The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions
| Autor: | Wilson W. Good, Jaume Coll-Font, Jana Svehlikova, Eelco van Dam, Rob S. MacLeod, Yesim Serinagaoglu Dogrusoz, Laura Bear |
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| Rok vydání: | 2021 |
| Předmět: |
Signal processing
0206 medical engineering Torso Tank Biomedical Engineering 02 engineering and technology Iterative reconstruction Band-stop filter Article Electrocardiography QRS complex Hfr cell medicine Waveform Physics Body Surface Potential Mapping Cardiac Pacing Artificial Torso Signal Processing Computer-Assisted Inverse problem equipment and supplies 020601 biomedical engineering PVC medicine.anatomical_structure inverse problem ECGI Biomedical engineering |
| Zdroj: | IEEE transactions on bio-medical engineering |
| ISSN: | 1558-2531 0018-9294 |
| Popis: | Goal: To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data. Methods: Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded. Results: HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05). Conclusion: BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur. |
| Databáze: | OpenAIRE |
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