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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2016: Hybrid Artificial Intelligent Systems pp 261–272Cite as

Comparing ACO Approaches in Epilepsy Seizures

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

Abstract

Epilepsy is a neurological illness causing disturbances in the nervous system. In recent studies, a wearable device has been developed and a Hybrid Artificial Intelligent System has been proposed for enhancing the anamnesis in the case of new patients or patients with severe convulsions. Among the different Artificial Intelligent techniques that have been proposed during the last years for Epilepsy Convulsions Identification (ECI), Ant Colony Optimization (ACO) has been found as one of the most efficient alternatives in order to learn Fuzzy Rule Based Classifiers (FRBC) to tackle with this problem.

This study proposes the comparative of two different ACO based learning strategies: the Pittsburg FRBC learning by means of Ant Colony Systems (ACS) and the Michigan FRBC learning using the Ant-Miner+ algorithm. Different alternatives for both strategies are also analyzed.

The obtained results show the Pittsburg ACS learning as a very promising solution for mio-clonic ECI. The Ant-Miner+ based Michigan strategy doesn’t perform well for this research, which is mainly due to the reduced number of features considered in the experimentation.

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Acknowledgments

This research has been funded by the Spanish Ministry of Science and Innovation, under projects MICIN-12-TIN2011-24302 and MINECO-15-TIN2014-56967-R, and Junta de Castilla y León projects BIO/BU09/14 and SACYL 2013 GRS/822/A/13.

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Authors and Affiliations

  1. Computer Science Department, University of Oviedo, Oviedo, Spain

    Paula Vergara, José R. Villar & Enrique de la Cal

  2. Morphology and Cellular Biology Department, University of Oviedo, Oviedo, Spain

    Manuel Menéndez

  3. Instituto Tecnológico de Castilla y León, Burgos, Spain

    Javier Sedano

Authors
  1. Paula Vergara
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  2. José R. Villar
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  3. Enrique de la Cal
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  4. Manuel Menéndez
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  5. Javier Sedano
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Corresponding author

Correspondence to José R. Villar .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2016 Springer International Publishing Switzerland

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Vergara, P., Villar, J.R., de la Cal, E., Menéndez, M., Sedano, J. (2016). Comparing ACO Approaches in Epilepsy Seizures. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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