{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:48:20Z","timestamp":1768823300344,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T00:00:00Z","timestamp":1558483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.<\/jats:p>","DOI":"10.3390\/s19102350","type":"journal-article","created":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T03:22:03Z","timestamp":1558581723000},"page":"2350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2818-1010","authenticated-orcid":false,"given":"Mariusz","family":"Pelc","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, ul. Proszkowska 76, 45-758 Opole, Poland"},{"name":"School of Computing and Mathematical Sciences, University of Greenwich, Park Row, London SE10 9LS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4677-5392","authenticated-orcid":false,"given":"Yuriy","family":"Khoma","sequence":"additional","affiliation":[{"name":"Department of Information Measurement Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9391-6525","authenticated-orcid":false,"given":"Volodymyr","family":"Khoma","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, ul. Proszkowska 76, 45-758 Opole, Poland"},{"name":"Department of Information Measurement Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jain, A., Flynn, P., and Ross, A.A. (2008). Handbook of Biometrics, Springer.","DOI":"10.1007\/978-0-387-71041-9"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kindt, E.J. (2013). Privacy and Data Protection Issues of Biometric Applications: A Comparative Legal Analysis, Springer.","DOI":"10.1007\/978-94-007-7522-0"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-015-0072-y","article-title":"Individual identification via electrocardiogram analysis","volume":"14","author":"Fratini","year":"2015","journal-title":"Biomed. Eng. 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