{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:16:56Z","timestamp":1766067416457,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61675154"],"award-info":[{"award-number":["61675154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Key Research and Development Program","award":["19YFZCSY00180"],"award-info":[{"award-number":["19YFZCSY00180"]}]},{"name":"Tianjin Major Project for Civil-Military Integration of Science and Technology","award":["18ZXJMTG00260"],"award-info":[{"award-number":["18ZXJMTG00260"]}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Science and Technology Program","doi-asserted-by":"publisher","award":["20YDTPJC01380"],"award-info":[{"award-number":["20YDTPJC01380"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Municipal Special Foundation for Key Cultivation of China","award":["XB202007"],"award-info":[{"award-number":["XB202007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.<\/jats:p>","DOI":"10.3390\/s21186043","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T21:36:58Z","timestamp":1631223418000},"page":"6043","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2870-6171","authenticated-orcid":false,"given":"Hongqiang","family":"Li","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9851-4063","authenticated-orcid":false,"given":"Zhixuan","family":"An","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Shasha","family":"Zuo","sequence":"additional","affiliation":[{"name":"Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China"}]},{"given":"Wei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tiangong University, Tianjin 300387, China"}]},{"given":"Shanshan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, Nankai University, Tianjin 300071, China"}]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Wenchao","family":"Song","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Quanhua","family":"Mao","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Yuxin","family":"Mu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Enbang","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-5499","authenticated-orcid":false,"given":"Juan Daniel Prades","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona (UB), E-08028 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e52","DOI":"10.2196\/jmir.3951","article-title":"Impact of mHealth chronic disease management on treatment adherence and patient outcomes: A systematic review","volume":"17","author":"Hamine","year":"2015","journal-title":"J. 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