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.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 368, pp. 49–63). Springer Verlag. https://doi.org/10.1007/978-3-319-19719-7_5
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