Computer modeling of the electrophysiology of the heart has undergone significant
progress. A healthy heart can be modeled starting from the ion channels via the spread of a …

Objective: The aim of this study is to propose a method to reduce the sensitivity of the
estimated omnipolar electrogram (oEGM) with respect to the angle of the propagation …

Background and motivation: The application of artificial intelligence in medical research,
particularly unsupervised learning techniques, has shown promising potential. Medical time …

Arrhythmogenic Cardiomyopathy (ACM) is a rare cardiac genetic disease that can lead to
severe cardiac structural and electrical abnormalities. Diagnosing ACM includes several …

Abstract Background and Objectives: The extensive collection of electrocardiogram (ECG)
recordings stored in paper format has provided opportunities for numerous digitization …

The diagnosis and treatment of cardiac arrhythmias relies on catheter recordings, that may
be inefficient because of the continued use of the bipolar processing and analysis …

Accurate non-invasive diagnoses in the context of cardiac diseases are problems that
hitherto remain unresolved. We propose an unsupervised classification of atrial flutter (AFL) …

Metodología Robusta Basada en los Fundamentos del Machine Learning Para la Clasificación
de Señales Biomédicas. Aplicación Page 1 Metodología Robusta Basada en los Fundamentos …

Con la aparición de nuevos catéteres multielectrodos se han ampliado las opciones de
exploración en un estudio electrofisiológico. En particular el HD Grid de Abbot, con una …

En este estudio se propone una métrica para medir la heterogeneidad del sustrato cardiaco
a partir de mapas vectoriales derivados de electrogramas omnipolares. Dicho parámetro …