Feasibility of Machine Learned Intracardiac Electrograms to Predict Postinfarction Ventricular Scar Topography
Convolutional neural network-derived analysis of unipolar electrogram data has excellent predictive value for determination of scar patterns. Computational analyses of electrogram data beyond voltage and other time-domain features are necessary to improve the identification of arrhythmogenic sites in the ventricle.
Authors
- Ashwin Bhaskaran (ORCID: https://orcid.org/0000-0002-8049-5618)
- Saurabh Kumar (ORCID: https://orcid.org/0000-0002-5643-5076)
- Alexandra K O’Donohue (ORCID: https://orcid.org/0000-0003-2267-3671)
- Eddy Kizana (ORCID: https://orcid.org/0000-0002-7266-6720)
- James J.H. Chong (ORCID: https://orcid.org/0000-0002-5201-4856)
- Dinesh Selvakumar (ORCID: https://orcid.org/0000-0002-0228-564X)
- Richard G. Bennett (ORCID: https://orcid.org/0000-0001-9823-3993)
- Samual Turnbull
- Timothy Campbell (ORCID: https://orcid.org/0000-0003-4793-7741)
- Yasuhito Kotake (ORCID: https://orcid.org/0000-0003-4690-8885)
- Robert D. Anderson (ORCID: https://orcid.org/0000-0002-4597-7507)
- Aaron Schindeler (ORCID: https://orcid.org/0000-0002-7757-6281)
- Kasun De Silva (ORCID: https://orcid.org/0000-0002-3527-9289)
- Christopher J. Davey
- Chi-jen Hsu
Institutions
- The University of Sydney (AU)
- The Royal Melbourne Hospital (AU)
- University of British Columbia (CA)
- Children's Hospital at Westmead (AU)
- UNSW Sydney (AU)
- Westmead Institute (AU)
- Westmead Institute for Medical Research (AU)
Publication Details
- Journal
- Circulation Arrhythmia and Electrophysiology
- Published
- 2025-06-13
- DOI
- https://doi.org/10.1161/circep.124.013611
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 0.00