Advertisement

Epilepsy Detection from EEG Signals Using Artificial Neural Network

  • Amer A. Sallam
  • Muhammad Nomani KabirEmail author
  • Abdulghani Ali Ahmed
  • Khalid Farhan
  • Ethar Tarek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 866)

Abstract

In the field of medical science, one of the major recent researches is the diagnosis of the abnormalities in brain. Electroencephalogram (EEG) is a record of neuro signals that occur due the different electrical activities in the brain. These signals can be captured and processed to get the useful information that can be used in early detection of some mental and brain diseases. Suitable analysis is essential for EEG to differentiate between normal and abnormal signals in order to detect epilepsy which is one of the most common neurological disorders. Epilepsy is a recurrent seizure disorder caused by abnormal electrical discharges from the brain cells, often in the cerebral cortex. This research focuses on the usefulness of EGG signal in detecting seizure activities in brainwaves. Artificial Neural Network (ANN) is used to train the data set. Then tests are conducted on the test data of EEG signals to identify normal (non-seizure) and abnormal (seizure) states of the brain. Finally, accuracy is computed to evaluate the performance of ANN. The experiments are carried out on CHB-MIT Scalp EEG Database. The experiments show plausible results from the proposed approach in terms of accuracy.

Keywords

Electroencephalogram Artificial neural networks Discrete wavelet transform 

Notes

Acknowledgments

This work was supported by RDU project number 1603102 from University Malaysia Pahang.

References

  1. 1.
    World Health Organization Factsheet: On Epilepsy (2018). http://www.who.int/news-room/fact-sheets/detail/epilepsy
  2. 2.
    Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. Doctoral dissertation, Massachusetts Institute of Technology (2009)Google Scholar
  3. 3.
    Moran, L.V., Hong, L.E.: High vs low frequency neural oscillations in schizophrenia. Schizophr. Bull. 37(4), 659–663 (2011)CrossRefGoogle Scholar
  4. 4.
    Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120(11), 1927–1940 (2009)CrossRefGoogle Scholar
  5. 5.
    Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the K-Means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011)CrossRefGoogle Scholar
  6. 6.
    Salem, O., Naseem, A., Mehaoua, A.: Epileptic seizure detection from EEG signal using discrete wavelet transform and ant colony classifier. In: IEEE International Conference on Communications (ICC), pp. 3529–3534. IEEE (2014)Google Scholar
  7. 7.
    Li, M., Chen, W., Zhang, T.: Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed. Signal Process. Control 31, 357–365 (2017)CrossRefGoogle Scholar
  8. 8.
    Gupta, A., Singh, P., Karlekar, M.: A novel signal modeling approach for classification of seizure and seizure-free EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 26(5), 925–935 (2018)CrossRefGoogle Scholar
  9. 9.
    Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. (in press)Google Scholar
  10. 10.
    CHB-MIT Scalp EEG Database. https://www.physionet.org/pn6/chbmit/
  11. 11.
    Ernawan, F., Kabir, M.N.: A robust image watermarking technique with an optimal DCT-psychovisual threshold. IEEE Access. 6, 20464–20480 (2018)CrossRefGoogle Scholar
  12. 12.
    Ernawan, F., Kabir, M.N.: A blind watermarking technique using redundant wavelet transform for copyright protection. In: 14th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 221–226. IEEE (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amer A. Sallam
    • 1
  • Muhammad Nomani Kabir
    • 2
    Email author
  • Abdulghani Ali Ahmed
    • 2
  • Khalid Farhan
    • 2
  • Ethar Tarek
    • 1
  1. 1.Faculty of Engineering and Information TechnologyTaiz UniversityTaizYemen
  2. 2.Faculty of Computer Systems and Software EngineeringUniversity Malaysia PahangGambangMalaysia

Personalised recommendations