{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T16:38:36Z","timestamp":1784824716916,"version":"3.55.0"},"reference-count":133,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the eCareWell project"},{"name":"HM Treasury"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cardiovascular disease (CVD) is the world\u2019s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.<\/jats:p>","DOI":"10.3390\/s22208002","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"8002","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":154,"title":["Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3519-0559","authenticated-orcid":false,"given":"Jian-Dong","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4043-763X","authenticated-orcid":false,"given":"Elaine","family":"Ramsey","sequence":"additional","affiliation":[{"name":"Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8411-8919","authenticated-orcid":false,"given":"Gerard","family":"Leavey","sequence":"additional","affiliation":[{"name":"School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy J. A.","family":"Chico","sequence":"additional","affiliation":[{"name":"Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3517-2182","authenticated-orcid":false,"given":"Joan","family":"Condell","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Badnjevi\u0107, A., Pokvi\u0107, L.G., and Spahi\u0107, L. (2020). Cardiovascular techniques and technology. 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