Application of Permutation Entropy in the Stratification of Patients with Chagas Disease

D. Cornejo, M. Rodriguez, L. Diaz, E. Alvarez, Miguel Angel Vizcardo Cornejo

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


Chagas disease is an endemic disease that in recent decades has ceased to be a rural disease to become mainly an urban disease, becoming a public health problem because it is a life-threatening disease and because 70 million people are at risk of infection. This disease, which has cardiac involvement in its chronic phase, can often be silent and asymptomatic, because of this the establishment of early markers in this type of patients is of great interest. To achieve this goal this study proposes the use of the permutation entropy (PE) which has been shown satisfactory results in the analysis of hearth rhythm and the differentiation between healthy people and people who suffered of congestive heart failure, also known as heart failure. This study analyzes three groups: 83 volunteers (Con-trol), 102 patients with positive serology and no cardiac involvement diagnosed by conventional non-invasive methods (CH1) and 107 patients with positive serology and mild to moderate incipient heart failure (CH2). The used data comes from the 24-hour ECG record, the RR intervals from each of the three groups are shown in 288 frames of 5 minutes. The analysis performed shows significant differences between the three groups, with 90% to 100% specificity and sensitivity, making this study a promising precedent for the development of a low-cost, non-invasive method for detecting possible heart failure in asymptomatic patients.

Idioma originalInglés
Título de la publicación alojada2020 Computing in Cardiology, CinC 2020
EditorialIEEE Computer Society
ISBN (versión digital)9781728173825
EstadoPublicada - 13 set. 2020
Evento2020 Computing in Cardiology, CinC 2020 - Rimini, Italia
Duración: 13 set. 202016 set. 2020

Serie de la publicación

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


Conferencia2020 Computing in Cardiology, CinC 2020

Nota bibliográfica

Publisher Copyright:
© 2020 Creative Commons; the authors hold their copyright.


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