ECG-Based Unsupervised Clustering in Coronary Artery Disease Associates with Ventricular Arrhythmia

Authors

  • Josseline Madrid Instituto de Investigación en Ingeniería de Aragón (I3A)
  • Julia Ramírez Instituto de Investigación en Ingeniería de Aragón (I3A) https://orcid.org/0000-0003-4130-5866
  • Ana Mincholé Instituto de Investigación en Ingeniería de Aragón (I3A)

DOI:

https://doi.org/10.26754/jjii3a.20239034

Abstract

Coronary Artery Disease (CAD) is a leading cause of life-threatening ventricular arrhythmias (LTVAs). This study aimed to identify distinct clusters of CAD individuals based on QRS morphology using a 3-nearest neighbors clustering algorithm. Cluster 1, characterized by the lowest QRS amplitudes and widest QRS complexes, was strongly associated with LTVA risk.

Display downloads

Downloads

Published

2023-07-07

Issue

Vol. 11 (2023): Actas de la XII Jornada de Jóvenes Investigadores e Investigadoras del I3A - 15 de junio de 2023

Section

Artículos (Ingeniería Biomédica)

License

Copyright (c) 2023 Josseline Madrid, Julia Ramírez, Ana Mincholé

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Madrid, J., Ramírez, J., & Mincholé, A. (2023). ECG-Based Unsupervised Clustering in Coronary Artery Disease Associates with Ventricular Arrhythmia. Jornada De Jóvenes Investigadores Del I3A, 11. https://doi.org/10.26754/jjii3a.20239034