An automatic single beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins. (19th May 2022)

• Record Type: Journal Article
• Authors: Schlageter, V; Badertscher, P; Krisai, P; Spies, F; Luca, A; Osswald, S; Vesin, JM; Kuehne, M; Sticherling, C; Knecht, S
• Is Part Of: Europace. Volume 24:Supplement 1(2022)
• Publication Date: 2022-05-19
• Subjects: Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euac053.564
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: Public hospital(s). Main funding source(s): "Stiftung für Herzschrittmacher und Elektrophysiologie" Basel, Switzerland Background: Confirmation of pulmonary vein (PV) isolation (PVI) during ablation of atrial fibrillation can be challenging due to superimposition of nearfield (NF) PV and farfield (FF) atrial bipolar voltage electrograms (BVE). Purpose: To develop an automatic algorithm allowing to discriminate PV nearfield (PV-NF) from atrial farfield (atrial-FF) BVE from a circular mapping catheter during cryoballoon (CB) PVI based on a single-heartbeat analysis. Methods: BVEs from a decapolar inner-lumen diagnostic catheter (Achieve, Medtronic) during CB PVI were manually classified as PV-NF, atrial-FF and combined FF-NF signal based on the characteristics and disappearance of the PV signal during isolation (Figure, upper row). BVE power spectra were computed using the fast Fourier transform (FFT) and the automatic classification of PV-NF, atrial-FF and combined FF-NF signals was performed using the power in different frequency bands (Figure, lower row). Support vector machine classifier was used to identify PV-NF BVE due to its highest predictive accuracy for the two classes PV-NF+ (PV-NF only and combined FF-NF) and PV-NF- (atrial-FF only). Validation of the approach was performed by comparison of a subset of 80 random samples, which were classified in addition by five experienced electrophysiologists. Results: We analysed a dataset of 355 BVEs from 57 patients. The examples were balanced between the two classes PV-NF+ and PV-NF-. The mean duration (95% CI) of the BVE was 58 ms (26 to 86), 70 ms (50 to 100) and 94 ms (71 to 139) for PV NF, atrial-FF and combined FF-NF, respectively. The overall balanced accuracy including BVE from all PVs was 82.7% (95% CI: 80.3% to 85.1%). The analysis on individual PVs showed an accuracy of 96.6%, 85.2%, 80.8%, and 76.9% for the right inferior, right superior, left inferior and left superior PV, respectively. Validation of the algorithm in the subset of 80 patients showed a comparable accuracy, sensitivity and specificity in PV-NF detection between the automatic algorithm and the experienced electrophysiologists (82.8%, 89.2%, and 76.3%, compared to 85.2%, 91.9%, and 78.5%, respectively). Conclusion: A reliable automatic based classification algorithm to identify PV-NF BVE could be developed based on a single-beat analysis. Real-time applications as well as using other electrode configurations may improve local signal interpretation.