Background and objectives: Junctional Ectopic Tachycardia (JET) is a tachyarrhythmia most commonly observed in infants and children in the postoperative setting. An automatic detection algorithm could be valuable for early identification and timely treatment of JET. However, the detection is challenging as the initial changes on the electrocardiogram (ECG) are often subtle and monitor data commonly contains substantial noise and artefacts. The objective of this study was to investigate which features contribute to accurate JET detection and to develop an automated detection model.

Methods: A retrospective study was conducted using monitor ECG data of paediatric patients admitted to the Paediatric Intensive Care Unit. The training set consisted of 17 patients, and the test set of 8 patients. ECG metrics were detected, in order to segment the signal and to derive several features. The two-dimensional vectorcardiogram (VCG) was computed for calculating features representing the beat-to-beat variability of the signal. Automatic feature selection methods were applied to identify which features most effectively differentiate JET from sinus rhythm (SR), based on balanced accuracy. Logistic regression (LR) and random forest (RF) models were finally created and performance was validated.

Results: The LR and RF models achieved balanced accuracy scores of 0,989 and 0,988, respectively, on the training dataset. The selected features included the number of P waves and the variance of the PQ interval. For the RF model, the standard deviation (SD) of the RR interval was also selected. VCG-features did not prove effective in distinguishing JET from SR. A secondary validation on the test set yielded lower scores of 0,899 and 0,892. An analysis of misclassifications revealed that they were all attributed to errors in peak detection, which occurred in cases of deviating ECG morphologies or the presence of artefacts and noise.

Conclusions: This study demonstrates that P wave-related features are most effective for distinguishing JET from SR, with simple machine learning models based on these features showing promising results for automated JET detection. Peak detection is currently the most important limiting factor for the robustness and generalisability of this method. Interpatient variability and the low quality of monitor ECG data remain important challenges. Expanding the dataset, improving the data quality and implementing signal quality assessment (SQA) methods are recommended to improve the robustness of the models.