Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition

DENG Zhaohong; CHEN Junyong; LIU Jiefang; WANG Shitong

doi:10.11999/JEIT160082

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2848-2855

DENG Zhaohong, CHEN Junyong, LIU Jiefang, WANG Shitong. Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2848-2855. doi: 10.11999/JEIT160082

Citation:

DENG Zhaohong, CHEN Junyong, LIU Jiefang, WANG Shitong. Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2848-2855. doi: 10.11999/JEIT160082

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Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition

doi: 10.11999/JEIT160082 cstr: 32379.14.JEIT160082

Funds:

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)

Received Date: 2016-01-19
Rev Recd Date: 2016-06-08
Publish Date: 2016-11-19

Abstract

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.
- ElectroEncephaloGram (EEG) signal,
- Radial basis function neural network,
- Minimax probability,
- Classification tree

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References(24)

References

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