A Novel Algorithm for Early Detection of Junctional Ectopic Tachycardia in Patients with Congenital Heart Disease
Paper ⋅ Presentation
Arrhythmias can be lethal for children in the period following cardiac surgery, and junctional ectopic tachycardia (JET) is considered the most common type of tachycardia seen during early post-operative care. We present a novel classification algorithm that detects the JET onset based on electrocardiogram (ECG) waveforms. Our algorithm obtains an average cross-validation sensitivity of 87.07%, specificity of 87.12% and area under the receiver operating charactieristic curve (AUROC) of 90.71% on a dataset of 9 patients from Texas Children’s Hospital. In addition, we present a “human in loop” development pipeline by creating a novel waveform visualization system. This pipeline will enable cardiac surgeons to better recognize and label arrhythmia onset.
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