Densely Connected Neural Network and Permutation Entropy in the Early Diagnostic in COVID Patients

Luz Alexandra Diaz, Antonio Ravelo-Garcia, Esteban Alvarez, Maria Fernanda Rodriguez, Diego Rodrigo Cornejo, Victor Cabrera-Caso, Dante Condori-Merma, Miguel Vizcardo Cornejo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


The COVID-19 pandemic has been characterized by the high number of infected cases due to its rapid spread around the world, with more than 6 million of deaths. Given that we are all at risk of acquiring this disease and that vaccines do not completely stop its spread, it is necessary to continue proposing tools that help mitigate it. This is the reason why it is ideal to develop a method for early detection of the disease, for which this work uses the Stanford University database to classify patients with SARS-CoV-2, also commonly called as COVID-19, and healthy ones. In order to do that we used a densely connected neural network on a total of 77 statistical features, including permutation entropy, that were contrasted from two different time windows, extracted from the heart rate of 24 COVID patients and 24 healthy people. The results of the classification process reached an accuracy of 86.67% and 100% of precision with the additional parameters of recall and F1-score being 80% and 88.89% respectively. Finally, from the ROC curve for this classification model it could be calculated an AUC of 0.982.

Idioma originalInglés
Título de la publicación alojada2022 Computing in Cardiology, CinC 2022
EditorialIEEE Computer Society
ISBN (versión digital)9798350300970
EstadoPublicada - 2022
Evento2022 Computing in Cardiology, CinC 2022 - Tampere, Finlandia
Duración: 4 set. 20227 set. 2022

Serie de la publicación

NombreComputing in Cardiology
ISSN (versión impresa)2325-8861
ISSN (versión digital)2325-887X


Conferencia2022 Computing in Cardiology, CinC 2022

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© 2022 Creative Commons.


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