Key result
Baseline drift and high-frequency noise removal improve reconstructed electrogram morphology and activation map accuracy.
Why the study?
To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data.
Do specific signal processing methods (HFR, BDR) improve the accuracy of noninvasive electrocardiographic imaging reconstructions in experimental torso-tank setups?
Do specific signal processing methods (HFR, BDR) improve the accuracy of noninvasive electrocardiographic imaging reconstructions in experimental torso-tank setups?
p-value: p=<0.05
Baseline drift removal improves reconstructed electrogram morphologies and amplitudes in noninvasive electrocardiographic imaging, while high-frequency noise removal improves activation time and pacing site localization.
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May enhance ECGI fidelity in experimental models; leaves open clinical validation for arrhythmia mapping.
Bear et al. (2020) studied Noninvasive electrocardiographic imaging (ECGI) (n=4). Baseline drift removal (BDR) and high-frequency noise removal (HFR) vs. No filtering (raw potentials) was evaluated on Reconstructed electrogram morphologies, amplitudes, and activation map accuracy (p=<0.05). Baseline drift removal significantly improved reconstructed electrogram morphologies and amplitudes, while high-frequency noise removal improved activation time and pacing site localization.
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