TY - GEN
T1 - Machine Learning Techniques for Classifying Cardiac Arrhythmias
AU - Figueroa-Gil, Lennin Eduardo
AU - López-Cons, Ivana Valeria
AU - Orjuela-Cañón, Alvaro David
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-031-82123-3_3
DO - 10.1007/978-3-031-82123-3_3
M3 - Conference contribution
AN - SCOPUS:85216016577
SN - 9783031821226
T3 - IFMBE Proceedings
SP - 27
EP - 39
BT - 47th Mexican Conference on Biomedical Engineering - Proceedings of CNIB 2024 - Signal Processing And Bioinformatics Congreso Nacional de Ingeniería Biomédica CNIB Hermosillo
A2 - Flores Cuautle, José de Jesús Agustín
A2 - Benítez-Mata, Balam
A2 - Reyes-Lagos, José Javier
A2 - Hernandez Acosta, Humiko Yahaira
A2 - Ames Lastra, Gerardo
A2 - Zuñiga-Aguilar, Esmeralda
A2 - Del Hierro-Gutierrez, Edgar
A2 - Salido-Ruiz, Ricardo Antonio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 47th Mexican Conference on Biomedical Engineering, CNIB 2024
Y2 - 7 November 2024 through 9 November 2024
ER -