Deep Learning for Cardiac Rhythm Assessment: Evaluating CNN Models on ECG Data

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Victor Manuel Astudillo Delgado
David Armando Revelo Luna
Javier Andrés Muñoz Chaves


The electrocardiogram (ECG) is a technique for detecting heart rhythm problems and evaluating the cardiovascular system. Traditionally, physicians relied on manual observations, but this had limitations in terms of accuracy. In this research, convolutional neural networks were used for the identification of cardiac arrhythmias in patients. Three different models were trained, using architectures: VGG16 and ResNet-50, as well as one proposed by the researchers. All models were trained with the dataset (PhysioNet MIT-BIH), with the same amount of data and configuration. The evaluation of the models was performed using the following metrics: precision, recall, F1Score and accuracy. The VGG16 model proved to be the most effective, achieving an accuracy of 98.8%. The results of the research may lead to improved detection of cardiac arrhythmias, which could lead to more accurate diagnoses and better cardiovascular health care for patients.


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Astudillo Delgado , V. M. . ., Revelo Luna , D. A., & Muñoz Chaves, J. A. (2023). Deep Learning for Cardiac Rhythm Assessment: Evaluating CNN Models on ECG Data. I+ T+ C- Research, Technology and Science, 1(17). Retrieved from
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