Machine Learning Techniques for Classifying Cardiac Arrhythmias

Lennin Eduardo Figueroa-Gil, Ivana Valeria López-Cons, Alvaro David Orjuela-Cañón

Research output: Chapter in Book/InformConference contribution

Abstract

Cardiac arrhythmia, a condition characterized by irregular heartbeats, represents a significant health risk, contributing to 15–20% of deaths worldwide. These irregularities in the heart, whether tachycardia, bradycardia or any other condition, can be life-threatening if it is not diagnosed in time, and especially if it is not diagnosed correctly. Traditional diagnostic methods have the problem of the complexity of the nature of the electrocardiogram signals, as well as the variability of the data. These methods, in addition to being time-consuming, are prone to human error. In this study, we analyze three Deep Learning methods with the objective of improving the detection and classification of different types of arrhythmias. We used the Physionet MIT-BIH arrhythmia data set, which was divided into 80% training data and 20% test data. In preprocessing, we balanced the database and used a Butterworth low-pass filter to reduce noise and obtain only the part of the signal of interest. We compared three different architectures: a multilayer CNN, Mobile-Net and ResNet. The results obtained are very promising in terms of advances for rapid diagnosis of cardiac arrhythmias with accuracies ranging from 0.9779 to 0.9894, and very low losses between 0.0416 and 0.0652, depending on the model. The integration of these advanced models into real-time monitoring systems can provide immediate feedback and alerts for timely medical interventions, representing a powerful tool for preventive and personalized medicine.

Original languageEnglish (US)
Title of host publication47th Mexican Conference on Biomedical Engineering - Proceedings of CNIB 2024 - Signal Processing And Bioinformatics Congreso Nacional de Ingeniería Biomédica CNIB Hermosillo
EditorsJosé de Jesús Agustín Flores Cuautle, Balam Benítez-Mata, José Javier Reyes-Lagos, Humiko Yahaira Hernandez Acosta, Gerardo Ames Lastra, Esmeralda Zuñiga-Aguilar, Edgar Del Hierro-Gutierrez, Ricardo Antonio Salido-Ruiz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages27-39
Number of pages13
ISBN (Print)9783031821226
DOIs
StatePublished - 2025
Event47th Mexican Conference on Biomedical Engineering, CNIB 2024 - Hermosillo, Mexico
Duration: Nov 7 2024Nov 9 2024

Publication series

NameIFMBE Proceedings
Volume116 IFMBE
ISSN (Print)1680-0737
ISSN (Electronic)1433-9277

Conference

Conference47th Mexican Conference on Biomedical Engineering, CNIB 2024
Country/TerritoryMexico
CityHermosillo
Period11/7/2411/9/24

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

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