Fetal Arrhythmia: Deep Learning and Clustering Techniques, Analysis Through Permutation Entropy and Genetic Algorithms in Its Early Diagnosis

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

Fetal arrhythmias occur in 1-2% of pregnancies and involve irregular fetal heart rhythms, typically outside the 100-200 bpm reference range. This condition can be diagnosed as benign in most cases due to its subsequent natural regularization, but 10% of the registered cases indicate that the presence of irregularities in the fetal heart rhythm can trigger morbidity, fetal hydrops or even imminent death of the fetus. In this context, early and precise diagnosis is crucial for addressing this condition and reducing fetal deaths. That is why, a deep learning model is proposed based on a classifying neural network trained with an ECG database of 6 channels (fetal and maternal) accompanied by an intelligent arrangement of clustering techniques, analysis by permutation entropy and data augmentation based on genetic algorithms. This set of techniques aims to form an effective system for the rapid diagnosis of heart rhythm irregularities present in fetuses, ensuring an overall accuracy greater than 92% in fetal arrhythmia risk stratification.

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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