面向癫痫脑电图信号识别的径向基最小最大概率分类树

doi:10.11999/JEIT160082

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Abstract

ElectroEncephaloGram (EEG) signal detection and recognition is an important diagnostic method for the epilepsy. Radial Basis Function (RBF) neural network has excellent performance on approximation and generalization, and can directly recognize EEG signals in different states. However, its transparency and interpretability are low, and it also ignore the different separabilities between different classes of data. In this paper, a classification tree based on RBF neural networks and minimax probability decision technique is proposed, using one-against-one and exclusive method and paying much attention to the different separabilities among classes. Experiments on EEG signals show that the proposed method has clear structure, strong classification ability and better interpretability.

Key words： ElectroEncephaloGram (EEG) signal Radial basis function neural network Minimax probability Classification tree

Received: 19 January 2016 Published: 01 September 2016

PACS:	R741.0044
	TP183

Fund:

The Youth Fund of Jiangsu Province (BK20140001), YangFan Project of Shanghai Municipal Science and Technology Commission(Grant No. 14YF1411000), The Innovation Program of Shanghai Municipal Education Commission (Grant No. 14YZ131)

Corresponding Authors: DENG Zhaohong E-mail: dengzhaohong@jiangnan.edu.cn

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	DENG Zhaohong
	CHEN Junyong
	LIU Jiefang
	WANG Shitong

Cite this article:

DENG Zhaohong,CHEN Junyong,LIU Jiefang等. Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition[J]. JEIT, 2016, 38(11): 2848-2855.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160082 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2848

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