Comparison of QRS Complexes’ Classifiers Developed Using Machine Learning
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1074Keywords:
Machine Learning, Heart arrhythmia, ElectrocardiogramAbstract
Cardiovascular diseases are the most common cause of death and its early diagnosis is key to prevention(1). In 2019, there were 17,9 million deaths caused by cardiovascular diseases globally(1). In the case of arrhythmias, they can be detected through an electrocardiography(2). Some papers have proposed and built models to heartbeat classification(3-7). In this work, three machine learning models were built, based in the MLII lead of the MIT-BIH Database, using a decision tree, multilayer perceptron and deep neural network using two different balanced versions of the same data to classify between 10 arrhythmias. The models were trained with 5-fold stratified cross-validation and their performances, compared using de F1-Score metric, were statistically analyzed with the deep neural network having a better performance within both databases.
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Copyright (c) 2023 Guilherme Bachega Gomes, Rômulo César Silva, Adriana Tokuhashi Kauati, Lucas Guilherme Hübner
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