{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T01:15:03Z","timestamp":1788052503976,"version":"build-2784847793"},"reference-count":37,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004569","name":"Ministerstwo Edukacji i Nauki","doi-asserted-by":"publisher","award":["SKN\/SP\/534630\/2022"],"award-info":[{"award-number":["SKN\/SP\/534630\/2022"]}],"id":[{"id":"10.13039\/501100004569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007771","name":"Medical University of Silesia in Katowice","doi-asserted-by":"publisher","award":["PCN\u20101\u2010085\/N\/2\/K"],"award-info":[{"award-number":["PCN\u20101\u2010085\/N\/2\/K"]}],"id":[{"id":"10.13039\/100007771","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We used motif and discord extraction techniques, alongside long short\u2010term memory networks, to analyse 12\u2010lead, 10\u2010s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10\u2010fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver\u2010operating characteristic curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 205 patients (mean age 54\u2009\u00b1\u200917\u2009years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91\u20130.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54\u20130.81).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large\u2010scale screening of CAN, particularly to identify definite\/severe CAN where cardiovascular risk factor modification may be initiated.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.15578","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T15:10:16Z","timestamp":1712848216000},"page":"2624-2633","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial intelligence\u2010enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes"],"prefix":"10.1111","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9699-1686","authenticated-orcid":false,"given":"Krzysztof","family":"Irlik","sequence":"first","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool Liverpool John Moores University and Liverpool Heart and Chest Hospital  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