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

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.

[1]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[2]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[3]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[4]  Kwang Suk Park,et al.  Nonlinear model for ECG R-R interval variation using genetic programming approach , 2005, Future Gener. Comput. Syst..

[5]  James McNames,et al.  Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes , 2004, IEEE Transactions on Biomedical Engineering.

[6]  Max Schaldach,et al.  Characterization of heart rate variability using a nonlinear model , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[7]  A Laupacis,et al.  Prevalence, age distribution, and gender of patients with atrial fibrillation. Analysis and implications. , 1995, Archives of internal medicine.

[8]  David J. Murray-Smith,et al.  Nonlinear model structure identification using genetic programming , 1998 .

[9]  W. Zong,et al.  A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis , 2001, Computers in Cardiology 2001. Vol.28 (Cat. No.01CH37287).

[10]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[11]  J.-M. Boucher,et al.  ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.