{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T13:00:27Z","timestamp":1781787627597,"version":"3.54.5"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,17]],"date-time":"2023-12-17T00:00:00Z","timestamp":1702771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"COST (European Cooperation in Science and Technology)","award":["CA22137"],"award-info":[{"award-number":["CA22137"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.<\/jats:p>","DOI":"10.3390\/s23249878","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6472-2254","authenticated-orcid":false,"given":"Ana","family":"Minic","sequence":"first","affiliation":[{"name":"Teacher Education Faculty, University of Pristina in Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9402-7391","authenticated-orcid":false,"given":"Luka","family":"Jovanovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-924X","authenticated-orcid":false,"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5917-1857","authenticated-orcid":false,"given":"Catalin","family":"Stoean","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4351-068X","authenticated-orcid":false,"given":"Miodrag","family":"Zivkovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6867-7259","authenticated-orcid":false,"given":"Petar","family":"Spalevic","sequence":"additional","affiliation":[{"name":"Faculty of Technical Science, University of Pristina in Kosovska Mitrovica, Filipa Visnjica bb, 38220 Kosovska Mitrovica, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3324-3909","authenticated-orcid":false,"given":"Aleksandar","family":"Petrovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3798-312X","authenticated-orcid":false,"given":"Milos","family":"Dobrojevic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9849-5712","authenticated-orcid":false,"given":"Ruxandra","family":"Stoean","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Cardiovascular disease as a leading cause of death: How are pharmacists getting involved?","volume":"8","author":"Alzubaidi","year":"2019","journal-title":"Integr. 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