{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T04:23:49Z","timestamp":1773721429301,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program","award":["2021YFC 2400203"],"award-info":[{"award-number":["2021YFC 2400203"]}]},{"name":"National Key Research and Development Program","award":["GYQJ-2018-2-05"],"award-info":[{"award-number":["GYQJ-2018-2-05"]}]},{"name":"National Key Research and Development Program","award":["yg2021-38"],"award-info":[{"award-number":["yg2021-38"]}]},{"name":"Shanghai Municipal Science and Economic and Informatization Commission Project","award":["2021YFC 2400203"],"award-info":[{"award-number":["2021YFC 2400203"]}]},{"name":"Shanghai Municipal Science and Economic and Informatization Commission Project","award":["GYQJ-2018-2-05"],"award-info":[{"award-number":["GYQJ-2018-2-05"]}]},{"name":"Shanghai Municipal Science and Economic and Informatization Commission Project","award":["yg2021-38"],"award-info":[{"award-number":["yg2021-38"]}]},{"name":"Medical Engineering Fund of Fudan University","award":["2021YFC 2400203"],"award-info":[{"award-number":["2021YFC 2400203"]}]},{"name":"Medical Engineering Fund of Fudan University","award":["GYQJ-2018-2-05"],"award-info":[{"award-number":["GYQJ-2018-2-05"]}]},{"name":"Medical Engineering Fund of Fudan University","award":["yg2021-38"],"award-info":[{"award-number":["yg2021-38"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Densenet to detect ECG characteristic points, including the onset, peak and termination points of P-wave, QRS complex wave and T-wave. We extracted high-level features of ECG heartbeats from the QT Database (QTDB) and two other larger datasets, MIT-BIH Arrhythmia Database (MITDB) and MIT-BIH Normal Sinus Rhythm Database (NSRDB) with no human-annotated labels as pre-training. By applying different transformations to ECG signals, the task of discriminating signals before and after transformation was defined as the pretext task. Subsequently, the convolutional layer was frozen and the weights of the self-supervised network were transferred to the downstream task of characteristic point localizations on heart beats in the QT dataset. Finally, the mean \u00b1 standard deviation of the detection errors of our proposed self-supervised learning method in QTDB for detecting the onset, peak, and termination points of P-waves, the onset and termination points of QRS waves, and the peak and termination points of T-waves were \u22120.24 \u00b1 10.04, \u22120.48 \u00b1 11.69, \u22120.28 \u00b1 10.19, \u22123.72 \u00b1 8.18, \u22124.12 \u00b1 13.54, \u22120.68 \u00b1 20.42, and 1.34 \u00b1 21.04. The results show that the deep learning network based on the self-supervised framework constructed in this manuscript can accurately detect the feature points of a heartbeat, laying the foundation for automatic extraction of key information related to ECG-based diagnosis.<\/jats:p>","DOI":"10.3390\/e24121828","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:43:49Z","timestamp":1671075829000},"page":"1828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4098-4580","authenticated-orcid":false,"given":"Wenwen","family":"Wu","sequence":"first","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8517-0142","authenticated-orcid":false,"given":"Yanqi","family":"Huang","sequence":"additional","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]},{"given":"Xiaomei","family":"Wu","sequence":"additional","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"},{"name":"Academy for Engineering and Technology, Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200433, China"},{"name":"Yiwu Research Institute of Fudan University, Yiwu 322000, China"},{"name":"Shanghai Engineering Research Center of Assistive Devices, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s12938-019-0630-9","article-title":"Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: A deep-learning approach","volume":"18","author":"Sbrollini","year":"2019","journal-title":"Biomed. 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