A multimodal deep learning tool for detection of junctional ectopic tachycardia in children with congenital heart disease.

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

SUBMITTER: Ju Y 

PROVIDER: S-EPMC11305876 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature