Separation Method of Atrial Fibrillation Classes with High Order Statistics and Classification using Machine Learning

Luís Fillype da Silva, Jonathan Queiroz, Caroline Vanessa, Allan Barros, Gean Lopes, Letícia Cabral

Abstract

The electrocardiogram (ECG) is an exam that presents 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 three groups of individuals: with atrial fibrillation, intra-atrial fibrillation and normal sinus rhythm. The methodology of extraction of characteristics based and adapted to classify Atrial Fibrillation and its subtype, Intracardiac Atrial Fibrillation. The classifications were carried out in three-dimensional space in two stages: with the application of Principal Component Analysis (PCA) and without application of it, through Artificial Neural Networks (ANN), Support Vector Machines (SVM) and K-nearest Neighbors (KNN), obtaining accuracy of 93% to 99%.

Paper Citation

in Harvard Style

Fillype da Silva L., Queiroz J., Vanessa C., Barros A., Lopes G. and Cabral L. (2021). Separation Method of Atrial Fibrillation Classes with High Order Statistics and Classification using Machine Learning.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS, ISBN 978-989-758-490-9, pages 284-291. DOI: 10.5220/0010325402840291

in Bibtex Style

@conference{biosignals21,
author={Luís Fillype da Silva and Jonathan Queiroz and Caroline Vanessa and Allan Barros and Gean Lopes and Letícia Cabral},
title={Separation Method of Atrial Fibrillation Classes with High Order Statistics and Classification using Machine Learning},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,},
year={2021},
pages={284-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010325402840291},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - Separation Method of Atrial Fibrillation Classes with High Order Statistics and Classification using Machine Learning
SN - 978-989-758-490-9
AU - Fillype da Silva L.
AU - Queiroz J.
AU - Vanessa C.
AU - Barros A.
AU - Lopes G.
AU - Cabral L.
PY - 2021
SP - 284
EP - 291
DO - 10.5220/0010325402840291