{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:40:31Z","timestamp":1781073631910,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T00:00:00Z","timestamp":1683936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Council (NRC) of Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.<\/jats:p>","DOI":"10.3390\/s23104727","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T08:33:01Z","timestamp":1684139581000},"page":"4727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2082-2840","authenticated-orcid":false,"given":"Mehdi","family":"Hazratifard","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vibhav","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fayez","family":"Gebali","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1458-3035","authenticated-orcid":false,"given":"Haytham","family":"Elmiligi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4045-8687","authenticated-orcid":false,"given":"Mohammad","family":"Mamun","sequence":"additional","affiliation":[{"name":"National Research Council of Canada, Government of Canada, Ottawa, ON K1A 0R6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"ref_1","unstructured":"Rowlands, D. (2019). Health Informatics Society of Australia, Australian Institute of Digital Health (AIDH). White Paper."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wiig, S., Aase, K., Billett, S., Canfield, C., R\u00f8ise, O., Nj\u00e5, O., Guise, V., Haraldseid-Driftland, C., Ree, E., and Anderson, J.E. (2020). Defining the boundaries and operational concepts of resilience in the resilience in healthcare research program. BMC Health Serv. Res., 20.","DOI":"10.1186\/s12913-020-05224-3"},{"key":"ref_3","first-page":"29","article-title":"Institutional resilience in healthcare systems","volume":"10","author":"Carthey","year":"2001","journal-title":"BMJ Qual. Saf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1108\/IJOA-09-2021-2960","article-title":"Readiness for sustainable-resilience in healthcare organisations during COVID-19 era","volume":"31","author":"Thomas","year":"2022","journal-title":"Int. J. Organ. Anal."},{"key":"ref_5","unstructured":"Wadhwani, P., and Gankar, S. (2023, March 20). E-Learning Market Size by Technology (Online E-Learning, Learning Management System (LMS), Mobile E-Learning, Rapid E-Learning, Virtual Classroom), By Provider (Service, Content), By Application (Academic [K-12, Higher Education, Vocational Training], Corporate [SMBs, Large Enterprises], Government), Industry Analysis Report, Regional Outlook, Growth Potential, Competitive Market Share & Forecast. pp. 2020\u20132026, 2019. Available online: https:\/\/www.gminsights.com\/industry-analysis\/elearning-market-size."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1109\/TETCI.2021.3131374","article-title":"EDITH: ECG biometrics aided by Deep learning for reliable Individual Authentication","volume":"6","author":"Ibtehaz","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.patrec.2018.03.028","article-title":"Deep-ECG: Convolutional neural networks for ECG biometric recognition","volume":"126","author":"Labati","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.patcog.2004.05.014","article-title":"ECG to identify individuals","volume":"38","author":"Israel","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.gheart.2016.12.003","article-title":"Feasibility of using mobile ECG recording technology to detect atrial fibrillation in low-resource settings","volume":"12","author":"Evans","year":"2017","journal-title":"Glob. Heart"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9232","DOI":"10.1109\/ACCESS.2019.2891817","article-title":"Cancelable ECG biometrics using compressive sensing-generalized likelihood ratio test","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guglielmi, A.V., Muraro, A., Cisotto, G., and Laurenti, N. (2021, January 7\u201311). Information theoretic key agreement protocol based on ECG signals. Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685523"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jelectrocard.2023.03.009","article-title":"Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning","volume":"79","author":"Kashou","year":"2023","journal-title":"J. Electrocardiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1016\/j.bbe.2022.08.004","article-title":"BAED: A secured biometric authentication system using ECG signal based on deep learning techniques","volume":"42","author":"Prakash","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pereira, T.M., Concei\u00e7\u00e3o, R.C., and Sebasti\u00e3o, R. (2022). Initial Study Using Electrocardiogram for Authentication and Identification. Sensors, 22.","DOI":"10.3390\/s22062202"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hazratifard, M., Gebali, F., and Mamun, M. (2022). Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial. Sensors, 22.","DOI":"10.3390\/s22197655"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s40998-018-0154-5","article-title":"Sparse representation using deep learning to classify multi-class complex data","volume":"43","author":"Hashemi","year":"2019","journal-title":"Iran. J. Sci. Technol. Trans. Electr. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, N., Park, H., Lee, G.H., Han, J., Oh, H., and Choi, J.K. (2022, January 21\u201324). Hierarchical User Status Classification for Imbalanced Biometric Data Class. Proceedings of the 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICAIIC54071.2022.9722653"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dong, X., and Shen, J. (2018, January 8\u201314). Triplet loss in siamese network for object tracking. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhong, D., Yang, Y., and Du, X. (2018, January 11\u201312). Palmprint recognition using the Siamese network. Proceedings of the Chinese Conference on Biometric Recognition, Urumqi, China.","DOI":"10.1007\/978-3-319-97909-0_6"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"00368504211029777","DOI":"10.1177\/00368504211029777","article-title":"Machine learning on small size samples: A synthetic knowledge synthesis","volume":"105","author":"Kokol","year":"2022","journal-title":"Sci. Prog."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s42979-020-00211-1","article-title":"Efficacy of imbalanced data handling methods on deep learning for smart homes environments","volume":"1","author":"Hamad","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_23","first-page":"1367","article-title":"Using ECG Signals in Siamese Networks for Authentication in Digital Healthcare Systems","volume":"3","author":"Behrouzi","year":"2022","journal-title":"J. ISSN"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Amritha, V.S., and Aravinth, J. (2020, January 6\u20137). Matcher performance-based score level fusion schemes for multi-modal biometric authentication system. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074446"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ins.2021.01.001","article-title":"PlexNet: A fast and robust ECG biometric system for human recognition","volume":"558","author":"Srivastva","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15555","DOI":"10.1109\/ACCESS.2023.3244651","article-title":"Ecg biometric recognition: Review, system proposal, and benchmark evaluation","volume":"11","author":"Melzi","year":"2023","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Diab, M.O., Seif, A., Sabbah, M., El-Abed, M., and Aloulou, N. (2020). A review on ECG-based biometric authentication systems. Hidden Biom., 17\u201344.","DOI":"10.1007\/978-981-13-0956-4_2"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1049\/iet-bmt.2012.0055","article-title":"Novel fiducial and non-fiducial approaches to electrocardiogram-based biometric systems","volume":"2","author":"Silva","year":"2013","journal-title":"IET Biom."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105338","DOI":"10.1016\/j.compbiomed.2022.105338","article-title":"Deep learning for predicting respiratory rate from biosignals","volume":"144","author":"Kumar","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.future.2019.06.008","article-title":"A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication","volume":"101","author":"Hammad","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s11042-022-12796-1","article-title":"LSTM model for visual speech recognition through facial expressions","volume":"82","author":"Bhaskar","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4725639","DOI":"10.1155\/2022\/4725639","article-title":"Evolving long short-term memory network-based text classification","volume":"2022","author":"Singh","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kim, B.H., and Pyun, J.Y. (2020). ECG identification for personal authentication using LSTM-based deep recurrent neural networks. Sensors, 20.","DOI":"10.3390\/s20113069"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6734","DOI":"10.1038\/s41598-019-42516-z","article-title":"Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network","volume":"9","author":"Zhu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tirado-Martin, P., and Sanchez-Reillo, R. (2021). BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets. Appl. Sci., 11.","DOI":"10.3390\/app11135880"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fard, S.M.H., and Hashemi, S. (2017, January 25\u201327). Employing deep learning and sparse representation for data classification. Proceedings of the 2017 Artificial Intelligence and Signal Processing Conference (AISP), Shiraz, Iran.","DOI":"10.1109\/AISP.2017.8324099"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Centeno, M.P., van Moorsel, A., and Castruccio, S. (2017, January 28\u201330). Smartphone continuous authentication using deep learning autoencoders. Proceedings of the 2017 15th Annual Conference on Privacy, Security and Trust (PST), Calgary, AB, Canada.","DOI":"10.1109\/PST.2017.00026"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1007\/s40846-021-00637-9","article-title":"An ECG-based authentication system using Siamese neural networks","volume":"41","author":"Ivanciu","year":"2021","journal-title":"J. Med. Biol. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"189720","DOI":"10.1109\/ACCESS.2020.3031447","article-title":"Review of methods for EEG signal classification and development of new fuzzy classification-based approach","volume":"8","author":"Rabcan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/13102818.2017.1389303","article-title":"Detection and classification of cardiovascular abnormalities using FFT based multi-objective genetic algorithm","volume":"32","author":"Prasad","year":"2018","journal-title":"Biotechnol. Biotechnol. Equip."},{"key":"ref_41","unstructured":"Venkat N, G., Vijay, V.R., Venu, G., and Rao, C. (2020). Cognitive Computing: Theory and Applications, Elsevier."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4727\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:18Z","timestamp":1760124858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,13]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104727"],"URL":"https:\/\/doi.org\/10.3390\/s23104727","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,13]]}}}