Ventricular activity cancellation in ECG using an adaptive echo state network

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. The paper introduces a new method for ventricular activity cancellation in AF from surface ECG signals. The proposed method is based on AF signal extraction using adaptive echo state neural network (ESN). Adaptive ESN estimates a time-varying, nonlinear transfer function between two ECG leads and separates ventricular activity from atrial activity. The method was compared with conventional pre-whitened recursive least squares (RLS) based adaptive filter. Both algorithms were applied to surrogate ECG data with known component of AF signal. Results show that adaptive ESN performs better than conventional pre-whitened RLS filter, especially in lower amplitude AF signals.