Automated differentiation of wide QRS complex tachycardia using QRS complex polarity

Abstract

Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]). In a three-part study, we derive and validate machine learning (ML) models—logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)—using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. Part 1 uses WCT ECG measurements alone, Part 2 pairs WCT and baseline ECG features, and Part 3 combines all features used in Parts 1 and 2 Among 235 WCT patients (158 SWCT, 77 VT), 103 had gold standard diagnoses. Part 1 models achieved AUCs of 0.86–0.88 using WCT ECG features alone. Part 2 improved accuracy with paired ECGs (AUCs 0.90–0.93). Part 3 showed variable results (AUC 0.72–0.93), with RF and SVM performing best. Incorporating engineered parameters related to QRS polarity direction and shifts can yield effective WCT differentiation, presenting a promising approach for automated CEI algorithms. Wide QRS complex tachycardias (WCTs) are abnormal, rapid heart rhythms that can be dangerous. Differentiating between the two main types, which are ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT), is critical for treatment decisions but remains challenging. An electrocardiogram (ECG) measures the electrical activity of the heart. We used automated ECG measurements to develop computational methods that enhance the accuracy of ECG interpretation. The computational methods, particularly those that analyzed paired ECG recordings, were able to differentiate WCTs with high accuracy. This method could help doctors diagnose heart conditions more reliably, resulting in faster and more precise treatments for patients with abnormal heart rhythms. May et al. propose machine learning algorithms that leverage QRS polarity direction and shifts to differentiate wide QRS complex tachycardias. Strong diagnostic accuracy is demonstrated, particularly when integrating features from both wide QRS tachycardia and baseline electrocardiograms.