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Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

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Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

dc.contributor.author	Rodrigo Bort, Miguel
dc.contributor.author	Alhusseini, Mahmood I.
dc.contributor.author	Rogers, A. J.
dc.contributor.author	Krittanawong, Chayakrit
dc.contributor.author	Thakur, Sumiran
dc.contributor.author	Feng, Ruibin
dc.contributor.author	Ganesan, Prasanth
dc.contributor.author	Narayan, Sanjiv M.
dc.date.accessioned	2023-05-22T14:46:05Z
dc.date.available	2023-05-22T14:46:05Z
dc.date.issued	2022
dc.identifier.citation	Rodrigo Bort, Miguel Alhusseini, Mahmood I. Rogers, A. J. Krittanawong, Chayakrit Thakur, Sumiran Feng, Ruibin Ganesan, Prasanth Narayan, Sanjiv M. 2022 Atrial fibrillation signatures on intracardiac electrograms identified by deep learning Computers in Biology and Medicine 145
dc.identifier.uri	https://hdl.handle.net/10550/86826
dc.description.abstract	Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p < 0.001), and also by a set of unipolar EGM shapes that classified as AF independent of rate or regularity. Overall, the optimal AF 'fingerprint' comprised these specific EGM shapes, >15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF.
dc.language.iso	eng
dc.relation.ispartof	Computers in Biology and Medicine, 2022, vol. 145
dc.subject	Tecnologia
dc.subject	Sistema cardiovascular
dc.title	Atrial fibrillation signatures on intracardiac electrograms identified by deep learning
dc.type	journal article
dc.date.updated	2023-05-22T14:46:06Z
dc.identifier.doi	10.1016/j.compbiomed.2022.105451
dc.identifier.idgrec	158806
dc.rights.accessRights	open access