  Analysis of intra-atrial electrograms (EGMs) nowadays constitutes the most
common way to gain new insights about the mechanisms triggering and maintaining
atrial fibrillation (AF). However, these recordings are highly contaminated by
powerline interference (PLI) due to the large amount of electrical devices
operating simultaneously in the electrophysiology laboratory. To remove this
perturbation, conventional notch filtering has been widely used. However, this
method adds artificial fractionation to the EGMs, thus concealing their
accurate interpretation. Hence, the development of novel algorithms for PLI
suppression in EGMs is still an unresolved challenge. Within this context, the
present work introduces the joint application of common notch filtering and
Wavelet denoising for enhanced PLI removal in AF EGMs. The algorithm was
validated on a set of 100 unipolar EGM signals, which were synthesized with
different noise levels. Original and denoised EGMs were compared in terms of a
signed correlation index (SCI), computed both in time and frequency domains.
Compared with the single use of notch filtering, improvements between 4 and 15%
were reached with Wavelet denoising in both domains. As a consequence, the
proposed algorithm was able to efficiently reduce high levels of PLI and
simultaneously preserve the original morphology of AF EGMs.
