Publication Date
7-1-2024
Journal
Heart Rhythm O2
DOI
10.1016/j.hroo.2024.04.014
PMID
39119021
PMCID
PMC11305876
PubMedCentral® Posted Date
5-16-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Arrhythmia detection, Deep learning, Ensembles, Explainable AI, Junctional ectopic tachycardia
Abstract
BACKGROUND: Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease. It has a marked resemblance to normal sinus rhythm, often leading to delay in diagnosis and management.
OBJECTIVE: The study sought to develop a novel multimodal automated arrhythmia detection tool that outperforms existing JET detection tools.
METHODS: This is a cohort study performed on 40 patients with congenital heart disease at Texas Children's Hospital. Electrocardiogram and central venous pressure waveform data produced by bedside monitors are captured by the Sickbay platform. Convolutional neural networks (CNNs) were trained to classify each heartbeat as either normal sinus rhythm or JET based only on raw electrocardiogram signals.
RESULTS: Our best model improved the area under the curve from 0.948 to 0.952 and the true positive rate at 5% false positive rate from 71.8% to 80.6%. Using a 3-model ensemble further improved the area under the curve to 0.953 and the true positive rate at 5% false positive rate to 85.2%. Results on a subset of data show that adding central venous pressure can significantly improve area under the receiver-operating characteristic curve from 0.646 to 0.825.
CONCLUSION: This study validates the efficacy of deep neural networks to notably improve JET detection accuracy. We have built a performant and reliable model that can be used to create a bedside alarm that diagnoses JET, allowing for precise diagnosis of this life-threatening postoperative arrhythmia and prompt intervention. Future validation of the model in a larger cohort is needed.
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