Open AccessBalanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals
Author(s) -
Mossaad Ben Ayed
Publication year - 2020
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.3878
Subject(s) - support vector machine , computer science , electroencephalography , cad , epilepsy , artificial intelligence , raspberry pi , focus (optics) , machine learning , pattern recognition (psychology) , embedded system , internet of things , neuroscience , engineering , psychology , engineering drawing , physics , optics
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.