Classification of Intra Cardiac Atrial Fibrillation using High Order Statistics and Machine Learning.

Resumo

The electrocardiogram is an exam that quantifies the electrical activity of the heart, allowing the
detection of the heart rhythm, the number of beats per minute and the diagnosis of various cardiac pathologies.
This article aims to obtain a classification model based on the beats of two groups of individuals: healthy and
unhealthy. The feature extraction methodology was used and adapted for the classification of atrial fibrillation.
Classifications were performed in two-dimensional and three-dimensional space in the database, obtaining an
accuracy of 97% to 100%.