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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.<\/jats:p>","DOI":"10.1038\/s41746-023-00869-w","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T03:02:22Z","timestamp":1689044542000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3812-3260","authenticated-orcid":false,"given":"Akshay","family":"Khunte","sequence":"first","affiliation":[]},{"given":"Veer","family":"Sangha","sequence":"additional","affiliation":[]},{"given":"Evangelos K.","family":"Oikonomou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5664-4126","authenticated-orcid":false,"given":"Lovedeep S.","family":"Dhingra","sequence":"additional","affiliation":[]},{"given":"Arya","family":"Aminorroaya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2655-2095","authenticated-orcid":false,"given":"Bobak J.","family":"Mortazavi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5243-552X","authenticated-orcid":false,"given":"Andreas","family":"Coppi","sequence":"additional","affiliation":[]},{"given":"Cynthia A.","family":"Brandt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2046-127X","authenticated-orcid":false,"given":"Harlan M.","family":"Krumholz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9467-6199","authenticated-orcid":false,"given":"Rohan","family":"Khera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"869_CR1","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1161\/01.CIR.0000085166.44904.79","volume":"108","author":"TJ Wang","year":"2003","unstructured":"Wang, T. 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V.S. and R.K. are the coinventors of U.S. Pending Patent Application No. 63\/346,610, \u201cArticles and methods for format independent detection of hidden cardiovascular disease from printed electrocardiographic images using deep-learning\u201d. E.K.O. and R.K. are the coinventors of U.S. Provisional Patent Application No. 63\/177,117 (unrelated to current work) and are co-founders of Evidence2Health, a precision health platform for evidence-based care. B.J.M. reported receiving grants from the National Institute of Biomedical Imaging and Bioengineering, National Heart, Lung, and Blood Institute, US Food and Drug Administration, and the US Department of Defense Advanced Research Projects Agency outside the submitted work; in addition, B.J.M. has a pending patent on predictive models using electronic health records (US20180315507A1). H.M.K. works under contract with the Centers for Medicare & Medicaid Services to support quality measurement programs, was a recipient of a research grant from Johnson & Johnson, through Yale University, to support clinical trial data sharing; was a recipient of a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; receives payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation, from the Martin Baughman Law Firm for work related to the Cook Celect IVC filter litigation, and from the Siegfried and Jensen Law Firm for work related to Vioxx litigation; chairs a Cardiac Scientific Advisory Board for UnitedHealth; was a member of the IBM Watson Health Life Sciences Board; is a member of the Advisory Board for Element Science, the Advisory Board for Facebook, and the Physician Advisory Board for Aetna; and is the co-founder of Hugo Health, a personal health information platform, and co-founder of Refactor Health, a healthcare AI-augmented data management company. R.K. is an associate editor at JAMA, and received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award, 2022060). He also receives research support, through Yale, from Bristol-Myers Squibb and Novo Nordisk.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"124"}}