Support method for the diagnosis of Atrial Fibrillation using Machine Learning.

Resumo

The electrocardiogram is an examination that provides a graphical representation of the electrical
activity of the heart. Through it, it is possible to observe the rhythm of heart beats, the number of beats per
minute, in addition to enabling the diagnosis of various arrhythmias. This article aims to develop a classification
model based on the beats of two groups of individuals, healthy and Atrial Fibrillation. The methodology for the
extraction of characteristics based on the morphology of the cardiac signal was adapted to classify Atrial
Fibrillation. Classifications were performed in two-dimensional and three-dimensional space, obtaining accuracy
from 95% to 100%.