@article{Pinho_Gomes_dos Santos_2020, title={Classifying cardiac rhythms by means of digital signal processing and machine learning}, volume={35}, url={https://jcis.sbrt.org.br/jcis/article/view/674}, DOI={10.14209/jcis.2020.3}, abstractNote={<div dir="ltr"> <div><span style="color: #000000;">Electrocardiogram (ECG) measures the electrical activity of the heart, which can be used in the diagnosis of different heart diseases. In the scientific literature there are many studies that have been applied machine learning for recognizing ECG patterns, where most of them attempt to classify heart beats. This paper presents a novel methodology for automatically classifying seventeen cardiac rhythms by means of digital signal processing and machine learning. The steps before the classification include the mapping of ECG signal to the frequency domain through power spectrum density, class balance with Adaptive Synthetic Sampling algorithm, and statistical normalization. The classifiers employed were Support Vector Machine, Multilayer Perceptron Neural Network, k-Nearest Neighbors, and Random Forest. The results showed accuracy, sensitivity, specificity, and Fleiss’ kappa of up to 98.86%, 99.93%, 98.85%, and 89.68%, respectively, which are relatively better than the performance observed in the state-of-the-art works. In addition, this study highlighted that when the class balance procedure is applied, the classification step becomes less complex and can increase in terms of performance.</span></div> </div>}, number={1}, journal={Journal of Communication and Information Systems}, author={Pinho, Nielson da Silva and Gomes, Diego de Azevedo and dos Santos, Adam Dreyton Ferreira}, year={2020}, month={Feb.}, pages={25–33} }