Approximate Entropy and Densely Connected Neural Network in the Early Diagnostic of Patients with Chagas Disease

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

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

Abstract

It is estimated that in the world there are between 6 and 8 million people infected with Chagas disease, mainly in endemic areas of 21 Latin American countries, and in recent years it is slowly becoming a health problem in more urban areas and countries. In that sense, developing diagnosis methods is primordial. That is why this work used a deep neural network to classify 292 subjects (volunteers and patients) composed of 83 health volunteers (Control group); 102 asymptomatic chagasic patients (CH1 group) and 107 seropositive chagasic patients with incipient heart disease (CH2 group). Approximate Entropy ApEn was calculated from the tachograms of the circadian profiles of 24 hours every 5 minutes (288 frames) of each subject, and part of this data were used to train the network. The classification work done by the deep neural network had 98% of accuracy and 98% of precision, validated with the ROC curve, whose AUC values were approximately the unit for each group. Taking into account the good performance, we can consider this deep neural network and approximate entropy as useful tools to have a good early diagnosis about Chagas disease and its cardiac compromise.

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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