Multi-expert ensemble ECG diagnostic algorithm using mutually exclusive-symbiotic correlation between 254 hierarchical multiple labels

Multi-expert ensemble ECG diagnostic algorithm using mutually exclusive-symbiotic correlation between 254 hierarchical multiple labels | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-expert ensemble ECG diagnostic algorithm using mutually exclusive-symbiotic correlation between 254 hierarchical multiple labels Wei Yang, Jiewei Lai, Yue Zhang, Chenyu Zhao, Jinliang Wang, Yong Yan, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3845014/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Electrocardiograms (ECGs) are a cheap and convenient means of assessing heart health, and provide an important basis for diagnosis and treatment by cardiologists. However, existing intelligent ECG diagnostic approaches can only detect up to several tens of ECG terms, which barely cover the most common arrhythmias. Thus, further diagnosis is required by cardiologists in clinical settings. This paper describes the development of a multi-expert ensemble learning model that can recognize 254 ECG terms. Based on data from 191,804 wearable 12-lead ECGs, mutually exclusive-symbiotic correlations between hierarchical multiple labels are applied at the loss level to improve the diagnostic performance of the model and make its predictions more reasonable, while alleviating the difficulty of class imbalance. The model achieves an average area under the receiver operating characteristics curve of 0.973 and 0.956 on offline and online test sets, respectively. We select 130 terms from the 254 available for clinical settings by considering the classification performance and clinical significance, providing real-time and comprehensive ancillary support for the public. Health sciences/Cardiology Health sciences/Health care Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2024 Reviews received at journal 25 Feb, 2024 Reviewers agreed at journal 05 Feb, 2024 Reviewers agreed at journal 29 Jan, 2024 Reviewers agreed at journal 11 Jan, 2024 Reviewers invited by journal 10 Jan, 2024 Editor assigned by journal 09 Jan, 2024 Submission checks completed at journal 08 Jan, 2024 First submitted to journal 08 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3845014","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266094528,"identity":"58aef4aa-f339-4db4-b1fe-4011c5d8c393","order_by":0,"name":"Wei 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