Name
Title | ||
|---|---|---|
A Wavelet-Based Electrogram Onset Delineator for Automatic Ventricular Activation Mapping |
Abstract | ||
|---|---|---|
Electroanatomical mapping (EAM) systems are commonly used in clinical practice for guiding catheter ablation treatments of common arrhythmias. In focal tachycardias, the ablation target is defined by locating the earliest activation area determined by the joint analysis of electrogram (EGM) signals at different sites. However, this is currently a manual time-consuming and experience-dependent task performed during the intervention and thus prone to stress-related errors. In this paper, we present an automatic delineation strategy that combines electrocardiogram (ECG) information with the wavelet decomposition of the EGM signal envelope to identify the onset of each EGM signal for activation mapping. Fourteen electroanatomical maps corresponding to ten patients suffering from non-tolerated premature ventricular contraction (PVC) beats and admitted for ablation procedure were used for evaluation. We compared the results obtained automatically with two types of manual annotations: one during the intervention by an expert technician (on-procedure) and other after the intervention (off-procedure), free from time and procedural constraints, by two other technicians. The automatic annotations show a significant correlation (0.95, p <; 0.01) with the evaluation reference (off-procedure annotation sets combination) and has an error of 2.1 ± 10.9 ms, around the order of magnitude of the on-procedure annotations error (-2.6 ± 6.8 ms). The results suggest that the proposed methodology could be incorporated into EAM systems to considerably reduce processing time during ablation interventions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TBME.2014.2330847 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
focal ventricular tachycardia,electrocardiography,stress-related errors,signal envelope,activation mapping,wavelet analysis,medical disorders,bioelectric potentials,nontolerated premature ventricular contraction beats,catheter ablation,focal tachycardias,wavelet-based electrogram onset delineator,egm signal,catheter ablation treatments,local activation time,arrhythmias,off-procedure annotation sets,medical signal processing,electrogram signals,electroanatomical mapping systems,automatic ventricular activation mapping,electrocardiogram information,egm signals,eam systems,experience-dependent task,manual time-consuming,ablation interventions,on-procedure annotation error,electrophysiology,wavelet decomposition,automatic delineation strategy,electroanatomical maps,ecg information,bipolar electrogram,patient treatment,catheters | Computer vision,Computer science,Electronic engineering,Artificial intelligence,Wavelet | Journal |
Volume | Issue | ISSN |
61 | 12 | 0018-9294 |
Citations | PageRank | References |
4 | 0.72 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alejandro Alcaine | 1 | 12 | 4.42 |
| David Soto-Iglesias | 2 | 18 | 5.91 |
| Mireia Calvo | 3 | 7 | 3.91 |
| Esther Guiu | 4 | 4 | 0.72 |
| David Andreu | 5 | 22 | 4.41 |
| Juan Fernandez-Armenta | 6 | 23 | 4.47 |
| Antonio Berruezo | 7 | 24 | 6.86 |
| Pablo Laguna | 8 | 124 | 20.11 |
| Oscar Camara | 9 | 236 | 24.07 |
| Juan Pablo Martínez | 10 | 461 | 57.73 |