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

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Cybernetics and Systems Analysis Aims and scope

Abstract

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.

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Correspondence to W. A. Chaovalitwongse.

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This research was partially supported by Rutgers Research Council grant 202018, NSF grants CCF-0546574, DBI-980821, EIA-9872509, and CCF 0546574, and NIH grant R01-NS-39687-01A1.

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Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 159–173, January–February 2008.

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Chaovalitwongse, W.A., Pardalos, P.M. On the time series support vector machine using dynamic time warping kernel for brain activity classification. Cybern Syst Anal 44, 125–138 (2008). https://doi.org/10.1007/s10559-008-0012-y

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