Preliminary research of epilepsy brain electricity based on SVM algorithm / 医疗卫生装备

ABSTRACT

Objective To select algorithm for noninvasive EEG screening of epilepsy patients with a view to early detection and reduction of the incidence of epilepsy,morbidity and mortality.Methods Electroencephalogram(EEG)signal characteristics of the normal and epilepsy wave were extracted,automatically identified and classified based on support vector machine(SVM) analysis with a view to achieving epilepsy automatic scale screening.Results The EEG characteristics energy displayed by the model between epilepsy patients and healthy people could be divided obviously.As a new machine learning methods,SVM had a strong ability to generalize.EEG signals based on the algorithm of the classification would become diagnosis of epilepsy patients misprision of a new viable avenue.Conclusion SVM is suitable for the limited samples(small samples).The spontaneous EEG classification with SVM can achieve better results,so it can be used to epileptic EEG abnormality screening.