Abstract :
The pre-diagnosis of diseases with computerized systems is widely used in recent years for reducing diagnosis time and ratio of misdiagnosis. In this study, a pre-diagnosis system has been proposed which separates of healthy and epileptic seizures periods. For the experiments, EEG signals acquired from healthy and epileptic individuals were used. In feature extraction stage, recurrence quantification analysis (RQA); in classification stage, support vector machines (SVM), multilayer perceptron neural networks (MLPNN) and Naive Bayes classifiers have been utilized. Accordingly, in case of using MLPNN, 96.67% classification performance was obtained.
Keywords :
Bayes methods; electroencephalography; feature extraction; medical diagnostic computing; medical signal processing; patient diagnosis; perceptrons; recurrent neural nets; signal classification; support vector machines; EEG signals; MLPNN; Naive-Bayes classifiers; RQA; SVM; classification stage; computerized systems; diagnosis time; disease prediagnosis; epileptic seizures periods; feature extraction stage; misdiagnosis; multilayer perceptron neural networks; prediagnosis system; recurrence quantification analysis; support vector machines; Artificial neural networks; Brain; Conferences; Educational institutions; Electroencephalography; Epilepsy; Signal processing; classifiers; epilepsy; epileptic seizure; recurrence quantification analysis;
