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The electrocardiogram (ECG) is an essential tool in the diagnosis of cardiovascular disease, providing valuable information about heart rhythm and function. Traditionally, physicians relied on manually identified heuristic features to detect ECG abnormalities. However, this methodology had limitations in terms of accuracy and reliability. With the aim of improving accuracy in the identification of cardiac arrhythmias, this research focused on the development of models based on convolutional neural networks. Two data sets were used: the PhysioNet MIT-BIH dataset, widely used in the scientific community, and data acquired by the Bio-Tek BP Pump NIBP arrhythmia simulator. Five models were trained with different architectures, including conventional models such as (VGG16, ResNet-50 and AlexNet), as well as two architectures proposed by the authors. All models were trained with the same number of samples and hyperparameter settings. Performance evaluation was performed using common metrics such as precision, recall, F1-score and accuracy. The results showed that the VGG16 architecture was the most effective in classifying cardiac arrhythmias, achieving an accuracy of 98.8% on the MIT-BIH dataset. Furthermore, when evaluating test data from the Bio-Tek BP Pump NIBP simulator, the customize-2 model demonstrated the best performance with an accuracy of 96.3%. These results are promising, as they demonstrate the potential of convolutional neural networks to improve accuracy in the diagnosis of cardiac arrhythmias. The models developed in this research could be a useful tool for clinicians in the early detection and appropriate treatment of these cardiovascular conditions. [ABSTRACT FROM AUTHOR]