização Mundial da Saúde,” FAMUN 2015 - Facamp Model United Nations. Pod. e Auton. Nac. O Pap. da indústria na luta contra a obesidade e transtornos Aliment., 2014.
[2] M. J. da S. Fernandes, “Epilepsia do lobo temporal: mecanismos e perspectivas,” Estud. Avançados, vol. 27, no. 77, pp. 85–98, 2013.
[3] P. Kwan and M. J. Brodie, “Early identification of refractory epilepsy,” N. Engl. J. Med., 2000.
[4] L. Hussain, “Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach,” Cogn. Neurodyn., 2018.
[5] L. Hussain et al., “Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states,” J. Physiol. Anthropol., vol. 36, no. 1, p. 21, Dec. 2017.
[6] L. Guo, D. Rivero, J. Dorado, C. R. Munteanu, and A. Pazos, “Automatic feature extraction using genetic programming: An application to epileptic EEG classification,” Expert Syst. Appl., 2011.
[7] K. Fu, J. Qu, Y. Chai, and T. Zou, “Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals,” Biomed. Signal Process. Control, 2015.
[8] H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert Syst. Appl., vol. 36, no. 2, pp. 2027–2036, Mar. 2009.
[9] H. Adeli, Z. Zhou, and N. Dadmehr, “Analysis of EEG records in an epileptic patient using wavelet transform,” J. Neurosci. Methods, 2003.
[10] Z. Iscan, Z. Dokur, and T. Demiralp, “Classification of electroencephalogram signals with combined time and frequency features,” Expert Syst. Appl., 2011.
[11] A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Automatic seizure detection based on time-frequency analysis and artificial neural networks,” Comput. Intell. Neurosci., 2007.
[12] V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks,” IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 3, pp. 288–295, May 2007.
[13] V. P. Nigam and D. Graupe, “A neural-network-based detection of epilepsy,” Neurol. Res., vol. 26, no. 1, pp. 55–60, Jan. 2004.
[14] U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Syst. Appl., 2011.
[15] S. Haykin, “Neural Networks and Kalman filtering. Edited by,” Communications, 2001.
[16] D. Nascimento, J. Queiroz, L. C. Silva, G. C. de Sousa, and A. K. Barros, “EEG Classification of Epileptic Patients Based on Signal Morphology,” 2019, pp. 130–141.

Downloads

Published

01-04-2020

How to Cite

Soares Pinto, M. A. ., Rocha Gomes, B. ., Moreno Vale, J. P., Rolim de Castro Silva, A. L. ., Teixeira Castro das Chagas, J. ., Silva Santos, W. ., Silva Alves, V. H. ., Costa Nascimento, D. ., & Barreiros, M. de O. (2020). Epilepsy Detection Using Artificial Neural Networks. International Journal for Innovation Education and Research, 8(4), 323–328. https://doi.org/10.31686/ijier.vol8.iss4.2292