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Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography

Nature Biomedical Engineering volume 7, pages 1229–1241 (2023)Cite this article

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Abstract

Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.

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Fig. 1: Combined haemodynamic sensor concepts and devices overview.
Fig. 2: Combined haemodynamic sensor system and measurements.
Fig. 3: Representative haemodynamic studies on healthy individuals.
Fig. 4: Post-surgical monitoring.
Fig. 5: Haemodynamic measurement scatterplots.
Fig. 6: Haemodynamic classification.

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Data availability

The data used for the training of the classification model are presented in its entirety in Fig. 5 and are available from the corresponding authors on reasonable request and Institutional Review Board approval.

Code availability

Code used for analysis and to produce figures is also available upon reasonable request to the corresponding authors; however, implementations of methods described in the text exist in public repositories described in the data analysis section and in referenced work.

References

  1. Heidenreich, P. A. et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 79, e263–e421 (2022).

    Article  PubMed  Google Scholar 

  2. January, C. T. et al. 2019 AHA/ACC/HRS focused update of the 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons. Circulation 140, e125–e151 (2019).

    Article  PubMed  Google Scholar 

  3. Truby, L. K. & Rogers, J. G. Advanced heart failure: epidemiology, diagnosis, and therapeutic approaches. Heart Fail. 8, 523–536 (2020).

    Google Scholar 

  4. Pinsky, M. R. & Payen, D. Functional hemodynamic monitoring. Crit. Care 9, 566–572 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Teboul, J. L. et al. Less invasive hemodynamic monitoring in critically ill patients. Intensive Care Med. 42, 1350–1359 (2016).

    Article  PubMed  Google Scholar 

  6. Cotter, G. et al. The role of cardiac power and systemic vascular resistance in the pathophysiology and diagnosis of patients with acute congestive heart failure. Eur. J. Heart Fail. 5, 443–451 (2003).

    Article  PubMed  Google Scholar 

  7. Delong, C. & Sharma, S. Physiology, Peripheral Vascular Resistance (StatPearls Publishing, 2022).

  8. Keville, M. P. et al. Arterial or cuff pressure: clinical predictors among patients in shock in a critical care resuscitation unit. Am. J. Emerg. Med. 46, 109–115 (2021).

    Article  PubMed  Google Scholar 

  9. Tran, Q. K. et al. Discrepancy between invasive and noninvasive blood pressure measurements in patients with sepsis by vasopressor status. West. J. Emerg. Med. 23, 358–367 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chen, Y. et al. Right heart catheterization-related complications: a review of the literature and best practices. Cardiol. Rev. 28, 36–41 (2020).

    Article  PubMed  Google Scholar 

  11. Desai, A. S. et al. Ambulatory hemodynamic monitoring reduces heart failure hospitalizations in ‘real-world’ clinical practice. J. Am. Coll. Cardiol. 69, 2357–2365 (2017).

    Article  PubMed  Google Scholar 

  12. Abraham, J. et al. Association of ambulatory hemodynamic monitoring of heart failure with clinical outcomes in a concurrent matched cohort analysis. JAMA Cardiol. 4, 556–563 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Malasinghe, L. P., Ramzan, N. & Dahal, K. Remote patient monitoring: a comprehensive study. J. Ambient Intell. Humaniz. Comput. 10, 57–76 (2019).

    Article  Google Scholar 

  14. Noah, B. et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. npj Digital Med. 1, 20172 (2018).

    Article  Google Scholar 

  15. Yetisen, A. K. et al. Wearables in medicine. Adv. Mater. 30, 1706910 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Olmedo-Aguirre, J. O. et al. Remote healthcare for elderly people using wearables: a review. Biosensors 12, 73 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bayoumy, K. et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18, 581–599 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Pevnick, J. M., Birkeland, K., Zimmer, R., Elad, Y. & Kedan, I. Wearable technology for cardiology: an update and framework for the future. Trends Cardiovasc. Med. 28, 144–150 (2018).

    Article  PubMed  Google Scholar 

  19. Prieto-Avalos, G. et al. Wearable devices for physical monitoring of heart: a review. Biosensors 12, 292 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huhn, S. et al. The impact of wearable technologies in health research: scoping review. JMIR Mhealth Uhealth 10, e34384 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Boubouchairopoulou, N., Ntineri, A., Kollias, A., Destounis, A. & Stergiou, G. S. Blood pressure variability assessed by office, home, and ambulatory measurements: comparison, agreement, and determinants. Hypertens. Res. 44, 1617–1624 (2021).

    Article  CAS  PubMed  Google Scholar 

  22. Stergiou, G. S. et al. Hypertension evaluation of the accuracy of cuffless blood pressure measurement devices: challenges and proposals. Hypertension 78, 1161–1167 (2021).

    Article  PubMed  Google Scholar 

  23. Stergiou, G. S. et al. Cuffless blood pressure measuring devices: review and statement by the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability. J. Hypertens. 40, 1449–1460 (2022).

    Article  CAS  PubMed  Google Scholar 

  24. Schutte, A. E., Kollias, A. & Stergiou, G. S. Blood pressure and its variability: classic and novel measurement techniques. Nat. Rev. Cardiol. 19, 643–654 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Stergiou, G. S. et al. Methodology and technology for peripheral and central blood pressure and blood pressure variability measurement: current status and future directions – position statement of the European Society of Hypertension Working Group on blood pressure monitoring and cardiovascular variability. J. Hypertens. 34, 1665–1677 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Ma, Y. et al. Relation between blood pressure and pulse wave velocity for human arteries. Proc. Natl Acad. Sci. USA 115, 11144–11149 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mukkamala, R. et al. Towards ubiquitous blood pressure monitoring via pulse transit time: theory and practice HHS public access. IEEE Trans. Biomed. Eng. 62, 1879–1901 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wibmer, T. et al. Blood pressure monitoring during exercise: comparison of pulse transit time and volume clamp methods. Blood Press. 24, 353–360 (2015).

    Article  PubMed  Google Scholar 

  29. Finnegan, E. et al. Pulse arrival time as a surrogate of blood pressure. Sci. Rep. 11, 22767 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Proença, J., Muehlsteff, J., Aubert, X. & Carvalho, P. Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population? Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010, 598–601 (2010).

    PubMed  Google Scholar 

  31. Kounalakis, S. N. & Geladas, N. D. The role of pulse transit time as an index of arterial stiffness during exercise. Cardiovasc. Eng. 9, 92–97 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. Polónia, J., Barbosa, L., Silva, J. A. & Maldonado, J. Different influences on central and peripheral pulse pressure, aortic wave reflections and pulse wave velocity of three different types of antihypertensive drugs. Rev. Port. Cardiol. 22, 1485–1492 (2003).

    PubMed  Google Scholar 

  33. Kelly, R. P., Millasseau, S. C., Ritter, J. M. & Chowienczyk, P. J. Vasoactive drugs influence aortic augmentation index independently of pulse-wave velocity in healthy men. Hypertension 37, 1429–1433 (2001).

    Article  CAS  PubMed  Google Scholar 

  34. Borner, A., Murray, K., Trotter, C. & Pearson, J. Baseline aortic pulse wave velocity is associated with central and peripheral pressor responses during the cold pressor test in healthy subjects. Physiol. Rep. 5, e13357 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Elias, S. O. & Ajayi, R. E. Effect of sympathetic autonomic stress from the cold pressor test on left ventricular function in young healthy adults. Physiol. Rep. 7, e13985 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Parati, G., Stergiou, G. S., Dolan, E. & Bilo, G. Blood pressure variability: clinical relevance and application. J. Clin. Hypertens. 20, 1133–1137 (2018).

  37. Lantelme, P., Mestre, C., Lievre, M., Gressard, A. & Milon, H. Heart rate: an important confounder of pulse wave velocity assessment. Hypertension 39, 1083–1087 (2002).

    Article  CAS  PubMed  Google Scholar 

  38. Papaioannou, T. G. et al. The influence of resting heart rate on pulse wave velocity measurement is mediated by blood pressure and depends on aortic stiffness levels: insights from the Corinthia study. Physiol. Meas. 40, 055005 (2019).

    Article  PubMed  Google Scholar 

  39. Sherwood, A. et al. Methodological guidelines for impedance cardiography. Psychophysiology 27, 1–23 (1990).

    Article  CAS  PubMed  Google Scholar 

  40. Kouz, K., Scheeren, T. W. L., de Backer, D. & Saugel, B. Pulse wave analysis to estimate cardiac output. Anesthesiology 134, 119–126 (2021).

    Article  PubMed  Google Scholar 

  41. Hametner, B. & Wassertheurer, S. Pulse waveform analysis: is it ready for prime time? Curr. Hypertens. Rep. 19, 1–7 (2017).

    Article  CAS  Google Scholar 

  42. Liu, J. et al. Multi-wavelength photoplethysmography method for skin arterial pulse extraction. Biomed. Opt. Express 7, 4313 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Liu, J. et al. Multi-wavelength photoplethysmography enabling continuous blood pressure measurement with compact wearable electronics. IEEE Trans. Biomed. Eng. 66, 1514–1525 (2019).

    Article  PubMed  Google Scholar 

  44. Liu, J. et al. PCA-based multi-wavelength photoplethysmography algorithm for cuffless blood pressure measurement on elderly subjects. IEEE J. Biomed. Health Inf. 25, 663–673 (2021).

    Article  Google Scholar 

  45. Spigulis, J., Gailite, L., Lihachev, A. & Erts, R. Simultaneous recording of skin blood pulsations at different vascular depths by multiwavelength photoplethysmography. Appl. Opt. 56, 1754–1759 (2007).

    Article  Google Scholar 

  46. Aidar, F. J. et al. Can post-exercise hemodynamic response be influenced by different recovery methods in paraplegic sportsmen? Int. J. Environ. Res. Public Health 19, 1772 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Francisco, M. A. et al. Hemodynamics of postexercise versus post-hot water immersion recovery. J. Appl. Physiol. 130, 1362–1372 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Churchill, T. W. The impact of exercise and athletic training on vascular structure and function. Curr. Treat. Options Cardiovasc. Med. 22, 1–11 (2020).

    Article  Google Scholar 

  49. Bauer, P. et al. Blood pressure response and vascular function of professional athletes and controls. Sports Med. Int. Open 5, E45–E52 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  50. McDuff, D. et al. Non-contact imaging of peripheral hemodynamics during cognitive and psychological stressors. Sci. Rep. 10, 1–13 (2020).

    Article  Google Scholar 

  51. Chung, H. U. et al. Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units. Nat. Med. 26, 418–429 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lee, K. H. et al. Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch. Nat. Biomed. Eng. 4, 148–158 (2020).

    Article  PubMed  Google Scholar 

  53. Finlayson, L. et al. Depth penetration of light into skin as a function of wavelength from 200 to 1000 nm. Photochem. Photobiol. 98, 974–981 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. Rwei, A. Y. et al. A wireless, skin-interfaced biosensor for cerebral hemodynamic monitoring in pediatric care. Proc. Natl Acad. Sci. USA 117, 31674–31684 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Nishimura, R. A. & Tajik, A. J. The Valsalva maneuver and response revisited. Mayo Clin. Proc. 61, 211–217 (1986).

    Article  CAS  PubMed  Google Scholar 

  56. Bennett, T., Hosking, D. J. & Hampton, J. R. Vasomotor responses to the Valsalva manoeuvre in normal subjects and in patients with diabetes mellitus. Br. Heart J. 42, 422–428 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Awad, A. A. et al. Different responses of ear and finger pulse oximeter wave form to cold pressor test. Anesth. Analg. 92, 1483–1486 (2001).

    Article  CAS  PubMed  Google Scholar 

  58. Lovallo, W. The cold pressor test and autonomic function: a review and integration. Psychophysiology 12, 268–282 (1975).

    Article  CAS  PubMed  Google Scholar 

  59. Heistad, D. D., Abboud, F. M. & Eckstein, J. W. Vasoconstrictor response to simulated diving in man. J. Appl. Physiol. 25, 542–549 (1968).

    Article  CAS  PubMed  Google Scholar 

  60. Kellogg, D. L., Crandall, C. G., Liu, Y., Charkoudian, N. & Johnson, J. M. Nitric oxide and cutaneous active vasodilation during heat stress in humans. J. Appl. Physiol. 85, 824–829 (1998).

    Article  CAS  PubMed  Google Scholar 

  61. Kim, M. Y. et al. Acute reduction of pulse wave velocity after sauna in adults in a community. J. Korean Acad. Fam. Med. 27, 384–390 (2006).

    Google Scholar 

  62. Lee, E. et al. Sauna exposure leads to improved arterial compliance: findings from a non-randomised experimental study. Eur. J. Prev. Cardiol. 25, 130–138 (2018).

    Article  PubMed  Google Scholar 

  63. Laukkanen, T. et al. Acute effects of sauna bathing on cardiovascular function. J. Hum. Hypertens. 32, 129–138 (2017).

    Article  PubMed  Google Scholar 

  64. Teixeira, L., Ritti-Dias, R. M., Tinucci, T., Mion, D. & de Moraes Forjaz, C. L. Post-concurrent exercise hemodynamics and cardiac autonomic modulation. Eur. J. Appl. Physiol. 111, 2069–2078 (2011).

    Article  PubMed  Google Scholar 

  65. Vatner, S. F. & Pagani, M. Cardiovascular adjustments to exercise: hemodynamics and mechanisms. Prog. Cardiovasc. Dis. 19, 91–108 (1976).

    Article  CAS  PubMed  Google Scholar 

  66. Desebbe, O. et al. Control of postoperative hypotension using a closed-loop system for norepinephrine infusion in patients after cardiac surgery: a randomized trial. Anesth. Analg. 134, 964–973 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Klabunde, R. E. Cardiovascular Physiology Concepts (Wolters Kluwer, Lippincott Williams & Wilkins, 2021).

  68. Hofmann, H., Wickham, H. & Kafadar, K. Letter-value plots: boxplots for large data. J. Comput. Graph. Stat. 26, 469–477 (2017).

    Article  Google Scholar 

  69. Brunner, E. & Munzel, U. Rank-score tests in factorial designs with repeated measures. J. Multivar. Anal. 70, 286–317 (1999).

    Article  Google Scholar 

  70. Mitchell, L. A., MacDonald, R. A. R. & Brodie, E. E. Temperature and the cold pressor test. J. Pain 5, 233–237 (2004).

    PubMed  Google Scholar 

  71. Reddy, S. Explainability and artificial intelligence in medicine. Lancet Digit. Health 4, e214–e215 (2022).

    Article  CAS  PubMed  Google Scholar 

  72. Lundberg, S. M., Allen, P. G. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).

    Google Scholar 

  73. Newlin, D. B. & Levenson, R. W. Pre-ejection period: measuring beta-adrenergic influences upon the heart. Psychophysiology 16, 546–552 (1979).

    Article  CAS  PubMed  Google Scholar 

  74. Garrard, C. L., Weissler, A. M. & Dodge, H. T. The relationship of alterations in systolic time intervals to ejection fraction in patients with cardiac disease. Circulation 42, 455–462 (1970).

    Article  PubMed  Google Scholar 

  75. Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. McKinney, W. Data structures for statistical computing in Python. In Proc. 9th Python in Science Conf. (eds van der Walt, S. & Millman, K. J.) 56–61 (2010).

  78. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  79. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  80. Waskom, M. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).

    Article  Google Scholar 

  81. Maeda, Y., Sekine, M. & Tamura, T. The advantages of wearable green reflected photoplethysmography. J. Med. Syst. 35, 829–834 (2011).

    Article  PubMed  Google Scholar 

  82. Lee, J. et al. Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2013, 1724–1727 (2013).

    PubMed  Google Scholar 

  83. Scholkmann, F., Boss, J. & Wolf, M. An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals. Algorithms 5, 588–603 (2012).

    Article  Google Scholar 

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Acknowledgements

We thank M. Banet and J. Martucci for their invaluable expertise and insights throughout the development and execution of this project. A.T. discloses support for the research described in this study from the National Heart, Lung and Blood Institute of the National Institutes of Health (grant number F30HL157066). The work was also supported in part by the National Center for Advancing Translational Sciences (NCATS; grant UM1TR004407 to J.P.).

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Author notes

  1. These authors contributed equally: Daniel Franklin, Andreas Tzavelis.

Authors and Affiliations

  1. Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Daniel Franklin, Megh Rathod, Jonathan Wu & Haolin Liu

  2. Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada

    Daniel Franklin, Megh Rathod, Jonathan Wu & Haolin Liu

  3. Medical Scientist Training Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Andreas Tzavelis

  4. Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA

    Andreas Tzavelis, Hany Arafa & John A. Rogers

  5. Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA

    Andreas Tzavelis, Jacob Trueb, Hany Arafa, Sung Soo Kwak, Ivy Huang & John A. Rogers

  6. Sibel Health, Niles, IL, USA

    Jong Yoon Lee & Ha Uk Chung

  7. Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA

    Ivy Huang, Changsheng Wu & John A. Rogers

  8. Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA

    Yiming Liu

  9. Scripps Research Translational Institute, San Diego, CA, USA

    Jay A. Pandit

  10. Division of Cardiology, Department of Medicine, Bluhm Cardiovascular Institute, Northwestern University, Chicago, IL, USA

    Faraz S. Ahmad

  11. Division of Cardiac Surgery, Department of Surgery, Bluhm Cardiovascular Institute, Northwestern University, Chicago, IL, USA

    Patrick M. McCarthy

  12. Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    John A. Rogers

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Contributions

A.T. and D.F. conceptualized the work. A.T., D.F., J.Y.L., H.A., J.T., H.U.C., S.S.K. and C.W. developed the methodology. A.T., D.F., H.A., Y.L., I.H., M.R., J.W., H.L. and J.P. performed experiments. A.T. and D.F. visualized data. A.T., D.F. and J.A.R acquired funding. J.P., F.S.A., P.M.M. and J.A.R. supervised the project. A.T. and D.F. wrote the manuscript. A.T., D.F., J.P., F.S.A. and J.A.R. edited the manuscript.

Corresponding authors

Correspondence to Daniel Franklin or John A. Rogers.

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Competing interests

J.Y.L., H.U.C. and J.A.R. own equity in Sibel Health and hold patents (US20210361165A1, USA 2021 pending; US20210386300A1, USA 2021 pending; WO2023043866A1, WIPO 2023) associated with this company. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Justin Estepp, Yftach Gepner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary tables, figures and video captions.

Reporting Summary

Supplementary Video 1

3D scatterplot showing the grouping of haemodynamic pressors according to sensor data.

Supplementary Video 2

Demonstration of the system.

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Franklin, D., Tzavelis, A., Lee, J.Y. et al. Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography. Nat. Biomed. Eng 7, 1229–1241 (2023). https://doi.org/10.1038/s41551-023-01098-y

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