An electrocardiogram (ECG) is a non-invasive study used for the diagnosis of cardiac arrhythmias (CAs). The identification of a cardiac arrhythmia depends on its classification. This classification has been approached through different strategies, both mathematical and computational. In this work, a new computational model based on the particle swarm optimization (PSO) algorithm and convolutional neural network (CNN) is proposed for the classification of five classes of CAs obtained from the MIT-BIH Arrhythmia Dataset (MITDB). The goal of the PSO is to optimize the hyperparameters that define the layered architecture of a CNN, to increase the accuracy and decrease the categorical crossentropy error (CE). The proposed model found a satisfactory layered architecture in 17.68 hours, obtaining an accuracy of 98% and 97%, a CE of 0.044968 and 0.084768, in training and testing, respectively. These results demonstrate that the proposed model is reliable and represents an innovative approach because it allows dispensing with the manual selection of the hyperparameters of the layered architecture of a CNN.
A Petri net (PN) is a directed graph which consists of two kinds of nodes called places and transitions. Besides their graphical representation, PN possess a mathematical formalism based on the incidence matrix and the state equation. In this paper we show that PN can be used as a general tool to represent the evolution of any elementary cellular automaton (ECA). This is performed by matrix operations obtained from the state equation of the PN which represent the cellular automaton and the use of a logical operator. It is presented an algorithm to construct a PN for any ECA and we give some comparative examples between the evolution of markings of the PN and the evolution of the respective ECA.
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