University of Twente Student Theses

Login

Improving Long QT Syndrome diagnosis using machine learning on ECG characteristics

Stoks, J. (2018) Improving Long QT Syndrome diagnosis using machine learning on ECG characteristics.

[img] PDF
12MB
Abstract: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.
Item Type:Essay (Master)
Clients:
MUMC+, Maastricht, The Netherlands
AMC, Amsterdam, The Netherlands
Faculty:TNW: Science and Technology
Subject:44 medicine, 50 technical science in general
Programme:Technical Medicine MSc (60033)
Link to this item:https://purl.utwente.nl/essays/74982
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page