{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:03:35Z","timestamp":1773511415907,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Pujiang Program","award":["22PJ1415300"],"award-info":[{"award-number":["22PJ1415300"]}]},{"name":"Shanghai Pujiang Program","award":["2023-K06"],"award-info":[{"award-number":["2023-K06"]}]},{"name":"Shanghai Pujiang Program","award":["62301395"],"award-info":[{"award-number":["62301395"]}]},{"name":"Shanghai Pujiang Program","award":["2023BSHEDZZ177"],"award-info":[{"award-number":["2023BSHEDZZ177"]}]},{"name":"Open research Fund of State Key Laboratory of Digital Medical Engineering","award":["22PJ1415300"],"award-info":[{"award-number":["22PJ1415300"]}]},{"name":"Open research Fund of State Key Laboratory of Digital Medical Engineering","award":["2023-K06"],"award-info":[{"award-number":["2023-K06"]}]},{"name":"Open research Fund of State Key Laboratory of Digital Medical Engineering","award":["62301395"],"award-info":[{"award-number":["62301395"]}]},{"name":"Open research Fund of State Key Laboratory of Digital Medical Engineering","award":["2023BSHEDZZ177"],"award-info":[{"award-number":["2023BSHEDZZ177"]}]},{"name":"National Natural Science Foundation of China","award":["22PJ1415300"],"award-info":[{"award-number":["22PJ1415300"]}]},{"name":"National Natural Science Foundation of China","award":["2023-K06"],"award-info":[{"award-number":["2023-K06"]}]},{"name":"National Natural Science Foundation of China","award":["62301395"],"award-info":[{"award-number":["62301395"]}]},{"name":"National Natural Science Foundation of China","award":["2023BSHEDZZ177"],"award-info":[{"award-number":["2023BSHEDZZ177"]}]},{"name":"Shaanxi Province Postdoctoral Science Foundation","award":["22PJ1415300"],"award-info":[{"award-number":["22PJ1415300"]}]},{"name":"Shaanxi Province Postdoctoral Science Foundation","award":["2023-K06"],"award-info":[{"award-number":["2023-K06"]}]},{"name":"Shaanxi Province Postdoctoral Science Foundation","award":["62301395"],"award-info":[{"award-number":["62301395"]}]},{"name":"Shaanxi Province Postdoctoral Science Foundation","award":["2023BSHEDZZ177"],"award-info":[{"award-number":["2023BSHEDZZ177"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to \u221216 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.<\/jats:p>","DOI":"10.3390\/s24123789","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T12:11:00Z","timestamp":1718107860000},"page":"3789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm"],"prefix":"10.3390","volume":"24","author":[{"given":"Huanqian","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hantao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception, and Image Understanding of Education Ministry of China, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7586-4020","authenticated-orcid":false,"given":"Zhang","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception, and Image Understanding of Education Ministry of China, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"},{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1038\/s41569-020-0354-3","article-title":"The effects of endurance exercise on the heart: Panacea or poison?","volume":"17","author":"Sharma","year":"2020","journal-title":"Nat. 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