{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:05:42Z","timestamp":1779253542839,"version":"3.51.4"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.compbiomed.2026.111616","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T17:18:01Z","timestamp":1773163081000},"page":"111616","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Interpretable evaluation of physiological signals for biometric identification"],"prefix":"10.1016","volume":"206","author":[{"given":"Vithurabiman","family":"Senthuran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-8174","authenticated-orcid":false,"given":"Uthayasanker","family":"Thayasivam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iynkaran","family":"Natgunanathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keshav","family":"Sood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2026.111616_bib0005","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s13755-024-00306-6","article-title":"Explainable federated learning scheme for secure healthcare data sharing","volume":"12","author":"Zhao","year":"2024","journal-title":"Health Inf. Sci. Syst."},{"issue":"4","key":"10.1016\/j.compbiomed.2026.111616_bib0010","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.icte.2022.03.006","article-title":"Human activity recognition based on wrist ppg via the ensemble method","volume":"8","author":"Almanifi","year":"2022","journal-title":"ICT Express"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106005","article-title":"Ecg-based biometric under different psychological stress states","volume":"202","author":"Zhou","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106189","article-title":"Electroencephalography-based recognition of six basic emotions in virtual reality environments","volume":"93","author":"Xie","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"issue":"6, Part B","key":"10.1016\/j.compbiomed.2026.111616_bib0025","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1016\/j.jksuci.2022.04.012","article-title":"A comparison of emotion recognition system using electrocardiogram (ECG) and photoplethysmogram (ppg)","volume":"34","author":"Sayed Ismail","year":"2022","journal-title":"J. King Saud Univ. - Comput. Inf. Sci."},{"issue":"10","key":"10.1016\/j.compbiomed.2026.111616_bib0030","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3390\/bios12100811","article-title":"Emotion recognition: photoplethysmography and electrocardiography in comparison","volume":"12","author":"Rinella","year":"2022","journal-title":"Biosensors"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0035","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s13755-022-00192-w","article-title":"A multi-label classification system for anomaly classification in electrocardiogram","volume":"10","author":"Li","year":"2022","journal-title":"Health Inf. Sci. Syst."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0040","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2023.1229743","article-title":"Machine learning-based detection of cardiovascular disease using ECG signals: performance VS. Complexity","volume":"10","author":"Pham","year":"2023","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2021.104536","article-title":"A novel ECG diagnostic system for the detection of 13 different diseases","volume":"107","author":"Monedero","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105654","article-title":"Biometric recognition based on scalable end-to-end convolutional neural network using photoplethysmography: a comparative study","volume":"147","author":"Wang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102226","article-title":"ECG based biometric identification using one-dimensional local difference pattern","volume":"64","author":"Benouis","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0060","doi-asserted-by":"crossref","first-page":"123069","DOI":"10.1109\/ACCESS.2019.2937357","article-title":"An enhanced electrocardiogram biometric authentication system using machine learning","volume":"7","author":"Al Alkeem","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.compbiomed.2026.111616_bib0065","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.18280\/ts.400517","article-title":"High-dimension EEG biometric authentication leveraging sub-band cube-code representation","volume":"40","author":"Esener","year":"2023","journal-title":"Traitement Signal"},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111616_bib0070","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1038\/s41598-022-06527-7","article-title":"Towards a universal and privacy preserving eeg-based authentication system","volume":"12","author":"Bidgoly","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0075","series-title":"Proceedings of the 41st International Conference on Machine Learning","article-title":"Scaling laws for the value of individual data points in machine learning","author":"Covert","year":"2024"},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111616_bib0080","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s12859-023-05156-9","article-title":"Evaluation of a decided sample size in machine learning applications","volume":"24","author":"Rajput","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0085","series-title":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","first-page":"1423","article-title":"Cardiovascular health management compliance with health insurance portability and accountability act","author":"Tasnim","year":"2023"},{"issue":"2","key":"10.1016\/j.compbiomed.2026.111616_bib0090","article-title":"Covid-19: putting the general data protection regulation to the test","volume":"6","author":"McLennan","year":"2020","journal-title":"J.M.I.R. Public Health Surveill."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0095","series-title":"2013 European Intelligence and Security Informatics Conference","first-page":"159","article-title":"Physiological signals: the next generation authentication and identification methods!?","author":"van den Broek","year":"2013"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0100","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.procs.2020.08.044","article-title":"SVM for human identification using the ECG signal","volume":"176","author":"Hamza","year":"2020","journal-title":"Proc. Comput. Sci."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0105","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.3390\/app12052692","article-title":"Personal identification using an ensemble approach of 1d-lstm and 2d-cnn with electrocardiogram signals","volume":"12","author":"Lee","year":"2022","journal-title":"Appl. Sci."},{"issue":"3","key":"10.1016\/j.compbiomed.2026.111616_bib0110","doi-asserted-by":"crossref","first-page":"439","DOI":"10.3390\/diagnostics13030439","article-title":"Electrocardiogram (ECG)-based user authentication using deep learning algorithms","volume":"13","author":"Agrawal","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0115","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/7997509","article-title":"Broken heart: privacy leakage analysis on ecg-based authentication schemes","volume":"2022","author":"Noh","year":"2022","journal-title":"Secur. Commun. Netw."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0120","doi-asserted-by":"crossref","first-page":"41352","DOI":"10.1109\/ACCESS.2022.3167667","article-title":"Evaluation of ppg feature values toward biometric authentication against presentation attacks","volume":"10","author":"Hinatsu","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105609","article-title":"Pulseid: multi-scale photoplethysmographic identification using a deep convolutional neural network","volume":"88","author":"Wei","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0130","first-page":"1","article-title":"Pulseoblivion: an effective session-based continuous authentication scheme using ppg signals","volume":"PP","author":"Aly","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0135","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eswa.2019.01.080","article-title":"Eeg-based user identification system using 1d-convolutional long short-term memory neural networks","volume":"125","author":"Sun","year":"2019","journal-title":"Expert Syst. Appl."},{"issue":"2531","key":"10.1016\/j.compbiomed.2026.111616_bib0140","article-title":"Towards a universal and privacy preserving eeg-based authentication system","volume":"12","author":"Bidgoly","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0145","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0150","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/5229576","article-title":"Review on eeg-based authentication technology","volume":"2021","author":"Zhang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0155","series-title":"2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","first-page":"4345","article-title":"ECG and EEG based multimodal biometrics for human identification","author":"Bashar","year":"2018"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105022","article-title":"Multimodal biometric authentication method by federated learning","volume":"85","author":"Coelho","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0165","series-title":"Proceedings of the 2nd International Conference on Digital Tools & Uses Congress","article-title":"A multimodal biometric identification system based on ECG and ppg signals","author":"Yaacoubi","year":"2020"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0170","series-title":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"Ecg-guided individual identification via ppg","author":"Wei","year":"2025"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0175","series-title":"International Conference on Machine Learning","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0180","series-title":"2025 IEEE Symposium on Computers and Communications (ISCC)","first-page":"1","article-title":"Enhancing biometric security with multimodal EEG and ppg identification","author":"Trist\u00e3o","year":"2025"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0185","series-title":"Proceedings of 3rd International Conference on Document Analysis and Recognition","first-page":"278","article-title":"Random decision forests","volume":"vol. 1","author":"Ho","year":"1995"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0190","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0195","first-page":"3146","article-title":"Lightgbm: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0200","series-title":"Advances in Neural Information Processing Systems","article-title":"Practical Bayesian optimization of machine learning algorithms","volume":"vol. 25","author":"Snoek","year":"2012"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0205","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"issue":"3","key":"10.1016\/j.compbiomed.2026.111616_bib0210","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the mit-bih arrhythmia database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0215","author":"Karlen"},{"issue":"8","key":"10.1016\/j.compbiomed.2026.111616_bib0220","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1109\/TBME.2016.2613124","article-title":"Toward a robust estimation of respiratory rate from pulse oximeters","volume":"64","author":"Pimentel","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"10.1016\/j.compbiomed.2026.111616_bib0225","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"Bci2000: a general-purpose brain-computer interface (BCI) system","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0230","article-title":"A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG","volume":"13","author":"Merdjanovska","year":"2023","journal-title":"Scientific Rep."},{"key":"10.1016\/j.compbiomed.2026.111616_bib0235","series-title":"Computing in Cardiology (CinC)","doi-asserted-by":"crossref","first-page":"1","DOI":"10.22489\/CinC.2025.350","article-title":"Peakwise correlation pulse detector: a novel method for noise-resilient peak detection in PPG signals","volume":"vol. 52","author":"Chierico","year":"2025"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0240","series-title":"2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","first-page":"7250","article-title":"Investigation of window size in classification of eeg-emotion signal with wavelet entropy and support vector machine","author":"Candra","year":"2015"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0245","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1135","article-title":"\u201cwhy should i trust you?\u201d explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.compbiomed.2026.111616_bib0250","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":"10.1016\/j.compbiomed.2026.111616_bib0255","series-title":"2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","first-page":"429","article-title":"Non-fiducial ppg-based authentication for healthcare application","author":"Karimian","year":"2017"},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111616_bib0260","doi-asserted-by":"crossref","DOI":"10.3390\/s23010186","article-title":"EEG authentication system based on one- and multi-class machine learning classifiers","volume":"23","author":"Hern\u00e1ndez-\u00c1lvarez","year":"2023","journal-title":"Sensors"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526001794?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526001794?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:06:48Z","timestamp":1779250008000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482526001794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":52,"alternative-id":["S0010482526001794"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111616","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable evaluation of physiological signals for biometric identification","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111616","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111616"}}