Spectral Analysis for Slow Pathway Characterization in Atrioventricular Nodal Reentry Tachycardia

Despite advancements in supraventricular arrhythmia mapping, ablating atrioventricular nodal reentrant tachycardia (AVNRT) still relies on qualitative identification of slow pathway (SP) potentials. This study aims to characterize SP signals in the frequency domain to improve mapping objectivity.By analyzing 355 atrial signals from AVNRT ablation patients, a multiparametric analysis of spectral and temporal parameters was conducted to distinguish target from non-target signals.Combining the amplitude of the second peak with the power spectrum density slope yielded the best discrimination (AUC 0.63), reducing false positives by 50% compared to the operator experience.Automating SP mapping is crucial to avoid non-target radiofrequency deliveries.Shifting to the frequency domain analysis shows promise and ensures further validation and integration into mapping systems for better ablation strategy planning.

Authors

Publication Details

Journal
Computing in cardiology
Published
2024-12-01
DOI
https://doi.org/10.22489/cinc.2024.361
Primary Topic
Cardiac Arrhythmias and Treatments
Type
article
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article

Spectral Analysis for Slow Pathway Characterization in Atrioventricular Nodal Reentry Tachycardia

F Soliani, Antonio Crocamo, Francesca M. Notarangelo, Matteo Fioravanti et al.
Computing in cardiology
Cardiac Arrhythmias and Treatments
article

Spectral Analysis for Slow Pathway Characterization in Atrioventricular Nodal Reentry Tachycardia

F Soliani, Antonio Crocamo, Francesca M. Notarangelo, Matteo Fioravanti, G Bongiovanni, Cristiana Corsi
article en

Abstract

Despite advancements in supraventricular arrhythmia mapping, ablating atrioventricular nodal reentrant tachycardia (AVNRT) still relies on qualitative identification of slow pathway (SP) potentials. This study aims to characterize SP signals in the frequency domain to improve mapping objectivity.By analyzing 355 atrial signals from AVNRT ablation patients, a multiparametric analysis of spectral and temporal parameters was conducted to distinguish target from non-target signals.Combining the amplitude of the second peak with the power spectrum density slope yielded the best discrimination (AUC 0.63), reducing false positives by 50% compared to the operator experience.Automating SP mapping is crucial to avoid non-target radiofrequency deliveries.Shifting to the frequency domain analysis shows promise and ensures further validation and integration into mapping systems for better ablation strategy planning.

Computing in cardiologyVol. 51
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Openalex Percentile: Top 58%
Cardiac Arrhythmias and Treatments
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Spectral Analysis for Slow Pathway Characterization in Atrioventricular Nodal Reentry Tachycardia — F Soliani, Antonio Crocamo, et al. · Computing in cardiology (2024) | TGRS Research Map | TGRS