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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2022 Computing in Cardiology, CinC 2022
PublisherIEEE Computer Society
ISBN (Electronic)9798350300970
DOIs
StatePublished - 2022
Event2022 Computing in Cardiology, CinC 2022 - Tampere, Finland
Duration: 4 Sep 20227 Sep 2022

Publication series

NameComputing in Cardiology
Volume2022-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference2022 Computing in Cardiology, CinC 2022
Country/TerritoryFinland
CityTampere
Period4/09/227/09/22

Bibliographical note

Publisher Copyright:
© 2022 Creative Commons.

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