Improving Long QT Syndrome diagnosis using machine learningon ECG characteristics
2018
Introduction: Congenital long QT syndrome (LQTS) is a genetic disorder affecting cardiac ion channels
which leads to an increased risk of malignant ventricular arrhythmias and sudden cardiac death [1]. Diagnosing
LQTS remains challenging because of a considerable overlap of the QT-interval between LQTS
patients and healthy controls [2]. Analysis of T-wave morphology has shown to be of discriminative
value to diagnose LQTS [3–6]. An objective diagnostic tool that includes T-wave morphology might
further improve LQTS diagnosis.
Methods: and results A retrospective study was performed on 699 standard ECGs recorded from
patients with LQT1, LQT2 and LQT3 and genotype-negative relatives. T-wave morphology parameters
and subject characteristics were used as inputs to three machine learning models: logistic regression,
bagged random forest and support vector machine. The final best performing support vector machine
showed an area under the curve (AUC) of 0.886, with a maximal sensitivity and specificity of 80% and
84.8%. The receiver operating characteristic (ROC) of a similarly trained model using only QTc values,
age and gender as inputs, showed an AUC of 0.823, with a maximal sensitivity and specificity of 70.7%
and 80%, respectively, to diagnose LQTS.
Conclusion: The proposed model resulted in a major rise in sensitivity and a minor rise in specificity
compared to the current situation and therefore leads to a decrease in LQTS underdiagnosis. External
validation, however, is still necessary to confirm these results.
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