Evidence map›Paper›PMID 35429831›Full record

ArticleComputers in biology and medicine2022

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.

Miguel Rodrigo, Mahmood I Alhusseini, Albert J Rogers, Chayakrit Krittanawong, Sumiran Thakur, Ruibin Feng, Prasanth Ganesan, Sanjiv M Narayan

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PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  9. Atrial fibrillation in continuous flow-left ventricular assist device (CF-LVAD) patients: impact on pathophysiology.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Miguel RodrigoCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA; CoMMLab and Electronic Engineering Department, Universitat de Valencia, VA, Spain. Electronic address: miguel.rodrigo@uv.es.
Mahmood I AlhusseiniCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA.
Albert J RogersCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA.
Chayakrit KrittanawongBaylor College of Medicine, TX, USA.
Sumiran ThakurCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA.
Ruibin FengCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA.
Prasanth GanesanCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA.
Sanjiv M NarayanCardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA. Electronic address: sanjiv1@stanford.edu.

Funding

The Dynamics of Human Atrial FibrillationR01HL083359 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Sanjiv M Narayan · 2008 to 2026
$7.9M
Machine Learning in Atrial FibrillationR01HL149134 · NHLBI · STANFORD UNIVERSITY · PI NARAYAN, SANJIV M · 2020 to 2024
$3.8M
Novel Atrial Fibrillation Phenotypes Defined by Functional-Anatomical, Machine-Learned ClassificationsF32HL144101 · NHLBI · STANFORD UNIVERSITY · PI ROGERS, ALBERT JOSEPH · 2018 to 2019
$130k
NHLBI NIH HHS F32 HL144101NHLBI NIH HHS R01 HL083359NHLBI NIH HHS R01 HL149134
6 · The paper itself

Abstract

backgroundAutomatic 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.

objectiveWe hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures.

methodsWe 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.

resultsDL 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).

conclusionsDeep 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.

Indexed as

Atrial FibrillationDeep LearningComputer SimulationElectrophysiologic Techniques, CardiacFemaleHumansArrhythmiaAtrial fibrillationBiomedical signal processingCardiologyElectrophysiologyImplanted devicesMachine learningWearables

Identifiers

PMID35429831
PMCPMC9951584

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.