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

被引：8

作者：

Ben Ayed, Mossaad ^{[1
,2
]}

机构：

[1] Alghat Majmaah Univ, Dept Comp Sci, Coll Sci & Humanities Sci, Al Majmaah, Saudi Arabia

[2] Sfax Univ, Dept Comp Sci, Comp & Embedded Syst Lab, Sfax, Tunisia

来源：

ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH | 2020年 / 10卷 / 06期

关键词：

healthcare; epilepsy; computer-aided diagnosis; balanced communication-avoiding support vector machine; Electroencephalogram (EEG); Raspberry Pi 4; AUTOMATIC IDENTIFICATION; FEATURE-EXTRACTION; SEIZURE DETECTION; CLASSIFICATION; DIAGNOSIS; NETWORKS; SYSTEM;

D O I：

10.48084/etasr.3878

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：6462 / 6468

页数：7

共 39 条

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