Chagas Disease: An Analysis with Temporal Features Extraction, Permutation Entropy and a Stratification of Heart Risk by a Deep Learning Model

Zayd Isaac Valdez, Luz Alexandra Diaz, Antonio G. Ravelo-Garcia, Miguel Vizcardo Cornejo

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

Resumen

Chagas disease is an endemic disease that in recent decades has ceased to be a rural disease to become mainly an urban disease. In this way, it currently constitutes a public health problem since 70 million people are at risk of contagion of this potentially fatal disease. This disease has an acute and a chronic phase, where in the latter it usually has cardiac involvement that can often be silent and asymptomatic at the beginning. As a result, the establishment of early markers in this type of patients is of great interest. To achieve this, the present study proposes the analysis of RR data through permutation entropy and feature extraction. This study analyzes three groups: 83 volunteers (Control), 102 with Chagas but without cardiac involvement (CH1) and 107 with mild to moderate incipient heart failure (CH2). The data used is from the 24-hour ECG recording, RR intervals are shown in 288 5-minute frames. The analysis performed using permutation entropy and feature extraction shows significant differences between the 3 groups. These data, after a selection of significant segments and dimension reduction by means of PCA, were used in a densely connected neural network that has shown more than satisfactory results, obtaining 98% total accuracy and precision greater than 97% when classifying each group, thus constituting a powerful tool for risk stratification and classification of patients.

Idioma originalInglés
Título de la publicación alojadaComputing in Cardiology, CinC 2023
EditorialIEEE Computer Society
ISBN (versión digital)9798350382525
DOI
EstadoPublicada - 2023
Evento50th Computing in Cardiology, CinC 2023 - Atlanta, Estados Unidos
Duración: 1 oct. 20234 oct. 2023

Serie de la publicación

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

Conferencia

Conferencia50th Computing in Cardiology, CinC 2023
País/TerritorioEstados Unidos
CiudadAtlanta
Período1/10/234/10/23

Nota bibliográfica

Publisher Copyright:
© 2023 CinC.

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