Wavelet Based Denoising of Fractionated Intracardiac Electrograms

Fractionated intracardiac electrogram (EGM) recordings contain information about the underlying pathophysiology and are promising to guide clinical ablation procedures.Lowpass filters (LPF) are commonly used to denoise EGM recordings.An alternative is wavelet denoising which does not rely on a constant separation of signal and noise in frequency space.Unipolar and bipolar EGM recordings of 2.5 s registered during a contact mapping guided ablation procedure were exported from the Carto3 (Biosense Webster) mapping system.Different wavelet denoising algorithms are evaluated.The success in reducing noise while retaining the physiologically relevant signal morphology of each algorithm is assessed.For this purpose recordings with a prominent signal are selected such that a signal dominated interval and an isoelectric interval with dominant noise can be identified.The standard deviation of the deviation from baseline in the isoelectric interval are calculated to quantify noise.The change due to denoising in peak counts of bipolar voltages and the negative derivative of unipolar voltages during signal dominated intervals is calculated to quantify the stability of physiologically relevant morphology.The han5.5 wavelet with BlockJS thresholding and decomposition level 8 is identified as a successful choice for a denoising algorithm.