A Clinically Aligned B-Bio Framework with BVidAl Algorithms for Robust and Explainable ECG Interpretation

Conventional electrocardiogram (ECG) interpretation systems remain largely waveform-centric and weakly constrained by physiology, limiting explainability, robustness, and clinical trust. This work introduces a clinically aligned Bansal-Bio(B-Bio) framework which is boundary-biological based on stratified algebraic geometry and its deterministic realization through BVidAl algorithms. ECG interpretation is reformulated as constrained physiological state inference on a Bansal manifold, whose fibers are algebra-valued rather than vector-valued. Clinical electrophysiology concepts are drawn from established medical literature, while novelty lies in the multidisciplinary engineering integration of Bansal space, Bansal algebra, Bansal tensor constraints, Bansal Hamiltonian dynamics, Bansal Bansal Ricci flow-based disease modeling, and geometry-aware state estimation. Cardiac diseases are represented as persistent geometric deformations, instabilities, or curvature accumulation in physiological state space rather than as discrete labels. The resulting system provides deterministic, explainable, auditable, and SaMD-aligned ECG interpretation suitable for cardiology and medical informatics applications.The proposed system emphasizes physiological plausibility, deterministic behavior, and explainability over unconstrained accuracy optimization.