{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T21:44:07Z","timestamp":1774907047293,"version":"3.50.1"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000070","name":"U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["1R01EB028106"],"award-info":[{"award-number":["1R01EB028106"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["1R01HL151240"],"award-info":[{"award-number":["1R01HL151240"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor\u2019s approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3\u2009\u00b1\u20097.6\u2009mmHg, diastolic: 0.6\u2009\u00b1\u20096.4\u2009mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.<\/jats:p>","DOI":"10.1038\/s41746-023-00853-4","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:01:57Z","timestamp":1686268917000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3862-8616","authenticated-orcid":false,"given":"Kaan","family":"Sel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7558-1706","authenticated-orcid":false,"given":"Amirmohammad","family":"Mohammadi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0320-8600","authenticated-orcid":false,"given":"Roderic I.","family":"Pettigrew","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6358-0458","authenticated-orcid":false,"given":"Roozbeh","family":"Jafari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"853_CR1","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.3390\/app9081526","volume":"9","author":"R Zemouri","year":"2019","unstructured":"Zemouri, R., Zerhouni, N. & Racoceanu, D. Deep learning in the biomedical applications: Recent and future status. Appl. Sci. 9, 1526 (2019).","journal-title":"Appl. Sci."},{"key":"853_CR2","unstructured":"Klabunde, R. Cardiovascular physiology concepts. (Lippincott Williams & Wilkins, 2011)."},{"key":"853_CR3","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1038\/s41565-022-01145-w","volume":"17","author":"D Kireev","year":"2022","unstructured":"Kireev, D. et al. Continuous cuffless monitoring of arterial blood pressure via graphene bioimpedance tattoos. Nat. Nanotechnol. 17, 864\u2013870 (2022).","journal-title":"Nat. Nanotechnol."},{"key":"853_CR4","doi-asserted-by":"publisher","first-page":"e0624","DOI":"10.1097\/CCE.0000000000000624","volume":"4","author":"A Dvir","year":"2022","unstructured":"Dvir, A. et al. Comparing Cardiac Output Measurements Using a Wearable, Wireless, Noninvasive Photoplethysmography-Based Device to Pulse Contour Cardiac Output in the General ICU: A Brief Report. Crit. Care Explor. 4, e0624 (2022).","journal-title":"Crit. Care Explor."},{"key":"853_CR5","doi-asserted-by":"crossref","unstructured":"Nemati, S. et al. Monitoring and detecting atrial fibrillation using wearable technology. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 3394\u20133397 (2016).","DOI":"10.1109\/EMBC.2016.7591456"},{"key":"853_CR6","doi-asserted-by":"crossref","unstructured":"Steinberg, S., Ono, Y., Rajan, S. & Venugopal, S. Continuous artery wall motion tracking using flexible and wearable ultrasonic sensor by signal decomposition. In 2021 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) 1\u20134 (2021).","DOI":"10.1109\/FLEPS51544.2021.9469811"},{"key":"853_CR7","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1021\/acsnano.1c06695","volume":"16","author":"S Baek","year":"2021","unstructured":"Baek, S. et al. Spatiotemporal measurement of arterial pulse waves enabled by wearable active-matrix pressure sensor arrays. ACS Nano. 16, 368\u2013377 (2021).","journal-title":"ACS Nano."},{"key":"853_CR8","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.3390\/s18041084","volume":"18","author":"S Mandal","year":"2018","unstructured":"Mandal, S. & Manasreh, M. O. An in-vitro optical sensor designed to estimate glycated hemoglobin levels. Sensors 18, 1084 (2018).","journal-title":"Sensors"},{"key":"853_CR9","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.3390\/s19081849","volume":"19","author":"YS Can","year":"2019","unstructured":"Can, Y. S., Chalabianloo, N., Ekiz, D. & Ersoy, C. Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors 19, 1849 (2019).","journal-title":"Sensors"},{"key":"853_CR10","doi-asserted-by":"publisher","first-page":"e000070","DOI":"10.1136\/bmjgh-2016-000070","volume":"1","author":"SR Steinhubl","year":"2016","unstructured":"Steinhubl, S. R. et al. Validation of a portable, deployable system for continuous vital sign monitoring using a multiparametric wearable sensor and personalised analytics in an Ebola treatment centre. BMJ Glob. Heal. 1, e000070 (2016).","journal-title":"BMJ Glob. Heal."},{"key":"853_CR11","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/s43856-022-00090-y","volume":"2","author":"Y Gepner","year":"2022","unstructured":"Gepner, Y. et al. Utilizing wearable sensors for continuous and highly-sensitive monitoring of reactions to the BNT162b2 mRNA COVID-19 vaccine. Commun. Med. 2, 27 (2022).","journal-title":"Commun. Med."},{"key":"853_CR12","doi-asserted-by":"publisher","first-page":"e33560","DOI":"10.2196\/33560","volume":"24","author":"V Welch","year":"2022","unstructured":"Welch, V. et al. Use of mobile and wearable artificial intelligence in child and adolescent psychiatry: scoping review. J. Med. Internet Res. 24, e33560 (2022).","journal-title":"J. Med. Internet Res."},{"key":"853_CR13","doi-asserted-by":"publisher","first-page":"4653923","DOI":"10.1155\/2022\/4653923","volume":"2022","author":"F Sabry","year":"2022","unstructured":"Sabry, F., Eltaras, T., Labda, W., Alzoubi, K. & Malluhi, Q. Machine learning for healthcare wearable devices: the big picture. J. Healthc. Eng. 2022, 4653923 (2022).","journal-title":"J. Healthc. Eng."},{"key":"853_CR14","unstructured":"Mohammadi, F. G., Shenavarmasouleh, F. & Arabnia, H. R. Applications of machine learning in healthcare and internet of things (IOT): a comprehensive review. Preprint at https:\/\/arxiv.org\/abs\/2202.02868 (2022)."},{"key":"853_CR15","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1093\/brain\/awab439","volume":"145","author":"AK Bonkhoff","year":"2022","unstructured":"Bonkhoff, A. K. & Grefkes, C. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 145, 457\u2013475 (2022).","journal-title":"Brain"},{"key":"853_CR16","doi-asserted-by":"publisher","first-page":"15413","DOI":"10.1109\/JIOT.2022.3161046","volume":"9","author":"Z Wang","year":"2022","unstructured":"Wang, Z. et al. From personalized medicine to population health: a survey of mHealth sensing techniques. IEEE Internet Things J. 9, 15413\u201315434 (2022).","journal-title":"IEEE Internet Things J."},{"key":"853_CR17","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019).","journal-title":"J. Comput. Phys."},{"key":"853_CR18","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1007\/s10409-021-01148-1","volume":"37","author":"S Cai","year":"2021","unstructured":"Cai, S., Mao, Z., Wang, Z., Yin, M. & Karniadakis, G. E. Physics-informed neural networks (PINNs) for fluid mechanics: A review. Acta Mech. Sin. 37, 1727\u20131738 (2021).","journal-title":"Acta Mech. Sin."},{"key":"853_CR19","doi-asserted-by":"publisher","first-page":"71050","DOI":"10.1109\/ACCESS.2020.2987324","volume":"8","author":"R Rai","year":"2020","unstructured":"Rai, R. & Sahu, C. K. Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus. IEEE Access. 8, 71050\u201371073 (2020).","journal-title":"IEEE Access."},{"key":"853_CR20","doi-asserted-by":"publisher","first-page":"4069","DOI":"10.1109\/TSP.2019.2926023","volume":"67","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Wang, G. & Giannakis, G. B. Real-time power system state estimation and forecasting via deep unrolled neural networks. IEEE Trans. Signal Process 67, 4069\u20134077 (2019).","journal-title":"IEEE Trans. Signal Process"},{"key":"853_CR21","doi-asserted-by":"publisher","first-page":"154104","DOI":"10.1063\/1.4979344","volume":"146","author":"H Wu","year":"2017","unstructured":"Wu, H. et al. Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations. J. Chem. Phys. 146, 154104 (2017).","journal-title":"J. Chem. Phys."},{"key":"853_CR22","doi-asserted-by":"publisher","first-page":"73","DOI":"10.15265\/IY-2016-025","volume":"25","author":"M Schukat","year":"2016","unstructured":"Schukat, M. et al. Unintended consequences of wearable sensor use in healthcare. Yearb. Med. Inform. 25, 73\u201386 (2016).","journal-title":"Yearb. Med. Inform."},{"key":"853_CR23","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1093\/jamia\/ocy082","volume":"25","author":"JP Burnham","year":"2018","unstructured":"Burnham, J. P., Lu, C., Yaeger, L. H., Bailey, T. C. & Kollef, M. H. Using wearable technology to predict health outcomes: a literature review. J. Am. Med. Inform. Assoc. 25, 1221\u20131227 (2018).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"853_CR24","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1109\/RBME.2021.3109643","volume":"15","author":"D Barvik","year":"2021","unstructured":"Barvik, D., Cerny, M., Penhaker, M. & Noury, N. Noninvasive Continuous Blood Pressure Estimation from Pulse Transit Time: A review of the calibration models. IEEE Rev. Biomed. Eng. 15, 138\u2013151 (2021).","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"853_CR25","doi-asserted-by":"publisher","first-page":"83","DOI":"10.33549\/physiolres.931360","volume":"58","author":"L Mourot","year":"2009","unstructured":"Mourot, L., Bouhaddi, M. & Regnard, J. Effects of the cold pressor test on cardiac autonomic control in normal subjects. Physiol. Res. 58, 83\u201391 (2009).","journal-title":"Physiol. Res."},{"key":"853_CR26","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.psyneuen.2018.03.010","volume":"92","author":"L Schwabe","year":"2018","unstructured":"Schwabe, L. & Sch\u00e4chinger, H. Ten years of research with the Socially Evaluated Cold Pressor Test: Data from the past and guidelines for the future. Psychoneuroendocrinology 92, 155\u2013161 (2018).","journal-title":"Psychoneuroendocrinology"},{"key":"853_CR27","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1152\/physiol.00041.2018","volume":"34","author":"AV Gourine","year":"2019","unstructured":"Gourine, A. V. & Ackland, G. L. Cardiac vagus and exercise. Physiology 34, 71\u201380 (2019).","journal-title":"Physiology"},{"key":"853_CR28","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1056\/NEJMoa1803180","volume":"381","author":"AC Flint","year":"2019","unstructured":"Flint, A. C. et al. Effect of systolic and diastolic blood pressure on cardiovascular outcomes. N. Engl. J. Med. 381, 243\u2013251 (2019).","journal-title":"N. Engl. J. Med."},{"key":"853_CR29","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1161\/HYPERTENSIONAHA.120.14742","volume":"76","author":"K Kario","year":"2020","unstructured":"Kario, K. Management of hypertension in the digital era: small wearable monitoring devices for remote blood pressure monitoring. Hypertension 76, 640\u2013650 (2020).","journal-title":"Hypertension"},{"key":"853_CR30","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1016\/j.jacc.2018.07.008","volume":"72","author":"RM Carey","year":"2018","unstructured":"Carey, R. M., Muntner, P., Bosworth, H. B. & Whelton, P. K. Prevention and control of hypertension: JACC health promotion series. J. Am. Coll. Cardiol. 72, 1278\u20131293 (2018).","journal-title":"J. Am. Coll. Cardiol."},{"key":"853_CR31","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/j.jacc.2016.01.037","volume":"67","author":"K Kario","year":"2016","unstructured":"Kario, K. et al. Morning home blood pressure is a strong predictor of coronary artery disease: the HONEST study. J. Am. Coll. Cardiol. 67, 1519\u20131527 (2016).","journal-title":"J. Am. Coll. Cardiol."},{"key":"853_CR32","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.2215\/CJN.03680320","volume":"15","author":"JA Pandit","year":"2020","unstructured":"Pandit, J. A., Lores, E. & Batlle, D. Cuffless blood pressure monitoring: promises and challenges. Clin. J. Am. Soc. Nephrol. 15, 1531\u20131538 (2020).","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"853_CR33","doi-asserted-by":"publisher","first-page":"2","DOI":"10.5617\/jeb.51","volume":"1","author":"DP Bernstein","year":"2010","unstructured":"Bernstein, D. P. Impedance cardiography: Pulsatile blood flow and the biophysical and electrodynamic basis for the stroke volume equations. J. Electr. Bioimpedance 1, 2\u201317 (2010).","journal-title":"J. Electr. Bioimpedance"},{"key":"853_CR34","unstructured":"Grimnes, S. & Martinsen, O. G. Bioimpedance and bioelectricity basics. (Academic press, 2011)."},{"key":"853_CR35","doi-asserted-by":"publisher","first-page":"03TR01","DOI":"10.1088\/1361-6579\/abe80e","volume":"42","author":"G Anand","year":"2021","unstructured":"Anand, G., Yu, Y., Lowe, A. & Kalra, A. Bioimpedance analysis as a tool for hemodynamic monitoring: overview, methods and challenges. Physiol. Meas. 42, 03TR01 (2021).","journal-title":"Physiol. Meas."},{"key":"853_CR36","doi-asserted-by":"publisher","first-page":"e127","DOI":"10.1016\/j.jacc.2017.11.006","volume":"71","author":"PK Whelton","year":"2018","unstructured":"Whelton, P. K. et al. 2017 ACC\/AHA\/AAPA\/ABC\/ACPM\/AGS\/APhA\/ASH\/ASPC\/NMA\/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology\/American Heart Association Task Force on Clinical Pr. J. Am. Coll. Cardiol. 71, e127\u2013e248 (2018).","journal-title":"J. Am. Coll. Cardiol."},{"key":"853_CR37","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","volume":"3","author":"GE Karniadakis","year":"2021","unstructured":"Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Rev. Phys. 3, 422\u2013440 (2021).","journal-title":"Nat. Rev. Phys."},{"key":"853_CR38","doi-asserted-by":"publisher","first-page":"A3055","DOI":"10.1137\/20M1318043","volume":"43","author":"S Wang","year":"2021","unstructured":"Wang, S., Teng, Y. & Perdikaris, P. Understanding and mitigating gradient flow pathologies in physics-informed neural networks. SIAM J. Sci. Comput. 43, A3055\u2013A3081 (2021).","journal-title":"SIAM J. Sci. Comput."},{"key":"853_CR39","doi-asserted-by":"publisher","first-page":"109951","DOI":"10.1016\/j.jcp.2020.109951","volume":"426","author":"X Jin","year":"2021","unstructured":"Jin, X., Cai, S., Li, H. & Karniadakis, G. E. NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. J. Comput. Phys. 426, 109951 (2021).","journal-title":"J. Comput. Phys."},{"key":"853_CR40","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/s10915-022-01939-z","volume":"92","author":"S Cuomo","year":"2022","unstructured":"Cuomo, S. et al. Scientific machine learning through physics\u2013informed neural networks: where we are and what\u2019s next. J. Sci. Comput. 92, 88 (2022).","journal-title":"J. Sci. Comput."},{"key":"853_CR41","unstructured":"Nichols, W. W. et al. Mcdonald\u2019s Blood Flow in Arteries Theoretical, Experimental and Clinical Principles. McDonald\u2019s Blood Flow in Arteries, Sixth Edition: Theoretical, Experimental and Clinical Principles (CRC press, 2011)."},{"key":"853_CR42","doi-asserted-by":"publisher","first-page":"S220","DOI":"10.1016\/S0002-8703(99)70313-3","volume":"138","author":"GM London","year":"1999","unstructured":"London, G. M. & Guerin, A. P. Influence of arterial pulse and reflected waves on blood pressure and cardiac function. Am. Heart J. 138, S220\u2013S224 (1999).","journal-title":"Am. Heart J."},{"key":"853_CR43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0001-8","volume":"6","author":"M Gao","year":"2016","unstructured":"Gao, M. et al. A simple adaptive transfer function for deriving the central blood pressure waveform from a radial blood pressure waveform. Sci. Rep. 6, 1\u20139 (2016).","journal-title":"Sci. Rep."},{"key":"853_CR44","first-page":"848","volume":"61","author":"M Gao","year":"2013","unstructured":"Gao, M., Zhang, G., Olivier, N. B. & Mukkamala, R. Improved pulse wave velocity estimation using an arterial tube-load model. IEEE Trans. Biomed. Eng. 61, 848\u2013858 (2013).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"853_CR45","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1080\/08037050802059225","volume":"17","author":"DG Brillante","year":"2008","unstructured":"Brillante, D. G., O\u2019sullivan, A. J. & Howes, L. G. Arterial stiffness indices in healthy volunteers using non-invasive digital photoplethysmography. Blood Press 17, 116\u2013123 (2008).","journal-title":"Blood Press"},{"key":"853_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-03612-1","volume":"12","author":"B Ibrahim","year":"2022","unstructured":"Ibrahim, B. & Jafari, R. Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder. Sci. Rep. 12, 1\u201314 (2022).","journal-title":"Sci. Rep."},{"key":"853_CR47","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/s41746-023-00796-w","volume":"6","author":"K Sel","year":"2023","unstructured":"Sel, K. et al. Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device. npj Digit. Med. 6, 59 (2023).","journal-title":"npj Digit. Med."},{"key":"853_CR48","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1161\/HYPERTENSIONAHA.117.10237","volume":"71","author":"GS Stergiou","year":"2018","unstructured":"Stergiou, G. S. et al. A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation\/European Society of Hypertension\/International Organization for Standardization (AAMI\/ESH\/ISO) Collaboration Statement. Hypertension 71, 368\u2013374 (2018).","journal-title":"Hypertension"},{"key":"853_CR49","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119\u2013139 (1997).","journal-title":"J. Comput. Syst. Sci."},{"key":"853_CR50","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster, A., Petitjean, F. & Webb, G. I. ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Discov. 34, 1454\u20131495 (2020).","journal-title":"Data Min. Knowl. Discov."},{"key":"853_CR51","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng, H., Runger, G., Tuv, E. & Vladimir, M. A time series forest for classification and feature extraction. Inf. Sci. (Ny.). 239, 142\u2013153 (2013).","journal-title":"Inf. Sci. (Ny.)."},{"key":"853_CR52","doi-asserted-by":"publisher","first-page":"116788","DOI":"10.1016\/j.eswa.2022.116788","volume":"197","author":"S Maqsood","year":"2022","unstructured":"Maqsood, S. et al. A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors. Expert Syst. Appl. 197, 116788 (2022).","journal-title":"Expert Syst. Appl."},{"key":"853_CR53","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02020-6","volume":"10","author":"S Gonz\u00e1lez","year":"2023","unstructured":"Gonz\u00e1lez, S., Hsieh, W.-T. & Chen, T. P.-C. A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram. Sci. Data 10, 149 (2023).","journal-title":"Sci. Data"},{"key":"853_CR54","doi-asserted-by":"publisher","first-page":"6022","DOI":"10.3390\/s21186022","volume":"21","author":"F Schrumpf","year":"2021","unstructured":"Schrumpf, F., Frenzel, P., Aust, C., Osterhoff, G. & Fuchs, M. Assessment of non-invasive blood pressure prediction from ppg and rppg signals using deep learning. Sensors. 21, 6022 (2021).","journal-title":"Sensors."},{"key":"853_CR55","doi-asserted-by":"publisher","first-page":"2338","DOI":"10.3390\/s20082338","volume":"20","author":"H Eom","year":"2020","unstructured":"Eom, H. et al. End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism. Sensors 20, 2338 (2020).","journal-title":"Sensors"},{"key":"853_CR56","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"853_CR57","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998\u20136008 (2017).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"853_CR58","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/0002-9149(65)90007-X","volume":"16","author":"MA Greene","year":"1965","unstructured":"Greene, M. A., Boltax, A. J., Lustig, G. A. & Rogow, E. Circulatory dynamics during the cold pressor test. Am. J. Cardiol. 16, 54\u201360 (1965).","journal-title":"Am. J. Cardiol."},{"key":"853_CR59","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1161\/CIRCRESAHA.119.315005","volume":"125","author":"JH Kim","year":"2019","unstructured":"Kim, J. H. et al. Peripheral vasoconstriction during mental stress and adverse cardiovascular outcomes in patients with coronary artery disease. Circ. Res. 125, 874\u2013883 (2019).","journal-title":"Circ. Res."},{"key":"853_CR60","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1161\/01.CIR.77.1.43","volume":"77","author":"EG Nabel","year":"1988","unstructured":"Nabel, E. G., Ganz, P., Gordon, J. B., Alexander, R. W. & Selwyn, A. P. Dilation of normal and constriction of atherosclerotic coronary arteries caused by the cold pressor test. Circulation 77, 43\u201352 (1988).","journal-title":"Circulation"},{"key":"853_CR61","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1097\/00003677-200110000-00005","volume":"29","author":"JB Buckwalter","year":"2001","unstructured":"Buckwalter, J. B. & Clifford, P. S. The paradox of sympathetic vasoconstriction in exercising skeletal muscle. Exerc. Sport Sci. Rev. 29, 159\u2013163 (2001).","journal-title":"Exerc. Sport Sci. Rev."},{"key":"853_CR62","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1088\/0143-0815\/12\/1\/003","volume":"12","author":"Y Tardy","year":"1991","unstructured":"Tardy, Y., Meister, J. J., Perret, F., Brunner, H. R. & Arditi, M. Non-invasive estimate of the mechanical properties of peripheral arteries from ultrasonic and photoplethysmographic measurements. Clin. Phys. Physiol. Meas. 12, 39 (1991).","journal-title":"Clin. Phys. Physiol. Meas."},{"key":"853_CR63","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1159\/000159037","volume":"31","author":"X Girerd","year":"1994","unstructured":"Girerd, X. et al. Noninvasive measurement of medium-sized artery intima-media thickness in humans: in vitro validation. J. Vasc. Res. 31, 114\u2013120 (1994).","journal-title":"J. Vasc. Res."},{"key":"853_CR64","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.diii.2013.01.025","volume":"94","author":"E Messas","year":"2013","unstructured":"Messas, E., Pernot, M. & Couade, M. Arterial wall elasticity: state of the art and future prospects. Diagn. Interv. Imaging 94, 561\u2013569 (2013).","journal-title":"Diagn. Interv. Imaging"},{"key":"853_CR65","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.3390\/s18072095","volume":"18","author":"TH Huynh","year":"2018","unstructured":"Huynh, T. H., Jafari, R. & Chung, W.-Y. An accurate bioimpedance measurement system for blood pressure monitoring. Sensors 18, 2095 (2018).","journal-title":"Sensors"},{"key":"853_CR66","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s13534-019-00096-x","volume":"9","author":"X Ding","year":"2019","unstructured":"Ding, X. & Zhang, Y.-T. Pulse transit time technique for cuffless unobtrusive blood pressure measurement: from theory to algorithm. Biomed. Eng. Lett. 9, 37\u201352 (2019).","journal-title":"Biomed. Eng. Lett."},{"key":"853_CR67","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1111\/j.1469-8986.1992.tb01698.x","volume":"29","author":"C France","year":"1992","unstructured":"France, C. & Ditto, B. Cardiovascular responses to the combination of caffeine and mental arithmetic, cold pressor, and static exercise stressors. Psychophysiology 29, 272\u2013282 (1992).","journal-title":"Psychophysiology"},{"key":"853_CR68","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1161\/HYPERTENSIONAHA.121.17747","volume":"78","author":"R Mukkamala","year":"2021","unstructured":"Mukkamala, R. et al. Evaluation of the accuracy of cuffless blood pressure measurement devices: challenges and proposals. Hypertension 78, 1161\u20131167 (2021).","journal-title":"Hypertension"},{"key":"853_CR69","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1146\/annurev-bioeng-110220-014644","volume":"24","author":"R Mukkamala","year":"2022","unstructured":"Mukkamala, R., Stergiou, G. S. & Avolio, A. P. Cuffless blood pressure measurement. Annu. Rev. Biomed. Eng. 24, 203\u2013230 (2022).","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"853_CR70","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1159\/000522660","volume":"10","author":"A Avolio","year":"2022","unstructured":"Avolio, A. et al. Challenges presented by cuffless measurement of blood pressure if adopted for diagnosis and treatment of hypertension. Pulse 10, 34\u201345 (2022).","journal-title":"Pulse"},{"key":"853_CR71","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/OJEMB.2021.3085482","volume":"2","author":"K Sel","year":"2021","unstructured":"Sel, K., Osman, D. & Jafari, R. Non-invasive cardiac and respiratory activity assessment from various human body locations using bioimpedance. IEEE open J. Eng. Med. Biol. 2, 210\u2013217 (2021).","journal-title":"IEEE open J. Eng. Med. Biol."},{"key":"853_CR72","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TBCAS.2020.2995810","volume":"14","author":"K Sel","year":"2020","unstructured":"Sel, K., Ibrahim, B. & Jafari, R. ImpediBands: Body Coupled Bio-Impedance Patches for Physiological Sensing Proof of Concept. IEEE Trans. Biomed. Circuits Syst. 14, 757\u2013774 (2020).","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"853_CR73","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine Learning in {P}ython. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"853_CR74","unstructured":"L\u00f6ning, M. et al. sktime: A unified interface for machine learning with time series. Preprint at https:\/\/arxiv.org\/abs\/1909.07872 (2019)."},{"key":"853_CR75","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1007\/s11357-014-9661-0","volume":"36","author":"TG Papaioannou","year":"2014","unstructured":"Papaioannou, T. G. et al. Total arterial compliance estimated by a novel method and all-cause mortality in the elderly: the PROTEGER study. Age (Omaha) 36, 1555\u20131563 (2014).","journal-title":"Age (Omaha)"},{"key":"853_CR76","doi-asserted-by":"crossref","unstructured":"Guo, C.-Y., Chang, C.-C., Wang, K.-J. & Hsieh, T.-L. Assessment of a calibration-free method of cuffless blood pressure measurement: a pilot study. IEEE J. Transl. Eng. Heal. Med. (2022).","DOI":"10.1109\/JTEHM.2022.3209754"},{"key":"853_CR77","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1109\/TBME.2016.2580904","volume":"64","author":"M Kachuee","year":"2016","unstructured":"Kachuee, M., Kiani, M. M., Mohammadzade, H. & Shabany, M. Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Trans. Biomed. Eng. 64, 859\u2013869 (2016).","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00853-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00853-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00853-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T01:07:35Z","timestamp":1686272855000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00853-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":77,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["853"],"URL":"https:\/\/doi.org\/10.1038\/s41746-023-00853-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2423200\/v1","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]},"assertion":[{"value":"28 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"R.J. is an Associate Editor for npj Digital Medicine. Other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"110"}}