Deep Learning and Permutation Entropy in the Stratification of Patients with Chagas Disease

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

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

Abstract

Chagas disease is a life threatening illness that in the last decades was becoming a public health problem because of the change in the epidemiological pattern. It may be silent and asymptomatic in the chronic phase. Hence the necessity of the development of early markers. To achieve this, we propose a deep neural network architecture in order to classify 292 patients into three groups: The Control group with 83 volunteers, the CH1 group with 102 patients with positive serology and no cardiac involvement and the CH2 group with 107 patients with positive serology and incipient heart failure. The used data comes from 24-hour ECG, the RR intervals from each subject was divided in 288 frames of 5 minutes each. Then it was preprocessed using permutation entropy obtaining the circadian profile for each patient. And by applying PCA each patient ended up represented by a vector of 144 entries. This was in turn used for the training of the proposed NN architecture. The classification performed with 91% accuracy and an average of 92% precision, consisting in a great work of classification validated by the AUC in each ROC curve. As this results were obtained with a limited quantity of data, this study can be improved provided with more samples, making this model a tool for analyzing ECG in order to try to do an early evaluation and diagnosis of a cardiac compromise related to the generally silent chronic phase.

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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