Open AccessAnalysis of Epilepsy Risk Levels from EEG Features through Support Vector Machine and Minimum Relative Entropy Model
Author(s) -
Harikumar Rajaguru,
M. V Ramesh,
K Manoranjith
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1084/1/012029
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , electroencephalography , approximate entropy , computer science , epilepsy , singleton , entropy (arrow of time) , classifier (uml) , machine learning , fuzzy logic , mathematics , speech recognition , psychology , psychiatry , physics , quantum mechanics , pregnancy , biology , genetics
The goal of this research is to attain the epilepsy risk levels from Extracted features from EEG signals. This paper analyses a two-levelmethod of classification. At level one fuzzy method were endorsed to reach a multiple sub optimal solutions. Then the SVM and MRE classifiers were used to reach the singleton results. The Classifiers were compared by means of quality values. The SVM classifier accomplisher higher Quality Value of 23.02 when compared with other classifiers.