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Comparing ACO Approaches in Epilepsy Seizures

  • Paula Vergara
  • José R. VillarEmail author
  • Enrique de la Cal
  • Manuel Menéndez
  • Javier Sedano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Generalization Capability Wearable Device Partition Scheme Rule Base Classifier Fuzzy Rule Base Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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.

References

  1. 1.
    de Vel, A.V., Cuppens, K., Bonroy, B., Milosevic, M., Huffel, S.V., Vanrumste, B., Lagae, L., Ceulemans, B.: Long-term home monitoring of hypermotor seizures by patient-worn accelerometers. Epilepsy Behav. 26(1), 118–125 (2013)CrossRefGoogle Scholar
  2. 2.
    Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N.E., Fernández, I.S., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., Loddenkemper, T.: Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291–307 (2014)CrossRefGoogle Scholar
  3. 3.
    Cogan, D., Pouyan, M., Nourani, M., Harvey, J.: A wrist-worn biosensor system for assessment of neurological status. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5748–5751 (2014)Google Scholar
  4. 4.
    Nijsen, T., Cluitmans, P., Arends, J., Griep, P.: Detection of subtle nocturnal motor activity from 3-d accelerometry recordings in epilepsy patients. IEEE Trans. Biomed. Eng. 54(11), 2073–2081 (2007)CrossRefGoogle Scholar
  5. 5.
    Bioserenity: Neuronaute (2015). https://www.bioserenity.com/. Accessed 1 October 2015
  6. 6.
    Scientist, N.: Nokia app powers portable brain scanner (2011). https://www.newscientist.com/article/smartphone-brain-scanner/. Accessed 1 October 2015
  7. 7.
    Pandher, P., Bhullar, K.: Smartphone applications for seizure management. Health Inform. J. 18, 1–12 (2014)Google Scholar
  8. 8.
    Ranganathan, L.N., Chinnadurai, S.A., Samivel, B., Kesavamurthy, B., Mehndirata, M.M.: Application of mobile phones in epilepsy care. Int. J. Epilepsy 2, 28–37 (2014)CrossRefGoogle Scholar
  9. 9.
    Villar, J.R., Menéndez, M., Sedano, J., de la Cal, E., González, V.: Analyzing accelerometer data for epilepsy episode recognition. In: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing, vol. 368, pp. 39–48. Springer (2015)Google Scholar
  10. 10.
    Vergara, P., Villar, J.R., Cal, E., Menéndez, M., Sedano, J.: Fuzzy rule learning with ACO in epilepsy crisis identification. In: In Evaluation for the 2015 11th International Conference on Innovations in Information Technology (IIT 2015), Dubai, UAE, November 2015Google Scholar
  11. 11.
    Alvarez-Alvarez, A., Triviño, G., Cordón, O.: Human gait modeling using a genetic fuzzy finite state machine. IEEE Trans. Fuzzy Syst. 20(2), 205–223 (2012)CrossRefGoogle Scholar
  12. 12.
    Villar, J.R., González, S., Sedano, J., Chira, C., Trejo-Gabriel-Galan, J.M.: Improving human activity recognition and its application in early stroke diagnosis. Int. J. Neural Syst. 25(4) (2015). doi: 10.1142/S0129065714500361
  13. 13.
    Casillas, J., Cordón, O., Herrera, F.: Learning Fuzzy Rules Using Ant Colony Optimization Algorithms, pp. 13–21. University of Granada, Granada (2000)Google Scholar
  14. 14.
    Martens, D., Backer, M.D., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11, 651–665 (2007)CrossRefGoogle Scholar
  15. 15.
    Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  16. 16.
    Parpinelli, R., Lopes, H., Freitas, A.: Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6(4), 321–332 (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Salama, K.M., Otero, F.E.B.: Using a unified measure function for heuristics, discretization, and rule quality evaluation in ant-miner. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation, IEEE, pp. 900–907 (2013)Google Scholar
  19. 19.
    Villar, J.R., Menéndez, M., Sedano, J., de la Cal, E., González, V.: Learning fuzzy rules through ant optimization, lasso and dirichlet mixtures. In: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE 2014, pp. 2558–2565 (2014)Google Scholar
  20. 20.
    Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Mach. Learn. 82, 1–42 (2011)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Sánchez, L., Otero, J.: Boosting fuzzy rules in classification problems under single-winner inference. Int. J. Intell. Syst. 22, 1021–1034 (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Paula Vergara
    • 1
  • José R. Villar
    • 1
    Email author
  • Enrique de la Cal
    • 1
  • Manuel Menéndez
    • 2
  • Javier Sedano
    • 3
  1. 1.Computer Science DepartmentUniversity of OviedoOviedoSpain
  2. 2.Morphology and Cellular Biology DepartmentUniversity of OviedoOviedoSpain
  3. 3.Instituto Tecnológico de Castilla y LeónBurgosSpain

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