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

Accurate and effective cardiovascular disease (CVD) diagnosis is particularly difficult in telemedicine and resource-constrained environments due to traditional multi-lead 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} electrocardiogram. The approach formulates lead relationships as a dynamic graph$G=(V, E)$whose nodesV= I, IIcorrespond to leads and whose edge weightswᵢⱼ(t) ∈ Ecapture time-varying cardiac axis deviation anglesθ (t)in the frontal plane. Three statistical features are obtained mean angleμ _θ, angular varianceσ _θ ², and lead correlation coefficientρ 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 multi-lead ECG. The approach ensures clinical-grade accuracy with$O(1)$feature extraction complexity, providing an optimal trade-off between accuracy and computational efficiency for resource-constrained wearable ECG sensors and tele-ECG applications.