Atrial fibrillation signatures on intracardiac electrograms identified by deep learning. (June 2022)

• Record Type: Journal Article
• Authors: Rodrigo, Miguel; Alhusseini, Mahmood I.; Rogers, Albert J.; Krittanawong, Chayakrit; Thakur, Sumiran; Feng, Ruibin; Ganesan, Prasanth; Narayan, Sanjiv M.
• Is Part Of: Computers in biology and medicine. Volume 145(2022)
• Publication Date: 2022-06
• Subjects: Atrial fibrillation -- Arrhythmia -- Machine learning -- Implanted devices -- Wearables -- Electrophysiology -- Biomedical signal processing -- Cardiology; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105451
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: 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. Highlights: Automatic detection of atrial fibrillation often groups AF with flutter or tachycardia, which may delay or misdirect therapy. Deep learning is as accurate as clinically validated electrocardiographic features to separate fibrillation from tachycardia. 'Opening the black box' allows to identify computational AF phenotypes comprising objective metrics and specific EGM traces. Computational AF signatures can define new AF sub-types of AF patients with differing mechanisms or response to therapy.