{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T19:44:24Z","timestamp":1784490264544,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pacing Clinical Electrophis"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning\u2010based ECG diagnosis system.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>From January 2015 to December 2019, four consecutive years of 100,120 conventional 12\u2010lead ECG data were collected in our hospital. Utilizing this dataset, we constructed a deep learning model designed to intelligently diagnose prevalent ECG anomalies by employing a multi\u2010task learning framework. The system performance was evaluated using various metrics, including sensitivity, specificity, negative predictive value, positive predictive value, and so forth. Additionally, we employed an ECG intelligent diagnostic platform for clinical application to undertake real\u2010time online analysis of 2500 conventional 12\u2010lead ECG samples in June 2020, aiming to validate our model. At this stage, we compared the performance of our model against the traditional manual identification method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The efficacy of the ECG intelligent diagnostic model was notably high for common and straightforward ECG patterns, such as sinus rhythm (F1\u00a0=\u00a098.01%), sinus tachycardia (F1\u00a0=\u00a096.26%), sinus bradycardia (F1\u00a0=\u00a094.88%), and a normal electrocardiogram (F1\u00a0=\u00a091.71%), as well as for Premature Ventricular Contractions (F1\u00a0=\u00a091.62%). Nevertheless, when diagnosing rarer and more intricate ECG anomalies, the system requires an increased number of samples to refine the deep learning models. During the validation stage, our model exhibited better efficiency in terms of accuracy, labor time and labor cost when compared to the manual identification approach.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our deep learning\u2010driven intelligent ECG diagnostic model clearly demonstrates significant clinical utility. The integrated artificial intelligence diagnosis system not only has the potential to augment physicians in their diagnostic processes but also offers a viable avenue to reduce associated labor costs.<\/jats:p><\/jats:sec>","DOI":"10.1111\/pace.14891","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T21:05:26Z","timestamp":1701291926000},"page":"139-148","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning\u2010based regional ECG diagnosis platform"],"prefix":"10.1111","volume":"47","author":[{"given":"Fang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Cardiology The First Affiliated Hospital of Huzhou University  Huzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology The First Affiliated Hospital of Huzhou University  Huzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7479-4984","authenticated-orcid":false,"given":"Xiao","family":"Wang Li","sequence":"additional","affiliation":[{"name":"Department of Cardiology The First Affiliated Hospital of Huzhou University  Huzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.05.013"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-020-01317-x"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-020-00503-2"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103726"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105740"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.09.009"},{"key":"e_1_2_11_8_1","doi-asserted-by":"crossref","unstructured":"SalemM TaheriS YuanJ ECG arrhythmia classification using transfer learning from 2\u2010 Dimensional Deep CNN features.Paper presented at: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS); 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