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A Paced-ECG Detector and Delineator for Automatic Multi-Parametric Catheter Mapping of Ventricular Tachycardia

Authors :
Freddy Odille
Julien Oster
Alberto Battaglia
Philip Hoyland
Jacques Felblinger
Nefissa Hammache
Christian de Chillou
Imagerie Adaptative Diagnostique et Interventionnelle (IADI)
Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Biosense Webster France, Johnson & Johnson
Service de Cardiologie [CHRU Nancy]
Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)
Centre d'Investigation Clinique - Innovation Technologique [Nancy] (CIC-IT)
Centre d'investigation clinique [Nancy] (CIC)
Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Odille, Freddy
Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)
Source :
IEEE Access, Vol 8, Pp 223952-223960 (2020), IEEE Access, IEEE Access, IEEE, 2020, 8, pp.223952-223960. ⟨10.1109/ACCESS.2020.3043542⟩
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

International audience; Ventricular tachycardia (VT) is a life-threatening arrhythmia, which can be treated by catheter intervention. Accurate identification of the underlying reentrant circuit is often challenging, yet it is key to successful ablation of the VT. In practice, the cardiologist often uses electrocardiography (ECG) data provided by various catheter mapping techniques, including parameters acquired during sinus rhythm (voltage maps, presence of fragmented/late potentials) and during controlled pacing from different sites of the ventricle, so-called pace-mapping. A novel method is presented here to automatically extract the key information from pace-mapping data with automated detection of paced heartbeats from the surface ECG signals, using wavelet detection of pacing spikes and combined time/energy criteria, and automated delineation of paced beats, QRS peak, and QRS onset. This allows the generation of correlation gradient maps (indicating QRS morphology changes as the catheter is moved) and stimulus-to-QRS maps (sQRS, indicating the delay between pacing and activation of the healthy myocardium). The delineator is shown to be in good agreement with manual annotations from experts in a retrospective study of 18 VT ablation procedures. Paced-QRS detection had 95.2% sensitivity and 98.4% positive predictive value. Resulting sQRS maps had a mean absolute error of 11.1 ms, which was in the same range as the inter-observer errors (9.7 ms). The automatic processing drastically reduces the need for manual annotations. Therefore it makes it feasible to process and visualize, during the procedure, all the relevant parametric maps, which can be analyzed jointly to identify VT circuits and corresponding ablation targets.

Details

Language :
English
ISSN :
21693536
Volume :
8
Database :
OpenAIRE
Journal :
IEEE Access
Accession number :
edsair.doi.dedup.....85b10e907d96bb44aa84794ce023eb03