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Classification of signals by means of Genetic Programming

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Abstract

This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.

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Acknowledgments

First of all, the authors want to thank the support from the CESGA to execute the test of this paper. The authors wants also to thank the support from different institutions who has funded this work, in particularly, projects: RD07/0067/0005 funded by the Carlos III Health and 10SIN105004PR funded by Economy and Industry Department of Xunta de Galicia.

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Authors and Affiliations

  1. University of A Coruña, Faculty of Computer Science, Campus Elviña, 15011, A Coruña, Spain

    Enrique Fernández-Blanco, Daniel Rivero, Marcos Gestal & Julián Dorado

Authors
  1. Enrique Fernández-Blanco
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  2. Daniel Rivero
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  3. Marcos Gestal
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  4. Julián Dorado
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Corresponding author

Correspondence to Enrique Fernández-Blanco.

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Communicated by F. Herrera.

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Fernández-Blanco, E., Rivero, D., Gestal, M. et al. Classification of signals by means of Genetic Programming. Soft Comput 17, 1929–1937 (2013). https://doi.org/10.1007/s00500-013-1036-4

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