Epileptic Seizure Detection In EEG Signals Using Non-Linear Analysis

  • Authors

    • Saneesh Cleatus T
    • Sunil S
    • Snigdha Naik
    • Swathi Sathyanarayana
    • Syed Afnan
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16497
  • Epilepsy, EEG, Seizure, Non- linear analysis, Entropy, Skewness, SVM, KNN.
  • Abstract

    Epilepsy is a chronic disorder of the central nervous system that occurs irregularly and unpredictably, due to the temporary electrical disturbances in the brain. According to World Health Organization (WHO), approximately 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally [1]. It predisposes individuals to experience recurrent seizures. Electroencephalogram (EEG) is a technique used to measure the electrical activity of the brain signals for the diagnosis of neurological disorders, and it also paves the way for seizure detection using scalp and intra-cranial EEGs as the input data. In this paper, we have proposed a method for non-linear feature based epileptic seizure detection by extracting five features namely Entropy, Mean, Skewness, Standard Deviation and Band Power. The classification techniques used are K-nearest neighbor (KNN) and Support vector machine (SVM) which gave an accuracy of 95.33% and 100% respectively.

     

  • References

    1. [1] World Health Organization. (2018). Epilepsy. [online] Available at: http://www.who.int/en/news-room/fact-sheets/detail/epilepsy.

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      [6] Sutrisno I, Ridha D and Abdullah A,†Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosisâ€, August 2017.

      [7] Selvin P, Ajitha.L, “Early Detection of Epilepsy using EEG signals “.2014 International Conferenceon Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

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  • How to Cite

    Cleatus T, S., S, S., Naik, S., Sathyanarayana, S., & Afnan, S. (2018). Epileptic Seizure Detection In EEG Signals Using Non-Linear Analysis. International Journal of Engineering & Technology, 7(3.12), 764-768. https://doi.org/10.14419/ijet.v7i3.12.16497

    Received date: 2018-07-29

    Accepted date: 2018-07-29

    Published date: 2018-07-20