Resource-aware continuous ECG monitoring with selective cloud escalation and exact decision reuse.

Continuous electrocardiogram (ECG) monitoring requires practical engineering solutions that balance predictive performance, response time, and resource consumption in distributed wearable-oriented systems. We present a wearable-fog-cloud framework for continuous ECG decision orchestration in which wearable devices perform first-pass triage and the fog gateway evaluates patient-specific temporal criticality, uncertainty and resource admissibility, reserving cloud consultation for ambiguous or critical cases. The method explicitly distinguishes logical cloud demand from actual cloud execution and incorporates exact record-level decision memoization for repeated beat-level tuples derived from the same ECG record. The framework was evaluated in iFogSim as a controlled system-level simulation using synchronized wearable-side and cloud-side prediction streams derived from PTB-XL. We compared the proposed method with three baselines: wearable-only, cloud-only, and no-reuse. We also assessed scalability, parameter sensitivity, cache realism, component-level ablations, and bootstrap uncertainty at the record and patient levels. At the selected operating point, the method achieved a Matthews correlation coefficient of 0.6073, an F1-score of 0.8255, and an area under the receiver operating characteristic curve of 0.8803, with an average latency of 27.9 ms and an actual cloud offload ratio of 6.13%. Among logically cloud-resolved tuples, 84.66% were served through exact record-level cache reuse. The proposed framework improved predictive performance compared with wearable-only inference while maintaining low latency; compared with the no-reuse configuration, it achieved higher predictive performance with slightly lower real cloud execution. These results indicate that selective cloud escalation and exact record-level decision reuse can improve the trade-off between predictive quality and resource usage in a controlled ECG decision-orchestration framework, while prospective validation on native wearable or ambulatory ECG data remains necessary.