Name
Title | ||
|---|---|---|
Refined Ventricular Activity Cancellation in Electrograms During Atrial Fibrillation by Combining Average Beat Subtraction and Interpolation |
Abstract | ||
|---|---|---|
Many techniques have been developed to cancel the ventricular interference in atrial electrograms (AEG) during atrial fibrillation. In particular, average beat subtraction (ABS) and interpolation are among those mostly adopted. However, ABS usually leaves high power residues and discontinuity at the borders, whereas interpolation totally substitutes the residual activity with a forecasting that might fail at the center of the cancellation segment. In this study, we proposed a new algorithm to refine the ventricular estimate provided by ABS, in such a way that the residual activity should likely be distributed as the local atrial activity. Briefly, the local atrial activity is first modeled with an autoregressive (AR) process, then the estimate is refined by maximizing the log likelihood of the atrial residual activity according to the fitted AR model. We tested the new algorithm on both synthetic and real AEGs, and compared the performance with other four algorithms (two variants of ABS, interpolation and zero substitution). On synthetic data, our algorithm outperformed all the others in terms of average root mean square error (0.043 vs 0.046 for interpolation; p <; 0.05). On real data, our methodology outperformed two variants of ABS (p <; 0.05) and performed similarly to interpolation when considering the high power residues left (both <; 5%), and the log likelihood with the fitted AR model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857335 | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Algorithms,Atrial Fibrillation,Electrocardiography,Heart Atria,Heart Ventricles,Humans | Atrial fibrillation,Computer vision,Autoregressive model,Residual,Computer science,Interpolation,Algorithm,Stochastic process,Mean squared error,Synthetic data,Artificial intelligence,Subtraction | Conference |
Volume | ISSN | ISBN |
2019 | 1557-170X | 978-1-5386-1312-2 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimo W. Rivolta | 1 | 0 | 0.68 |
| Roberto Sassi | 2 | 139 | 19.26 |
| Muhamed Vila | 3 | 0 | 0.68 |