Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the PR interval of ECG

Author

Arvaneh, M. ; Ahmadi, H. ; Azemi, A. ; Shajiee, M. ; Dastgheib, Z.S.

Author_Institution

Sch. of Comput., Nanyang Technol. Univ., Singapore, Singapore

fYear

2009

fDate

2-4 Dec. 2009

Firstpage

1

Lastpage

5

Abstract

In this work, we propose a new method for prediction of Paroxysmal Atrial Fibrillation (PAF) by only using the PR interval of ECG signal. We first obtain a nonlinear structure and parameters of PR interval by a Genetic Programming (GP) based algorithm. Next, we use the neural networks for prediction of PAF. The inputs of the neural networks are the parameters of nonlinear model of the PR intervals. For the modeling and prediction we have limited ourselves to only 30 seconds of an ECG signal, which is one of the advantages of our proposed approach. For comparison purposes, we have modeled 30 seconds of ECG signals by time based modeling method and have compared prediction results of them.

Keywords

electrocardiography; genetic algorithms; neural nets; ECG signal; Genetic Programming; PR interval; Paroxysmal Atrial Fibrillation; electrocardiography; neural networks; Atrial fibrillation; Cardiac disease; Databases; Electrocardiography; Genetic programming; Heart; Neural networks; Predictive models; Statistics; System identification; Arrhythmia prediction; Atrial fibrillation; Genetic Programming (GP); Neural Networks; Systems identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical and Pharmaceutical Engineering, 2009. ICBPE '09. International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-4763-3

Electronic_ISBN

978-1-4244-4764-0

Type

conf

DOI

10.1109/ICBPE.2009.5384063

Filename

5384063