Abstract
The epilepsy is one of the neurological disorders that affects people of all socioeconomic groups and ages. An incorrect treatment or a lack in monitoring might produce cognitive damage and depression. In previous work we presented a preliminary method for learning a generalized model to identify epilepsy episodes using 3DACC wearable devices placed on the dominant wrist of the subject. The model was based on a Fuzzy Finite State Machines to detect the epilepsy episodes in 3DACC time series. The learning model applied was a classical Genetic Fuzzy Finite State Machine. The goal of the present work is to adapt the previous learning scheme to a Cooperative Coevolutionary Genetic Fuzzy Finite State Machine to improve the classification results. The obtained results show that a Cooperative proposal outperform moderately the results of the original proposal.
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References
Villanueva V, Girón J, Martín J, Hernández-Pastor L, Lahuerta J, Doz M, Lévy-Bachelot MCL (2013) Quality of life and economic impact of refractory epilepsy in spain: the espera study. Neurologia 28(4):195–204
Engel JJ (2001) International league against epilepsy (ilae). a proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ilae task force on classification and terminology. Epilepsia 42(6):796–803
Shorvon S (2010) Handbook of epilepsy treatment. Wiley-Blackwell
Stefanescu RA, Shivakeshavan R, Talathi SS (2012) Computational models of epilepsy. Seizure 21(10):748–759
Holt AB, Netoff TI (2013) Computational modeling of epilepsy for an experimental neurologist. Exp Neurol 244(0):75–86 (Special Issue: Epilepsy)
Becq G, Bonnet S, Minotti L, Antonakios M, Guillemaud R, Kahane P (2011) Classification of epileptic motor manifestations using inertial and magnetic sensors. Comput Biol Med 41(1):46–55
Ramgopal S, Thome-Souza S, Jackson M, Kadish NE, Fernndez IS, Klehm J, Bosl W, Reinsberger C, Schachter S, Loddenkemper T (2014) Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav 37:291–307
Van de Vel A et al (2011) P26.3 accelerometers for detection of motor seizures during sleep in pediatric patients with epilepsy. Eur J Paediatr Neurol 15(Supplement 1(0)):S134
Lockman J, Fisher RS, Olson DM (2011) Detection of seizure-like movements using a wrist accelerometer. Epilepsy Behav 20(4):638–641
Schulc E, Unterberger I, Saboor S, Hilbe J, Ertl M, Ammenwerth E, Trinka E, Them C (2011) Measurement and quantification of generalized tonic-clonic seizures in epilepsy patients by means of accelerometry- an explorative study. Epilepsy Res 95(1–2):920–1211
Villar JR, Menéndez M, de la Cal E, González VM, Sedano J (2015) Obtaining general models for epilepsy episode recognitions. Inf Sci (2015 submitted)
Casillas Jorge, Cordón Óscar, Herrera Francisco, Merelo Juan Julián (2002) Cooperative Coevolution for Learning Fuzzy Rule-Based Systems. In: Collet Philippe, Fonlupt Cyril, Hao J-K, Lutton Evelyne, Schoenauer Marc (eds) EA 2001, vol 2310., LNCSSpringer, Heidelberg, pp 311–322
Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1(1):27–46
Coello CAC, Lamont GB, Veldhuizen DAV (2007) Evolutionary algorithms for solving multi-objective problems (genetic and evolutionary computation). Springer
Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation 9 (April 2012) 21+
Cogan D, Pouyan M, Nourani M, Harvey J (2014) A wrist-worn biosensor system for assessment of neurological status. In: 36th annual international conference of the IEEE engineering in medicine and biology society 5748–5751
Silva CJP, Rémi J, Vollmar C, Fernandes J, Gonzalez-Victores J, Noachtar S (2013) Upper limb automatisms differ quantitatively in temporal and frontal lobe epilepsies. Epilepsy Behav 27(2):404–408
Bonnet S, Jallon P, Bourgerette A, Antonakios M, Guillemaud R, Caritu Y, Becq G, Kahane P, Chapat P, Thomas-Vialettes B, Thomas-Vialettes F, Gerbi D, Ejnes D (2011) An ethernet motion-sensor based alarm system for epilepsy monitoring. IRBM 32(2):155–157
Van de Vel A et al (2013) Long-term home monitoring of hypermotor seizures by patient-worn accelerometers. Epilepsy Behav 26(1):118–125
Poh M, Loddenkemper T, Reinsberger C, Swenson N, Goyal S, Sabtala M, Madsen J, Picard R (2012) Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia 5(53):93–97
Nijsen T, Aarts R, Cluitmans P, Griep P (2010) Time-frequency analysis of accelerometry data for detection of myoclonic seizures. IEEE Trans Inf Technol Biomed 14:1197–1203
Beniczky S, Polster T, Kjaer T, Hjalgrim H (2013) Detection of generalized tonicclonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia 4(54):e58–61
Kramer U, Kipervasser S, Shlitner A, Kuzniecky R (2011) A novel portable seizure detection alarm system: preliminary results. J Clin Neurophysiol 4(28):36–8
Nijsen T, Cluitmans P, Arends J, Griep P (2007) Detection of subtle nocturnal motor activity from 3-d accelerometry recordings in epilepsy patients. IEEE Trans Biomed Eng 54(11):2073–2081
Cuppens K, Lagae L, Ceulemans B, Van Huffel S, Vanrumste B (2009) Detection of nocturnal frontal lobe seizures in pediatric patients by means of accelerometers: a first study. In: Conference Proceedings IEEE Engineering Medicine and Biology Society 6608–11
Dalton A, Patel S, Chowdhury A, Welsh M, Pang T, Schachter S et al (2012) Development of a body sensor network to detect motor patterns of epileptic seizures. IEEE Trans Biomed Eng 59:3204–11
Tan CH, Yap KS, Yap HJ (2012) Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using pittsburg approach. Appl Soft Comput 12(8):2168–2177
Tian J, Li M, Chen F (2010) Dual-population based coevolutionary algorithm for designing rbfnn with feature selection. Expert Syst Appl 37(10):6904–6918
Fernández A, López V, del Jesus M, Herrera F (2015) Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges. Knowl-Based Syst (In Press, Accepted Manuscript, February 2015)
Potter MA, Jong KAD (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8:1–29
Alvarez-Alvarez A, Triviño G, Cordón O (2012) Human gait modeling using a genetic fuzzy finite state machine. IEEE Trans Fuzzy Syst 20(2):205–223
Peña Reyes CA, Sipper M (2001) Fuzzy coco: a cooperative-coevolutionary approach to fuzzy modeling. IEEE Trans Fuzzy Syst 9(5):727–737
Villar J, Gonzlez S, Sedano J, Chira C, Trejo-Gabriel-Galan J (2014) Improving human activity recognition and its application in early stroke diagnosis. Int J Neural Syst 10:1–20
Acknowledgments
This research has been funded by the Spanish Ministry of Science and Innovation, under projects TIN2011-24302 and TIN2014-56967-R, Fundación Universidad de Oviedo project FUO-EM-340-13, Junta de Castilla y León projects BIO/BU09/14 and SACYL 2013 GRS/822/A/13.
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de la Cal, E.A., Villar, J.R., Vergara, P.M., Sedano, J., Herrero, A. (2015). A Preliminary Cooperative Genetic Fuzzy Proposal for Epilepsy Identification Using Wearable Devices. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_5
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