Automated electrocardiogram interpretation for the detection of postoperative junctional ectopic tachycardia at the pediatric intensive care unit

Master thesis (2024)

Authors

G.M. Raaijmakers Mechanical Engineering

Contributors

Eris van Twist Erasmus MC (mentor)
Nico Bruining Erasmus MC (mentor)
Marc Sylva Erasmus MC (mentor)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2024 Gini Raaijmakers
More Info
expand_more

Published Date

03-04-2024

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical Engineering

Abstract

Background: Postoperative junctional ectopic tachycardia (JET) is an arrhythmia associated with increased morbidity and mortality rates in children with congenital heart disease. Developing an automated detection algorithm could aid in early identification and timely treatment of JET.

Methods: A retrospective study was conducted using monitor electrocardiogram (ECG) data of pediatric patients who experienced JET during their admission to the pediatric intensive care unit. A manual decision tree was developed that aimed to differentiate between JET and sinus rhythm based on distinctive characteristics. These features were derived using signal analysis on both two-dimensional vectorcardiograms and ECG data. For the latter, ECG metrics were detected in a fictive lead that was created in the direction with the highest amplitudes. Metrics were identified within adaptive intervals that were dependent on ECG morphology rather than relying on fixed time intervals.

Results: A classification performance was achieved with a sensitivity of 96.3%, specificity of 71.4%, positive predictive value (PPV) of 86.7% and an accuracy of 87.8%. R peaks, Q peaks, S peaks, T peaks and P waves were detected with an accuracy of respectively 99.9%, 95.7%, 89.7%, 98.1% and 54.8%. The computational time of the classification of 41 minutes of data was 4 minutes and 48 seconds.

Conclusion: A manual decision tree algorithm for JET detection was developed, using signal analysis for feature extraction based on JET characteristics. This method with a low computational time and a high sensitivity and PPV holds potential for clinical application as a bedside tool. Implementing this proposed algorithm would allow for treatment in an earlier phase, thereby potentially reducing JET associated morbidity and mortality rates.