{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T13:11:52Z","timestamp":1783516312013,"version":"3.55.0"},"reference-count":56,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:00:00Z","timestamp":1606348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1\u20132% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.<\/jats:p>","DOI":"10.3390\/info11120549","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T09:04:15Z","timestamp":1606381455000},"page":"549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Detection of Atrial Fibrillation Using a Machine Learning Approach"],"prefix":"10.3390","volume":"11","author":[{"given":"Sidrah","family":"Liaqat","sequence":"first","affiliation":[{"name":"School of Engineering and Computing, University of the West of Scotland, Glasgow G72 0LH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9651-6487","authenticated-orcid":false,"given":"Kia","family":"Dashtipour","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adnan","family":"Zahid","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"},{"name":"School of Engineering and Physical Science, Heriot-Watt University, Edinburgh EH14 4AS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khaled","family":"Assaleh","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and IT, Ajman University, Ajman 346, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamran","family":"Arshad","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and IT, Ajman University, Ajman 346, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Engineering and Computing, University of the West of Scotland, Glasgow G72 0LH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shen, M., Zhang, L., Luo, X., and Xu, J. 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