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

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

VENEMA V, AMENT F, and SIMMER C. A stochastic iterative amplitude adjusted Fourier transform algorithm with improved accuracy[J]. Nonlinear Processes in Geophysics, 2006, 13(3): 321-328. doi: 10.5194/npg-13- 321-2006.

POLAT K and GNES S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform[J]. Applied Mathematics Computation, 2007, 187(2): 1017-1026. doi: 10.1016/j.amc. 2006.09.022.

INAN G and ELIF DERYA U. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients[J]. Journal of Neuroscience Methods, 2005, 148(2): 113-121. doi: 10.1016/j.jneumeth.2005.04.013.

SUBASI A. EEG signal classification using wavelet feature extraction and a mixture of expert model[J]. Expert Systems with Applications, 2007, 32(4): 1084-1093. doi: 10.1016/ j.eswa.2006.02.005.

王登, 苗夺谦, 王睿智. 一种新的基于小波包分解的EEG特征抽取与识别方法研究[J]. 电子学报, 2013, 41(1): 193-198. doi: 10.3969/j.issn.0372-2112.2013.01.33.

WANG Deng, MIAO Duoqian, and WANG Ruizhi. A new method of EEG classification with feature extraction based on wavelet packet decomposition[J]. Acta Electronica Sinica, 2013, 41(1): 193-198. doi: 10.3969/j.issn.0372-2112.2013. 01.33.

SRINIVASAN V, ESWARAN C, and SRIRAAM A N. Artificial neural network based epileptic detection using time-domain and frequency-domain features[J]. Journal of Medical Systems, 2005, 29(6): 647-660. doi: 10.1007/ s10916-005-6133-1.

VAIRAVAN S, CHIKKANNAN E, and NATARAJAN S. Approximate entropy-based epileptic EEG detection using artificial neural networks[J]. IEEE Transactions on Information Technology in Biomedicine, 2007, 11(3): 288-295. doi: 10.1109/TITB.2006.884369.

ORHAN U, HEKIM M, and OZER M. EEG signals classification using the K-means clustering and a multilayer perceptron neural network model[J]. Expert Systems with Applications, 2011, 38(10): 13475-13481. doi: 10.1016/ j.eswa.2011.04.149.

ASLAN K and HSAHIN B. A radial basis function neural network model for classification of epilepsy using EEG signals[J]. Journal of Medical Systems, 2008, 32(5): 403-408. doi: 10.1007/s10916-008-9145-9.

连可, 陈世杰, 周建明, 等. 基于遗传算法的SVM多分类决策树优化算法研究[J]. 控制与决策, 2009, 24(1): 7-12. doi: 10.3321/j.issn:1001-0920.2009.01.002.

LIAN Ke, CHEN Shijie, ZHOU Jianming , et al. Study on GA-based SVM multi-class classification decision-tree optimization agorithm[J]. Control and Decision, 2009, 24(1): 7-12. doi: 10.3321/j.issn:1001-0920.2009.01.002.

LANCKRIET G, GHAOUI L E, BHATTACHARYYA C, et al. Minimax probability machine[C]. Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada. 2001: 801-807.

LANCKRIET G R G, GHAOUI L E, BHATTACHARYYA C, et al. A robust minimax approach to classification[J]. The Journal of Machine Learning Research, 2002, 3(Dec): 555-582. doi: 10.1162/153244303321897726.

DENG Z, CAO L, JIANG Y, et al. Minimax probability TSK fuzzy system classifier: A more transparent and highly interpretable classification model[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(4): 813-826. doi: 10.1109/TFUZZ. 2014.2328014.

RUBIO-SOLIS A and PANOUTSOS G. Interval type-2 radial basis function neural network: A modeling framework[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(2): 457-473. doi: 10.1109/TFUZZ.2014.2315656.

陈聪, 王士同. 基于模糊分组和监督聚类的RBF回归性能改进[J]. 电子与信息学报, 2009, 31(5): 1157-1160.

CHEN Cong and WANG Shitong. Improved RBF regression using fuzzy partition and supervised fuzzy custering[J]. Journal of Electronics Information Technology, 2009, 31(5): 1157-1160.

ROTH P M, HIRZER M, KSTINGER M, et al. Mahalanobis Distance Learning for Person Re-identification [M]. London: Person Re-Identification, 2014: 247-267. doi: 10.1007/978-1-4471-6296-4_12.

Kang S, Cho S, and Kang P. Constructing a multi-class classifier using one-against-one approach with different binary classifiers[J]. Neurocomputing, 2014, 149, Part B(PB): 677-682. doi: 10.1016/j.neucom.2014.08.006.

GALAR M, FERN?NDEZ A, and BARRENECHEA E. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes[J]. Pattern Recognition, 2011, 44(8): 1761-1776. doi: 10.1016/j.patcog.2011.01.017.

ANDRZEJAK R G, LEHNERTZ K, MORMANN F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state[J]. Physical Review E, 2001, 64(6): 061907. doi: 10.1103/PhysRevE.64. 061907.

PARVEZ M Z and PAUL M. Epileptic seizure detection by analyzing EEG signals using different transformation techniques[J]. Neurocomputing, 2014, 145(18): 190-200. doi: 10.1016/j.neucom.2014.05.044.

ROY V and SHUKLA S. Automatic removal of artifacts from EEG signal based on spatially constrained ICA using daubechies wavelet[J]. International Journal of Modern Education and Computer Science (IJMECS), 2014, 6(7): 31-39. doi: 10.5815/ijmecs.2014.07.05.

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