Dataset Information

An improved window of interest for electroanatomical mapping of atrial tachycardia.

ABSTRACT:

Purpose

Diagnosis of atrial tachycardia (AT) with 3D mapping system remains challenging due to fibrosis or previous ablation. This study aims to evaluate a new electroanatomical mapping annotation setting using a window of interest adjusted at the end of the P wave (WOI_{p wave}) to identify the AT mechanism more accurately.

Methods

Twenty patients with successful ablation of left AT using navigation system CARTO3 were evaluated. Two maps for each patient were generated offline using either conventional settings of WOI (WOI_conv.) or WOI_{p wave}. Three investigators from two centres analysed the maps blindly.

Results

Mechanisms of AT were macroreentrant in 14/20 patients (70%) and focal in 6/20 (30%). WOI_{p wave} resulted in a significant increase in the percentage of correct identification of the mechanism based on mapping alone (93.3 ± 13.7% vs 58.3 ± 33.9%; p = 0.0003) compared with WOI_conv.. Diagnoses based on mapping were arrived at faster (27.8 ± 16.4 s vs 38.97 ± 13.64 s, respectively; p = 0.0231) and with a greater confidence in the diagnosis (confidence index 2.57 ± 0.45 vs 2.12 ± 0.45, respectively; p = 0.0024). With perimitral re-entry specifically "early meets late" was closer to the anatomical region of the mitral isthmus (15.9 ± 20.9 mm vs 48.77 ± 23.23 mm, respectively; p = 0.0028).

Conclusions

This study found that electroanatomical mapping acquisition with a window of interest set at the end of the P wave improves the ability to diagnose the arrhythmia mechanism based on the initial map. It is particularly beneficial in identifying area of interest for ablation in perimitral AT.

SUBMITTER: Mechulan A

PROVIDER: S-EPMC8755667 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An improved window of interest for electroanatomical mapping of atrial tachycardia.

Mechulan Alexis A Bun Sok-Sithikun SS Masse Alexandre A Peret Angélique A Leong-Feng Lauriane L Pons Frederic F Bouharaoua Ahmed A Dieuzaide Pierre P Prévot Sébastien S

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing 20210127 1

<h4>Purpose</h4>Diagnosis of atrial tachycardia (AT) with 3D mapping system remains challenging due to fibrosis or previous ablation. This study aims to evaluate a new electroanatomical mapping annotation setting using a window of interest adjusted at the end of the P wave (WOI<sub>p wave</sub>) to identify the AT mechanism more accurately.<h4>Methods</h4>Twenty patients with successful ablation of left AT using navigation system CARTO3 were evaluated. Two maps for each patient were generated of ...[more]

PMID: 33506319

Similar Datasets

Project description:BACKGROUND:Electrogram-guided ablation procedures have been proposed as an alternative strategy consisting of either mapping and ablating focal sources or targeting complex fractionated electrograms in atrial fibrillation (AF). However, the incomplete understanding of the mechanism of AF makes difficult the decision of detecting the target sites. To date, feature extraction from electrograms is carried out mostly based on the time-domain morphology analysis and non-linear features. However, their combination has been reported to achieve better performance. Besides, most of the inferring approaches applied for identifying the levels of fractionation are supervised, which lack of an objective description of fractionation. This aspect complicates their application on EGM-guided ablation procedures. METHODS:This work proposes a semi-supervised clustering method of four levels of fractionation. In particular, we make use of the spectral clustering that groups a set of widely used features extracted from atrial electrograms. We also introduce a new atrial-deflection-based feature to quantify the fractionated activity. Further, based on the sequential forward selection, we find the optimal subset that provides the highest performance in terms of the cluster validation. The method is tested on external validation of a labeled database. The generalization ability of the proposed training approach is tested to aid semi-supervised learning on unlabeled dataset associated with anatomical information recorded from three patients. RESULTS:A joint set of four extracted features, based on two time-domain morphology analysis and two non-linear dynamics, are selected. To discriminate between four considered levels of fractionation, validation on a labeled database performs a suitable accuracy (77.6 %). Results show a congruence value of internal validation index among tested patients that is enough to reconstruct the patterns over the atria to located critical sites with the benefit of avoiding previous manual classification of AF types. CONCLUSIONS:To the best knowledge of the authors, this is the first work reporting semi-supervised clustering for distinguishing patterns in fractionated electrograms. The proposed methodology provides high performance for the detection of unknown patterns associated with critical EGM morphologies. Particularly, obtained results of semi-supervised training show the advantage of demanding fewer labeled data and less training time without significantly compromising accuracy. This paper introduces a new method, providing an objective scheme that enables electro-physiologist to recognize the diverse EGM morphologies reliably.

Dataset Information

An improved window of interest for electroanatomical mapping of atrial tachycardia.

Purpose

Methods

Results

Conclusions

Publications

An improved window of interest for electroanatomical mapping of atrial tachycardia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets