{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:02:27Z","timestamp":1778785347335,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea government (MSIT)","award":["2020-0-00447"],"award-info":[{"award-number":["2020-0-00447"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.<\/jats:p>","DOI":"10.3390\/s21216966","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"6966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8809-1597","authenticated-orcid":false,"given":"Ho Bin","family":"Hwang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4120-5999","authenticated-orcid":false,"given":"Hyeokchan","family":"Kwon","sequence":"additional","affiliation":[{"name":"Information Security Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6314-8094","authenticated-orcid":false,"given":"Byungho","family":"Chung","sequence":"additional","affiliation":[{"name":"Information Security Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9884-9672","authenticated-orcid":false,"given":"Jongshill","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9580-7074","authenticated-orcid":false,"given":"In Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jain, A.K., Ross, A.A., and Nandakumar, K. (2011). Introduction to Biometrics, Springer Science & Business Media.","DOI":"10.1007\/978-0-387-77326-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s11760-013-0593-4","article-title":"Biometric authentication based on PCG and ECG signals: Present status and future directions","volume":"8","author":"Ahmed","year":"2014","journal-title":"Signal Image Video Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MCI.2007.353415","article-title":"Technology review\u2014Biometrics-Technology, Application, Challenge, and Computational Intelligence Solutions","volume":"2","author":"Qinghan","year":"2007","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/TIFS.2015.2480381","article-title":"What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics","volume":"11","author":"Dantcheva","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez-Pato, V.M., S\u00e1nchez, C.N., Dom\u00ednguez-Soberanes, J., M\u00e9ndoza-P\u00e9rez, D.E., and Vel\u00e1zquez, R. (2020). A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products. Foods, 9.","DOI":"10.3390\/foods9060774"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1109\/TIFS.2018.2812193","article-title":"Fingerprint Spoof Buster: Use of Minutiae-Centered Patches","volume":"13","author":"Chugh","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gupta, P., Behera, S., Vatsa, M., and Singh, R. (2014, January 24\u201328). On Iris Spoofing Using Print Attack. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.296"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1109\/TIFS.2016.2555286","article-title":"Face Spoofing Detection Using Colour Texture Analysis","volume":"11","author":"Boulkenafet","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tome, P., and Marcel, S. (2015, January 19\u201322). On the vulnerability of palm vein recognition to spoofing attacks. Proceedings of the 2015 International Conference on Biometrics (ICB), Phuket, Thailand.","DOI":"10.1109\/ICB.2015.7139056"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Barros, A., Resque, P., Almeida, J., Mota, R., Oliveira, H., Ros\u00e1rio, D., and Cerqueira, E. (2020). Data Improvement Model Based on ECG Biometric for User Authentication and Identification. Sensors, 20.","DOI":"10.3390\/s20102920"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sodhro, A.H., Sangaiah, A.K., Sodhro, G.H., Lohano, S., and Pirbhulal, S. (2018). An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors, 18.","DOI":"10.3390\/s18030923"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nykvist, C., Larsson, M., Sodhro, A.H., and Gurtov, A. (2020). A lightweight portable intrusion detection communication system for auditing applications. Int. J. Commun. Syst., 33.","DOI":"10.1002\/dac.4327"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jnca.2018.05.007","article-title":"PEA: Parallel electrocardiogram-based authentication for smart healthcare systems","volume":"117","author":"Zhang","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_14","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 Biometrics, Springer.","DOI":"10.1007\/978-981-13-0956-4_2"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.asoc.2017.07.032","article-title":"Real-time electrocardiogram streams for continuous authentication","volume":"68","author":"Camara","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1080\/13102818.2018.1428500","article-title":"ECG-based identity recognition via deterministic learning","volume":"32","author":"Dong","year":"2018","journal-title":"Biotechnol. Biotechnol. Equip."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Paiva, J.S., Dias, D., and Cunha, J.P.S. (2017). Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180942"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s11760-018-1238-4","article-title":"Biometric identification using fingertip electrocardiogram signals","volume":"12","author":"Guven","year":"2018","journal-title":"Signal Image Video Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34746","DOI":"10.1109\/ACCESS.2018.2849870","article-title":"Evolution, Current Challenges, and Future Possibilities in ECG Biometrics","volume":"6","author":"Pinto","year":"2018","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neucom.2020.01.019","article-title":"Toward improving ECG biometric identification using cascaded convolutional neural networks","volume":"391","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TIM.2015.2503863","article-title":"ECG Authentication for Mobile Devices","volume":"65","author":"Osman","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"89","DOI":"10.19101\/IJACR.2019.940129","article-title":"ECG signals for human identification based on fiducial and non-fiducial approaches","volume":"10","author":"Ibrahim","year":"2020","journal-title":"Int. J. Adv. Comput. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/LSP.2016.2531996","article-title":"ECG Authentication System Design Based on Signal Analysis in Mobile and Wearable Devices","volume":"23","author":"Kang","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Choi, G.H., Lim, K., and Pan, S.B. (2021). Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles. Sensors, 21.","DOI":"10.3390\/s21010202"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fatemian, S.Z., Agrafioti, F., and Hatzinakos, D. (2010, January 27\u201329). HeartID: Cardiac biometric recognition. Proceedings of the 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA.","DOI":"10.1109\/BTAS.2010.5634493"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Choi, G.H., Ko, H., Pedrycz, W., Singh, A.K., and Pan, S.B. (2020). Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics. Sensors, 20.","DOI":"10.3390\/s20247130"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1049\/el.2016.4149","article-title":"Optimised band-pass filter to ensure accurate ECG-based identification of exercising human subjects","volume":"53","author":"Nobunaga","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.dsp.2005.12.003","article-title":"Optimal selection of wavelet basis function applied to ECG signal denoising","volume":"16","author":"Singh","year":"2006","journal-title":"Digit. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.sigpro.2019.04.005","article-title":"An iterative wavelet threshold for signal denoising","volume":"162","author":"Bayer","year":"2019","journal-title":"Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TBME.2003.821031","article-title":"A wavelet-based ECG delineator: Evaluation on standard databases","volume":"51","author":"Martinez","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1161\/01.CIR.52.4.570","article-title":"Gradual changes of ECG waveform during and after exercise in normal subjects","volume":"52","author":"Simoons","year":"1975","journal-title":"Circulation"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.tics.2005.10.005","article-title":"Forebrain emotional asymmetry: A neuroanatomical basis?","volume":"9","author":"Craig","year":"2005","journal-title":"Trends Cogn. Sci."},{"key":"ref_34","unstructured":"Lugovaya, T.S. (2005). Biometric Human Identification Based on Electrocardiogram. [Master\u2019s Thesis, Faculty of Computing Technologies and Informatics, Electrotechnical University \u2018LETI\u2019]."},{"key":"ref_35","first-page":"51","article-title":"Point Estimation of the Parameters of Piecewise Regression Models","volume":"25","author":"Hawkins","year":"1976","journal-title":"J. R. Stat. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1109\/TIFS.2011.2162408","article-title":"Electrocardiogram (ECG) Biometric Authentication Using Pulse Active Ratio (PAR)","volume":"6","author":"Safie","year":"2011","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tan, R., and Perkowski, M. (2017). Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach. Sensors, 17.","DOI":"10.3390\/s17020410"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"117853","DOI":"10.1109\/ACCESS.2020.3004464","article-title":"ECG Biometric Authentication: A Comparative Analysis","volume":"8","author":"Ingale","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","unstructured":"Anzai, Y. (2012). Pattern Recognition and Machine Learning, Elsevier."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pinto, J.R., Cardoso, J.S., Louren\u00e7o, A., and Carreiras, C. (2017). Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel. Sensors, 17.","DOI":"10.3390\/s17102228"},{"key":"ref_41","first-page":"81","article-title":"Variation of QRS amplitude in exercise ECG as an index predicting result of physical training in patients with coronary heart disease","volume":"1","author":"Kentala","year":"1973","journal-title":"Acta Med. Scand."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/0002-8703(66)90211-0","article-title":"The configuration of the P wave during mild exercise","volume":"71","author":"Irisawa","year":"1966","journal-title":"Am. Heart J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1046\/j.1460-9592.2002.01230.x","article-title":"Quantification of T Wave Shape Changes Following Exercise","volume":"25","author":"Langley","year":"2002","journal-title":"Pacing Clin. Electrophysiol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kim, J., Yang, G., Kim, J., Lee, S., Kim, K.K., and Park, C. (2021). Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning. Sensors, 21.","DOI":"10.3390\/s21051568"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6966\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:19:27Z","timestamp":1760167167000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,20]]},"references-count":44,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21216966"],"URL":"https:\/\/doi.org\/10.3390\/s21216966","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,20]]}}}