Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals

Authors

  • M. Ben Ayed AlGhat College of Science and Humanities, Al Majmaah University, Saudi Arabia | University of Sfax, Tunisia
Volume: 10 | Issue: 6 | Pages: 6462-6468 | December 2020 | https://doi.org/10.48084/etasr.3878

Abstract

The revolution in technology affects many fields and among them the Healthcare system. The application-based computer was developed to help specialists to detect diseases, and to perform some basics operations. In this paper, focus is given on the proposed attempts to detect Epilepsy Disease (ED). Several Computer-Aided Diagnosis (CAD) methods were used to provide the brain’s disease status according to signals related to brain activities. These applications achieved acceptable results but still have their limitations. An intelligence CAD based on the Balanced Communication-Avoiding Support Vector Machine (BCA-SVM) is proposed to detect ED using Electroencephalogram (EEG) signals. This attempt is implemented on a Raspberry Pi 4 as a real board to ensure real-time processing. The CAD-based on BCA-SVM achieved an accuracy of 99.8% and the execution time was around 3.2s satisfying the real-time requirement.

Keywords:

healthcare, epilepsy, computer-aided diagnosis, balanced communication-avoiding support vector machine, Electroencephalogram (EEG), Raspberry Pi 4

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How to Cite

[1]
M. Ben Ayed, “Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 6, pp. 6462–6468, Dec. 2020.

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