Classification of Cardiac Arrhythmias by Convolutional Neural Networks and Particle Swarm Optimization
A cardiac arrhythmia is an irregular heartbeat that results in an abnormal electrical impulse, and its type is defined by its rhythm and duration. Its classification has been addressed in different fields of science, highlighting the use of deep learning algorithms (DLA). This research used a hybrid...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | México |
| Recursos: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/8655 |
| Acesso em linha: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/8655 |
| Access Level: | acceso abierto |
| Palavra-chave: | Convolutional Neural Networks Particle Swarm Optimization Computational Model Cardiac Arrhythmias Classification Electrocardiograms Redes Neuronales Convolucionales Optimización por Enjambre de Partículas Modelo Computacional Clasificación de Arrimitas Cardíacas Electrocardiogramas |
| Resumo: | A cardiac arrhythmia is an irregular heartbeat that results in an abnormal electrical impulse, and its type is defined by its rhythm and duration. Its classification has been addressed in different fields of science, highlighting the use of deep learning algorithms (DLA). This research used a hybrid model between Convolutional Neural Networks (CNN) and the Particle Swarm Optimization (PSO) metaheuristic algorithm; for the classification of cardiac arrhythmias. The metaheuristic was in charge of optimizing the architecture of the neural network layers, through the minimization of the loss during training and testing. Data were obtained from the MIT-BIH Arrhythmia dataset, which describes five categories of arrhythmias. The results achieved showed that the metaheuristic is a reliable algorithm in the search for the best layer architecture, achieving an accuracy of 97%, which means that the use of metaheuristic techniques is an option that should be taken into consideration when to optimize the performance of convolutional neural networks. |
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