Automatic coordinate prediction of the exit of ventricular tachycardia from 12-lead electrocardiogram

Prashnna K. Gyawali, Shuhang Chen, Huafeng Liu, B. Milan Horacek, John L. Sapp, Linwei Wang

Résultat de recherche: Conference articleexamen par les pairs

6 Citations (Scopus)

Résumé

Automatic localization of the exit site of ventricular tachycardia (VT) can improve the efficiency and efficacy of catheter ablation. Because the exit site of the VT gives rise to its QRS complex on electrocardiogram (ECG), it is possible to build a predictive model to directly localize the exit of a VT from its 12-lead ECG. In previous works, prescribed features such as QRS integrals have been used to build such models. In this paper, we propose a deep network to automatically extract more discriminative features from QRS complex to localize the origin of ventricular activation. To improve the resolution of localization compared to previous works based on a small number of pre-defined segments, we localize the origin of ventricular activation as 3D coordinates. Model training and testing were performed on 12-lead ECG data of 1012 distinct pacing sites, collected from patients during routine pace-mapping procedures. Compared with the use of prescribed QRS-integral as an input feature, the presented deep model achieved an improvement of localization accuracy by approximately 4 millimeters (∼26%) on average.

Langue d'origineEnglish
Pages (de-à)1-4
Nombre de pages4
JournalComputing in Cardiology
Volume44
DOI
Statut de publicationPublished - 2017
Événement44th Computing in Cardiology Conference, CinC 2017 - Rennes, France
Durée: sept. 24 2017sept. 27 2017

Note bibliographique

Funding Information:
This work is supported in part by the National Institutes of Health [No: R21HL125998], the National Science Foundation [No: ACI-1350374], the National Natural Science Foundation of China (No:61525106, 61427807) and the National Key Technology Research and Development Program of China (No:2016YFC1300302).

Publisher Copyright:
© 2017 IEEE Computer Society. All rights reserved.

ASJC Scopus Subject Areas

  • General Computer Science
  • Cardiology and Cardiovascular Medicine

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