On the time series support vector machine using dynamic time warping kernel for brain activity classification

被引：22

作者：

Chaovalitwongse W.A. ^{[1
]}

Pardalos P.M. ^{[2
]}

机构：

[1] Rutgers University, Piscataway, NJ

[2] University of Florida, Gainesville, FL

来源：

Cybernetics and Systems Analysis | 2008年 / 44卷 / 01期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

Brain dynamics; Classification; Dynamic time warping; EEG; Epilepsy; Optimization; Support vector machines; Time series;

D O I：

10.1007/s10559-008-0012-y

中图分类号：

学科分类号：

摘要：

A new data mining technique used to classify normal and pre-seizure electroencephalograms is proposed. The technique is based on a dynamic time warping kernel combined with support vector machines (SVMs). The experimental results show that the technique is superior to the standard SVM and improves the brain activity classification. © Springer Science+Business Media, Inc. 2008.

引用

页码：125 / 138

页数：13

共 28 条

[1]

Babloyantz A., Destexhe A., Low dimensional chaos in an instance of epilepsy, Proc. Nat. Acad. Sci. USA, 83, pp. 3513-3517, (1986)

[2]

Bertsimas D., Darnell C.R., Soucy R., Portfolio construction through mixed-integer programming at Grantham, Mayo, Van Otterloo and Company, Interfaces, 29, 1, pp. 49-66, (1999)

[3]

Bradley P.S., Fayyad U., Mangasarian O.L., Mathematical programming for data mining: Formulations and challenges, INFORMS J. Computing, 11, pp. 217-238, (1999)

[4]

Breiman L., Friedman J., Olsen R., Stone C., Classification and Regression Tree, (1993)

[5]

Chaovalitwongse W.A., Pardalos P.M., Prokoyev O.A., Reduction of multi-quadratic 0-1 programming problems to linear mixed 0-1 programming problems, Oper. Res. Letters, 32, 6, pp. 517-522, (2004)

[6]

Chaovalitwongse W.A., Pardalos P.M., Prokoyev O.A., Electroencephalogram (EEG) time series classification: Applications in epilepsy, Ann. Oper. Res., 148, 1, pp. 227-250, (2006)

[7]

Elger C.E., Lehnertz K., Seizure prediction by non-linear time series analysis of brain electrical activity, Eur. J. Neurosci., 10, pp. 786-789, (1998)

[8]

Grossman R.L., Kamath C., Kegelmeyer P., Data Mining for Scientific and Engineering Applications, (2001)

[9]

Hand D.J., Mannila H., Smyth P., Principle of Data Mining, (2001)

[10]

Hsu C.-W., Lin C.-J., A comparison of methods multi-class support vector machines, IEEE Trans. on Neural Networks, 13, pp. 415-425, (2002)

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