Implicit Graph-Based Cardiovascular Disease Detection Using Cardiac Axis Deviation in Reduced-Lead ECG

Abstract

Accurate and effective cardiovascular disease (CVD) diagnosis is particularly difficult in telemedicine and resource-constrained environments due to traditional multilead electrocardiogram (ECG) devices' high computational and operational expenses. We propose a computationally effective graph-based approach to the automated detection of CVD from reduced-lead {I, II} ECG. The approach formulates lead relationships as a dynamic graph $G=(V, E)$ whose nodes $V=\lbrace \text{I}, \text{II}\rbrace$ correspond to leads and whose edge weights $w_{ij}(t) \in E$ capture time-varying cardiac axis deviation angles $\theta (t)$ in the frontal plane. Three statistical features are obtained: mean angle $\mu _\theta$, angular variance $\sigma _\theta ^{2}$, and lead correlation coefficient $\rho _{\text{I, II}}$. Experimental testing on PTB-XL and PTB datasets establishes state-of-the-art performance at 89.2% and 84.1% accuracy, respectively, without redundant computations native to multilead ECG. The approach ensures clinical-grade accuracy with $O(1)$ feature extraction complexity, providing an optimal tradeoff between accuracy and computational efficiency for resource-constrained wearable ECG sensors and tele-ECG applications.