Assessing Susceptibility to Atrial Fibrillation Using Nonlinear Analysis of ECG Signals

Atrial Fibrillation is a supra ventricular tachyarrhythmia, which is characterized by the deterioration of atrial mechanical function and aberrant. It has become a social and economic problem because a large percentage of the world population suffering from this disease. The early diagnosis of this fatal cardiac Arrhythmia can be prevented and managed it. In this study, we used non-invasive methods based on nonlinear analysis of ECG signal, to identify individuals prone to Atrial Fibrillation. This study consists of three steps, the first step is preprocessing of ECG signals collected from the Physionet to removed 50 Hz and Artifacts noises, then the HRV signal is extracted from them. Secondly, extract nonlinear parameters from HRV signal, including the Poincare Plot Deviations, Correlation Dimension, Lyapunov Exponent, Approximate Entropy and Sample Entropy, and the five characteristics of Recurrence Plot Quantitative Analysis. Third we use a Support Vector Machine classifier for the classification healthy people and those at risk for Atrial Fibrillation Attack. Final results show that Support Vector Machine classifier is able to differentiate healthy from patient population with 93% accuracy, when HRV signal analysis 5 minutes before the onset of Atrial Fibrillation episodes.