{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T07:18:26Z","timestamp":1787815106230,"version":"build-2784847793"},"reference-count":29,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"tdm","delay-in-days":26,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2013fog\u2013cloud 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.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae995d","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T22:47:51Z","timestamp":1786661271000},"page":"045073","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Resource-aware continuous ECG monitoring with selective cloud escalation and exact decision reuse"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3614-5846","authenticated-orcid":true,"given":"Mohammed","family":"Chaouqi","sequence":"first","affiliation":[{"name":"E2SN, ENSAM, Mohammed V University in Rabat"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4687-0066","authenticated-orcid":false,"given":"Latifa","family":"Doudach","sequence":"additional","affiliation":[{"name":"E2SN, ENSAM, Mohammed V University in Rabat"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7939-0790","authenticated-orcid":false,"given":"Achraf","family":"Benba","sequence":"additional","affiliation":[{"name":"E2SN, ENSAM, Mohammed V University in Rabat"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"bpexae995dbib1","article-title":"Cardiovascular diseases (CVDs) WHO fact sheet","author":"World Health Organization","year":"2025","type":"other"},{"key":"bpexae995dbib2","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.dcan.2024.11.013","type":"journal-article","article-title":"A survey of internet of medical things: technology, application and future directions","volume":"12","author":"He","year":"2026","journal-title":"Digit. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-04-04","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-13","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-27","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}