The trustworthiness of medical AI systems is often undermined by their potential to violate established clinical principles, a critical issue in self-supervised learning (SSL) frameworks where explicit domain knowledge is typically disregarded. To address this trust deficit, we propose the Logic-Constrained Joint Embedding Predictive Architecture (LC-JEPA). LC-JEPA is a novel framework that bridges neural and symbolic learning by seamlessly integrating predefined medical rules into the SSL process via differentiable logic programming. It utilizes a dedicated Differentiable Constraint Satisfaction Layer (D-CSL) to formally encode clinical constraints, enabling end-to-end joint optimization of both predictive accuracy and logical consistency. We introduce the Constraint Satisfaction Rate (CSR) as a specialized metric to quantify adherence to medical knowledge. Comprehensive evaluation on the MIT-BIH Arrhythmia Database demonstrates that LC-JEPA achieves high accuracy (97.5%) while significantly improving domain adherence (CSR of 93.9%), substantially outperforming unconstrained baselines (e.g., Vanilla JEPA’s 86.4% CSR). Extensive sensitivity analysis confirms that the logic constraint is essential: removing it causes accuracy to drop from 97.5% to 32.1%, while the specific constraint weight λ ∈ [0.1,2.0] has minimal impact on performance. By successfully uniting representation learning with symbolic reasoning, LC-JEPA provides a robust and generalizable solution for building safer, more reliable, and clinically deployable trustworthy AI systems for ECG arrhythmia analysis.