Effect of ECG episodes on parameters extraction for paroxysmal atrial fibrillation classification

Abstract

Atrial fibrillation is a type of atria arrhythmia which can cause the formation of blood clot in the heart. The blood clot may enlarge or moving to the brain and cause stroke. Therefore, this study monitors the performance of ECG episodes for paroxysmal atrial fibrillation classification. Episode of 2 seconds to 8 seconds were used to observe the performance of electrocardiograph (ECG) signal processing of atrial fibrillation patient classification. Methods of features extraction were based on the concept of describing short-term behaviour of complex physical and biological system, namely second order system (SOS), and with modified algorithm (hybrid with fast-Fourier transform, FFT). Features extracted from the ECG signal of atrial fibrillation patient were defined using three parameters, i.e. natural frequency, forcing input and damping coefficient. A total of twelve parameters were observed. Comparisons of performance between length of ECG episodes were explored for SOS, FFT-SOS and SOS-FFT algorithms. The episode of 4 seconds using SOS algorithm provides the highest accuracy (98 %) during the classification of ECG signal.