Deep Learning Based Prediction of Atrial Fibrillation Disease Progression with Endocardial Electrograms in a Canine Model
Year: 2020
Type: conference-paper
Abstract: OBJECTIVE: We sought to determine whether electrical patterns in endocardial wavefronts contained elements specific to atrial fibrillation (AF) disease progression. METHODS: A canine paced model (n=7, female mongrel, 29±2 kg) of persistent AF was endocardially mapped with a 64-electrode basket catheter during periods of AF at 1 month, 3 month, and 6 months post-implant of stimulator. A 50-layer residual network was then trained to map half-second electrogram samples to their source timepoint. RESULTS: The trained network achieved final validation and testing accuracies of 51.6 and 48.5% respectively. Per class F1 scores were 24%, 59%, and 53% for 1 month, 3 month, and 6 month inputs from the testing dataset. CONCLUSION: Differentiation of AF based on its time progression was shown to be feasible with a deep learning method. This is promising for differentiating treatment based on disease progression though low accuracy with earlier timepoints may be an obstacle to identifying nascent AF.
Source: Computing in cardiology
Authors: Bram Hunt, Eugene Kwan, Mark McMillan, Derek Dosdall, Rob MacLeod, Ravi Ranjan
Institutions: University of Utah, Boston Scientific (United States)
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W3119912575