{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:04:03Z","timestamp":1784307843042,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["2021C03027"],"award-info":[{"award-number":["2021C03027"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ21F010016"],"award-info":[{"award-number":["LQ21F010016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.<\/jats:p>","DOI":"10.3390\/s21165290","type":"journal-article","created":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T09:35:32Z","timestamp":1628156132000},"page":"5290","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-6918-4088","authenticated-orcid":false,"given":"Huaiyu","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0763-3658","authenticated-orcid":false,"given":"Yisheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-9335-4291","authenticated-orcid":false,"given":"Yun","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanshuang","family":"Xie","sequence":"additional","affiliation":[{"name":"R&D Department, Hangzhou Proton Technology Co., Ltd., Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"R&D Department, Hangzhou Proton Technology Co., Ltd., Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2555-343X","authenticated-orcid":false,"given":"Ruohong","family":"Huan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16529","DOI":"10.1109\/ACCESS.2018.2807700","article-title":"An Automatic Cardiac Arrhythmia Classification System with Wearable Electrocardiogram","volume":"6","author":"Xia","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. 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