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Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology
Miguel Martínez-Iniesta1, Juan Ródenas2, Raúl Alcaraz2
1Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain. miguel.martinez@uclm.es.
Insights
Regular filtering significantly distorts atrial electrograms (EGMs), impacting atrial fibrillation analysis. Wavelet transform methods offer superior noise reduction while preserving EGM morphology for more accurate arrhythmia source localization.
Area of Science:
- Biomedical Engineering
- Cardiac Electrophysiology
- Signal Processing
Background:
- Atrial fibrillation (AF) is a prevalent arrhythmia, with catheter ablation as a first-line treatment.
- Cardiac electrophysiology systems rely on intracardiac electrograms (EGMs) for AF ablation guidance.
- EGMs are susceptible to noise, necessitating effective signal processing for accurate analysis.
Purpose of the Study:
- To investigate the impact of conventional filtering on EGM morphology and accuracy.
- To compare conventional filtering with advanced denoising techniques, including Wavelet transform.
- To evaluate the performance of denoising strategies in preserving EGM characteristics and reducing noise.
Main Methods:
- Analysis of synthesized and real unipolar and bipolar EGMs.
- Comparison of regular filtering techniques against Wavelet transform-based denoising.
- Assessment of performance using time and frequency domain parameters for EGM characterization.
Main Results:
- Conventional filtering substantially alters atrial waveform morphology and inaccurately characterizes EGMs.
- Wavelet transform methods significantly preserve EGM morphology (20-40% improvement).
- Wavelet transform reduces dominant atrial frequency estimation error (up to 25%) and improves noise reduction (up to 10 dB).
Conclusions:
- Conventional filtering strategies negatively impact EGM analysis accuracy.
- Wavelet transform-based algorithms are recommended to replace regular filtering for improved EGM fidelity.
- Adoption of advanced denoising can lead to more accurate atrial activity analysis and AF source localization.
Abstract:
Atrial fibrillation (AF) is the most common arrhythmia in clinical practice with an increasing prevalence of about 15% in the elderly. Despite other alternatives, catheter ablation is currently considered as the first-line therapy for the treatment of AF. This strategy relies on cardiac electrophysiology systems, which use intracardiac electrograms (EGM) as the basis to determine the cardiac structures contributing to sustain the arrhythmia. However, the noise-free acquisition of these recordings is impossible and they are often contaminated by different perturbations. Although suppression of nuisance signals without affecting the original EGM pattern is essential for any other later analysis, not much attention has been paid to this issue, being frequently considered as trivial. The present work introduces the first thorough study on the significant fallout that regular filtering, aimed at reducing acquisition noise, provokes on EGM pattern morphology. This approach has been compared with more refined denoising strategies. Performance has been assessed both in time and frequency by well established parameters for EGM characterization. The study comprised synthesized and real EGMs with unipolar and bipolar recordings. Results reported that regular filtering altered substantially atrial waveform morphology and was unable to remove moderate amounts of noise, thus turning time and spectral characterization of the EGM notably inaccurate. Methods based on Wavelet transform provided the highest ability to preserve EGM morphology with improvements between 20 and beyond 40%, to minimize dominant atrial frequency estimation error with up to 25% reduction, as well as to reduce huge levels of noise with up to 10 dB better reduction. Consequently, these algorithms are recommended as a replacement of regular filtering to avoid significant alterations in the EGMs. This could lead to more accurate and truthful analyses of atrial activity dynamics aimed at understanding and locating the sources of AF.