期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 9, 期 -, 页码 1-5出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2013.08.006
关键词
Electroencephalogram (EEG) signal; Fractional linear prediction (FLP); Support vector machine (SVM); Epileptic seizure classification
In this paper, we present a new method for electroencephalogram (EEG) signal classification based on fractional-order calculus. The method, termed fractional linear prediction (FLP), is used to model ictal and seizure-free EEG signals. It is found that the modeling error energy is substantially higher for ictal EEG signals compared to seizure-free EEG signals. Moreover, it is known that ictal EEG signals have higher energy than seizure-free EEG signals. These two parameters are then given as inputs to train a support vector machine (SVM). The trained SVM is then used to classify a set of EEG signals into ictal and seizure-free categories. It is found that the proposed method gives a classification accuracy of 95.33% when the SVM is trained with the radial basis function (RBF) kernel. (C) 2013 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据