ECG P-Wave Smoothing and Denoising by Quadratic Variation Reduction

Atrial fibrillation is the most common persistent cardiac arrhythmia and it is characterized by a disorganized atrial electrical activity. Its occurrence can be detected, and even predicted, through P-waves time-domain and morphological analysis in ECG tracings. Given the low signal-to-noise ratio associated to P-waves, such anal- ysis are possible if noise and artifacts are effectively filtered out from P-waves. In this paper a novel smoothing and denoising algorithm for P-waves is proposed. The algorithm is solution to a convex optimization problem. Smoothing and denoising are achieved reducing the quadratic variation of the measured P-waves. Simulation results confirm the effectiveness of the approach and show that the proposed algorithm is remarkably good at smoothing and denoising P-waves. The achieved SNR gain exceeds 15 dB for input SNR below 6 dB. Moreover the proposed algorithm has a computational complexity that is linear in the size of the vector to be processed. This property makes it suitable also for real-time applications