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How an epileptic EEG segment, used as reference, can influence a cross-correlation classifier?

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Abstract

Several neurological disorders, such as epilepsy, can be diagnosed by electroencephalogram (EEG). Data mining supported by machine learning (ML) techniques can be used to find patterns and to build classifiers for the data. In order to make it possible, data should be represented in an appropriate format, e.g. attribute-value table, which can be built by feature extraction approaches, such as the cross-correlation (CC) method, which uses one signal as reference and correlates it with other signals. However, the reference is commonly selected randomly and, to the best of our knowledge, no studies have been conducted to evaluate whether this choice can affect the ML method performance. Thereby, this work aims to verify whether the choice of an epileptic EEG segment as reference can affect the performance of classifiers built from data. Also, a CC with artificial reference (CCAR) method is proposed in order to reduce possible consequences of the random selection of a signal as reference. Two experimental evaluations were conducted in a set of 200 EEG segments to induce classifiers using ML algorithms, such as J48, 1NN, naive Bayes, BP-MLP, and SMO. In the first study, each epileptic EEG segment was selected as reference to apply CC and ML methods. The evaluation found extremely significant difference, evidencing that the choice of an EEG segment as reference can influence the performance of ML methods. In the second study, the CCAR method was performed, in which statistical tests, only in comparisons involving the SMO classifier, showed not-so-good results.

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Notes

  1. http://www.who.int

  2. http://www.bbci.de/competition/iii/

  3. http://www.bbci.de/competition/iv/

  4. http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3

  5. Classifier and predictive model are considered synonymous in this paper.

  6. http://www.oracle.com/technetwork/java/index.html

  7. https://netbeans.org/

  8. https://www.r-project.org/

  9. For example, one classifier could have presented classification errors in just one fold and another classifier could have presented the same number of classification errors, but distributed into the 10 folds.

  10. In the first experimental evaluation, 500 predictive models were built, 100 for each ML algorithm. In the second experimental evaluation, one classifier were built using each of these algorithms, totaling 5 predictive models.

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Acknowledgments

J. T. Oliva would like to thank the Brazilian funding agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support. J. L. G. Rosa is grateful to the Brazilian agency FAPESP (process 2016/02555-8) for the financial support.

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  1. Bioinspired Computing Laboratory, Computer Science Department, University of São Paulo, Avenida Trabalhador São-Carlense, 400, 13560-970, São Carlos, São Paulo, Brazil

    Jefferson Tales Oliva & João Luís Garcia Rosa

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Correspondence to Jefferson Tales Oliva.

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Oliva, J.T., Garcia Rosa, J.L. How an epileptic EEG segment, used as reference, can influence a cross-correlation classifier?. Appl Intell 47, 178–196 (2017). https://doi.org/10.1007/s10489-016-0891-y

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