An explainable machine learning framework for identifying left bundle branch block mechanisms via vectorcardiographic features

Abstract

Left Bundle Branch Block (LBBB) diagnosis is crucial for patient stratification and the selection of individuals who are likely to respond to Cardiac Resynchronization Therapy (CRT). The pathophysiological distinction between LBBB and strict LBBB (sLBBB) is investigated in this research with a view to optimizing diagnostic criteria and therapy. ECG signals were transformed into the vectorcardiographic (VCG) domain, where QRS loops were divided into two halves at the time of the velocity peak computed over the discrete derivates of the x, y, and z leads. From each half, angles and norms were extracted in all VCG planes, along with ratios between VCG peak velocity and VCG fidutial points. These were used to train machine learning models for classification into Healthy, LBBB, and sLBBB categories. The analysis identified four most significant features for the discrimination task: (1,2) peak velocity time relative to QRS onset/offset, (3) maximum norm of the early QRS loop in the frontal plane, and (4) QRS angle in the horizontal plane. These features preserved essential differences in conduction dynamics and electrical disturbances among the three groups. In particular, the time from velocity peak to QRS offset was the most discriminative feature, with progressive prolongation from Healthy to LBBB to sLBBB classes. This reduced 4-feature set achieved an accuracy of 0.85 and an F1-score of 0.83, which was on par with 15-feature-based models. Finally, the integration of explainable artificial intelligence (xAI) into these simplified models enabled the derivation of transparent diagnostic rules for LBBB, improving clinical interpretability on more reliable diagnostic decisions.