Torres Alegre, SantiagoFombellida, JuanPiñuela Izquierdo, Juan AntonioAndina, Diego2019-06-192019-06-192015Torres-Alegre, S., Fombellida, J., Piñuela-Izquierdo, J. A., & Andina, D. (2015). Artificial Metaplasticity: Application to MIT-BIH Arrhythmias Database. In J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. de la Paz López, F. J. Toledo-Moreo, & H. Adeli (Eds.), IWINAC 2015: Artificial Computation in Biology and Medicine (pp. 133–142). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-18914-7_1497833191891309783319189147http://hdl.handle.net/11268/8059Artificial Metaplasticity are Artificial Learning Algorithms based on modelling higher level properties of biological plasticity: the plasticity of plasticity itself, so called Biological Metaplasticity. Artificial Metaplasticity aims to obtain general improvements in Machine Learning based on the experts generally accepted hypothesis that the Metaplasticity of neurons in Biological Brains is of high relevance in Biological Learning. Artificial Metaplasticity Multilayer Perceptron (AMMLP) is the application of Metaplasticity in MLPs ANNs trying to improve uniform plasticity of the Backpropagation algorithm. In this paper two different AMMLP algorithms are applied to the MIT-BIH electro cardiograms database and results are compared in terms of network performance and error evolution.engconference output10.1007/978-3-319-18914-7_14restricted accessRedes de neuronas artificialesAlgoritmos computacionalesInteligencia artificialAlgoritmoAplicación informática