CARDIA-X: Global Semantic Transition and Rough-Set Rules for Auditable Post Hoc Electrocardiographic Explainability

Deep electrocardiogram (ECG) classifiers can achieve strong predictive performance, yet their latent evidence remains difficult to audit, and explanation pipelines can become misleading when semantic contracts or label provenance fail. In this work, we propose CARDIA-X, an electrocardiographic instantiation of the global semantic transition and rough-set rule sequence that couples a versioned 52-target semantic contract with evidence-gated class eligibility, separated primary and external branches, and end-to-end provenance controls. After correcting the compensatory-pause ratio to be nonnegative and unbounded above, patient-grouped development reconstruction achieved ratio-specific mean absolute errors of 0.2394 out of fold and 0.231 on validation. The frozen internal evaluation contained 2692 records from 1599 patients but no atrial-fibrillation-positive or atrial-flutter-positive exported labels; audit traced this to an upstream label-export discrepancy, so atrial fibrillation discrimination could not be estimated and no production rules or inference-route claims became eligible. External Lobachevsky University Database (LUDB) R-peak validation achieved an F1 score of 0.916, while single-clinician agreement on archived explanation displays reached Cohen’s kappa 0.683. CARDIA-X therefore currently supports reproducible research auditing while providing a foundation for future clinical validation, potential deployment, and evaluation of patient benefit.