Automated differentiation of wide QRS complex tachycardia using QRS complex polarity.

ABSTRACT:

Background

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]).

Methods

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),

SUBMITTER: May AM 

PROVIDER: S-EPMC11688452 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature