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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
Bioserenity: Neuronaute (2015). https://www.bioserenity.com/. Accessed 1 October 2015
Scientist, N.: Nokia app powers portable brain scanner (2011). https://www.newscientist.com/article/smartphone-brain-scanner/. Accessed 1 October 2015
Pandher, P., Bhullar, K.: Smartphone applications for seizure management. Health Inform. J. 18, 1–12 (2014)
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)
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)
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 2015
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)
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
Casillas, J., Cordón, O., Herrera, F.: Learning Fuzzy Rules Using Ant Colony Optimization Algorithms, pp. 13–21. University of Granada, Granada (2000)
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)
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)
Parpinelli, R., Lopes, H., Freitas, A.: Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6(4), 321–332 (2002)
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)
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)
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)
Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Mach. Learn. 82, 1–42 (2011)
Sánchez, L., Otero, J.: Boosting fuzzy rules in classification problems under single-winner inference. Int. J. Intell. Syst. 22, 1021–1034 (2007)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-32034-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32033-5
Online ISBN: 978-3-319-32034-2
eBook Packages: Computer ScienceComputer Science (R0)