Classification of atrial fibrillation episodes using short ECG segments

Author

Ortigosa, Nuria ; Cano, Scar ; Andrs, Ana ; Fernndez, Carmen ; Galbis, Antonio ; Ayala, Guillermo

Author_Institution

I.U. Matemtica Pura y Aplic., Univ. Politcnica de Valncia, Valencia, Spain

fYear

2014

fDate

1-4 June 2014

Firstpage

569

Lastpage

572

Abstract

This paper presents a method to classify different subtypes of atrial fibrillation episodes by analyzing short segments of electrocardiograms. We will process surface ECGs segments by time-frequency transforms to extract relevant features that will be used as input to a neural network classifier. As atrial fibrillation presents a progressive nature, this method can be a very useful tool in order to differentiate the progress of the arrythmia in each patient.

Keywords

diseases; electrocardiography; feature extraction; medical signal processing; neural nets; signal classification; time-frequency analysis; transforms; arrythmia progress differentiation; atrial fibrillation episode subtype classification; atrial fibrillation progression; electrocardiography; neural network classifier; relevant feature extraction; short ECG segment analysis; surface ECG segment processing; time-frequency transforms; Biological neural networks; Cardiology; Electrocardiography; Feature extraction; Guidelines; Time-frequency analysis; Transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on

Conference_Location

Valencia

Type

conf

DOI

10.1109/BHI.2014.6864428

Filename

6864428