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.

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.

Background: Solutions targeting early recognition of congestion in heart failure (HF) patients have the potential to prevent readmissions and can thus significantly reduce the burden on HF care. The gold standard measure of congestion is invasively measured pulmonary capillary wedge pressure (PCWP). However, the invasive nature and accessibility of this measurement limits its clinical use. Non-invasive approximation of the PCWP using biosensing wearables could be a promising replacement for HF monitoring.
Purpose: The primary aim of this retrospective study was to create a model that estimates the PCWP based on non-invasive measurements of vital signs using both traditional statistics and machine learning (ML) techniques.

Methods: The study cohort comprised right-sided heart catheterisations between 23/6/2017 and 19/8/2022 performed in the Erasmus MC, Rotterdam, The Netherlands. The following models were used: linear regression or classification, k-nearest neighbours, random forest, gradient boosting, and multilayer perceptron. The outcome measure for the regression models was the continuous PCWP as measured during the catheterisation. The two outcome classes for the classification models were low (<12 mmHg) and high (≥12 mmHg) PCWP. Non-invasive mean arterial blood pressure (MAP), saturation, heart rate, weight and temperature measured at most 72 hours before or after the catheterisation were collected as the features for the models, as well as the age and gender of the patient. Additionally, ECG-signals acquired during the catheterisation were used to calculate the heart rate variability (HRV). The data was split into a validation (20%) and training (80%) data set. The models were built based on the training set and then applied on the validation set to determine the coefficients of determination (R2) for the regression models and the area under the curve (AUC) for the classification models.

Results: A total of 853 catheterisation patients were included of which 31.3% had HF as primary diagnosis and 48.7% had a PCWP of 12 mmHg or higher. The average age of the cohort was 58.9 ± 13.7 years and 52.1% were male. The HRV had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.11 and the classification models in AUC values of up to 0.59.

Conclusion: In the current study, PCWP could not be approximated with non-invasive measurements using traditional statistics and ML techniques. These findings support the notion that traditional measures for monitoring HF are poorly correlated with hemodynamic parameters. Perhaps repeated measurements over time, e.g. trends, or continuously measured signals such as photoplethysmography (PPG) could overcome the shortcomings of a single vital signs measurement for congestion evaluation. Therefore, future prospective research is needed to evaluate the potential of wearable devices measuring trends based on PPG signals in predicting hemodynamics.