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Cuff less Blood Pressure Estimation Algorithms for Continuous Health care Monitoring

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Data availability

The complete dataset supporting the findings of this study is available via the PhysioNet data repository at https://doi.org/10.13026/qcc8-n557. The associated preprocessed raw data are available and can be shared with interested parties upon reasonable request. Source data are provided with this paper.

Code availability

The machine learning algorithm is publicly available via GitHub at https://github.com/TAMU-ESP/Graphene_BP. The custom codes used for data visualization are available from the corresponding authors upon request.

References

  1. Jennings, J. R., Muldoon, M. F., Allen, B., Ginty, A. T. & Gianaros, P. J. Cerebrovascular function in hypertension: does high blood pressure make you old? Psychophysiology 58, 1–17 (2021).

    Article  Google Scholar

  2. Shaffer, F., McCraty, R. & Zerr, C. L. A healthy heart is not a metronome: an integrative review of the heartas anatomy and heart rate variability. Front. Psychol. 5, 1040 (2014).

    Article  Google Scholar

  3. Kwon, Y. et al. Blood pressure monitoring in sleep: time to wake up. Blood Press. Monit. 25, 61–68 (2020).

    Article  Google Scholar

  4. Magder, S. The meaning of blood pressure. Crit. Care 22, 257 (2018).

    Article  CAS  Google Scholar

  5. Benjamin, E. J. et al. Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation 139, e556–e528 (2019).

    Article  Google Scholar

  6. Flint, A. C. et al. Effect of systolic and diastolic blood pressure on cardiovascular outcomes. N. Engl. J. Med. 381, 243–251 (2019).

    Article  Google Scholar

  7. Kario, K. Management of hypertension in the digital era. Hypertension 76, 640–650 (2020).

    Article  CAS  Google Scholar

  8. Carey, R. M., Muntner, P., Bosworth, H. B. & Whelton, P. K. Prevention and control of hypertension. J. Am. Coll. Cardiol. 72, 1278–1293 (2018).

    Article  Google Scholar

  9. Kario, K. et al. Morning home blood pressure is a strong predictor of coronary artery disease: the honest study. J. Am. Coll. Cardiol. 67, 1519–1527 (2016).

    Article  Google Scholar

  10. Al Ghorani, H., Kulenthiran, S., Lauder, L., Böhm, M. & Mahfoud, F. Hypertension trials update. J. Hum. Hypertens. 35, 398–409 (2021).

    Article  Google Scholar

  11. Marrone, O. & Bonsignore, M. R. Blood-pressure variability in patients with obstructive sleep apnea: current perspectives. Nat. Sci. Sleep. 10, 229–242 (2018).

    Article  Google Scholar

  12. Salazar, M. R. et al. Nocturnal hypertension in high-risk mid-pregnancies predict the development of preeclampsia/eclampsia. J. Hypertens. 37, 182–186 (2018).

  13. Stergiou, G. S. et al. A universal standard for the validation of blood pressure measuring devices. Hypertension 71, 368–374 (2018).

    Article  CAS  Google Scholar

  14. Bartels, K., Esper, S. A. & Thiele, R. H. Blood pressure monitoring for the anesthesiologist. Anesth. Analg. 122, 1866–1879 (2016).

    Article  CAS  Google Scholar

  15. Vischer, A. S. & Burkard, T. Principles of blood pressure measurement – current techniques, office vs ambulatory blood pressure measurement. Adv. Exp. Med. Biol. 956, 85–96 (2016).

    Article  Google Scholar

  16. Siu, A. L. et al. Screening for high blood pressure in adults: U.S. preventive services task force recommendation statement. Ann. Intern. Med. 163, 778–786 (2015).

    Article  Google Scholar

  17. Jeong, I. C., Bychkov, D. & Searson, P. C. Wearable devices for precision medicine and health state monitoring. IEEE Trans. Biomed. Eng. 66, 1242–1258 (2019).

    Article  Google Scholar

  18. Li, R., Liang, N., Bu, F. & Hesketh, T. The effectiveness of self-management of hypertension in adults using mobile health: systematic review and meta-analysis. JMIR mHealth uHealth 8, e17776 (2020).

    Article  Google Scholar

  19. \Asayama, K., Ohkubo, T. & Imai, Y. In-office and out-of-office blood pressure measurement. J. Hum. Hypertens. https://doi.org/10.1038/s41371-021-00486-8 (2021).

  20. Pandit, J. A., Lores, E. & Batlle, D. Cuffless blood pressure monitoring. Clin. J. Am. Soc. Nephrol. 15, 1531–1538 (2020).

    Article  Google Scholar

  21. Wang, C. et al. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nat. Biomed. Eng. 2, 687–695 (2018).

    Article  Google Scholar

  22. Wang, C. et al. Continuous monitoring of deep-tissue haemodynamics with stretchable ultrasonic phased arrays. Nat. Biomed. Eng. 5, 749–758 (2021).

    Article  CAS  Google Scholar

  23. Luo, N. et al. Flexible piezoresistive sensor patch enabling ultralow power cuffless blood pressure measurement. Adv. Funct. Mater. 26, 1178–1187 (2016).

    Article  CAS  Google Scholar

  24. Kim, J. et al. Soft wearable pressure sensors for beat-to-beat blood pressure monitoring. Adv. Healthc. Mater. 8, 1–9 (2019).

    Article  CAS  Google Scholar

  25. Yang, S., Zhang, Y., Cho, S. Y., Correia, R. & Morgan, S. P. Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model. Opt. Quantum Electron. 53, 1–20 (2021).

    Article  CAS  Google Scholar

  26. Elgendi, M. et al. The use of photoplethysmography for assessing hypertension. npj Digit. Med. 2, 1–11 (2019).

    Article  Google Scholar

  27. Chang, Y. H., Huang, K. C., Yang, C. C. & Tsai, H. Y. Evaluation of absorbed light dose in human skin tissue during Light Therapy by 630nm LED light. In 2015 IEEE 12th International Conference on Networking, Sensing and Control 394–398 (IEEE, 2015); https://doi.org/10.1109/ICNSC.2015.7116069

  28. Safar, M. E. & Boudier, H. S. Vascular development, pulse pressure, and the mechanisms of hypertension. Hypertension 46, 205–209 (2005).

    Article  CAS  Google Scholar

  29. Sel, K. et al. Electrical characterization of graphene-based e-tattoos for bio-impedance-based physiological sensing. In 2019 IEEE Biomedical Circuits and Systems Conference 1–4 (IEEE, 2019); https://doi.org/10.1109/BIOCAS.2019.8919003

  30. 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, 210–217 (2021).

    Article  Google Scholar

  31. Wang, T. W., Chen, W. X., Chu, H. W. & Lin, S. F. Single-channel bioimpedance measurement for wearable continuous blood pressure monitoring. IEEE Trans. Instrum. Meas. 70, 1–9 (2021).

  32. Kabiri Ameri, S. et al. Graphene electronic tattoo sensors. ACS Nano 11, 7634–7641 (2017).

    Article  CAS  Google Scholar

  33. Ameri, S. K. et al. Imperceptible electrooculography graphene sensor system for human–robot interface. npj 2D Mater. Appl. 2, 19 (2018).

    Article  CAS  Google Scholar

  34. Kireev, D. et al. Fabrication, characterization and applications of graphene electronic tattoos. Nat. Protoc. 16, 2395–2417 (2021).

    Article  CAS  Google Scholar

  35. Ibrahim, B. & Jafari, R. Cuffless blood pressure monitoring from an array of wrist bio-impedance sensors using subject-specific regression models: proof of concept. IEEE Trans. Biomed. Circ. Syst. 13, 1723–1735 (2019).

    Article  Google Scholar

  36. American National Standards Institute, Association for the Advancement of Medical Instrumentation. ANSI/AAMI ES60601-1:2005/A1:2012, Medical Electrical Equipment Part 1: General Requirements for Basic Safety and Essential Performance (ANSI/AAMI 2012); https://webstore.ansi.org/Standards/AAMI/ansiaamies606012005r2012

  37. Sel, K., Ibrahim, B. & Jafari, R. ImpediBands: body coupled bio-impedance patches for physiological sensing proof of concept. IEEE Trans. Biomed. Circ. Syst. 14, 757–774 (2020).

  38. Webster, J. Medical Instrumentation: Application and Design 4th edn (John Wiley & Sons, 2010).

  39. Miccoli, I., Edler, F., Pfnür, H. & Tegenkamp, C. The 100th anniversary of the four-point probe technique: the role of probe geometries in isotropic and anisotropic systems. J. Phys. Condens. Matter 27, 223201 (2015).

    Article  CAS  Google Scholar

  40. Brath, P. C. & Eisenach, J. C. Atlas of cardiovascular monitoring. Anesthesiology 93, 312–312 (2000).

    Article  Google Scholar

  41. Vlachopoulos, C., O'Rourke, M. & Nichols, W. W. McDonald's Blood Flow in Arteries (CRC, 2011); https://doi.org/10.1201/b13568

  42. Jang, H., Dai, Z., Ha, K.-H., Ameri, S. K. & Lu, N. Stretchability of PMMA-supported CVD graphene and of its electrical contacts. 2D Mater. 7, 014003 (2019).

    Article  CAS  Google Scholar

  43. Goldstein, D. S. & Cheshire, W. P. Beat-to-beat blood pressure and heart rate responses to the Valsalva maneuver. Clin. Auton. Res. 27, 361–367 (2017).

    Article  Google Scholar

  44. Johnson, B. D., Sackett, J. R., Schlader, Z. J. & Leddy, J. J. Attenuated cardiovascular responses to the cold pressor test in concussed collegiate athletes. J. Athl. Train. 55, 124–131 (2020).

    Article  Google Scholar

  45. Byambasukh, O., Snieder, H. & Corpeleijn, E. Relation between leisure time, commuting, and occupational physical activity with blood pressure in 125 402 Adults: the Lifelines cohort. J. Am. Heart Assoc. 9, e014313 (2020).

    Article  Google Scholar

  46. IEEE Engineering in Medicine and Biology Society IEEE Standard for Wearable, Cuffless Blood Pressure Measuring Devices IEEE 1708-2014 (IEEE, 2014).

  47. Koshimizu, H., Kojima, R. & Okuno, Y. Future possibilities for artificial intelligence in the practical management of hypertension. Hypertens. Res. 43, 1327–1337 (2020).

    Article  Google Scholar

  48. Herakova, N., Nwobodo, N. H. N., Wang, Y., Chen, F. & Zheng, D. Effect of respiratory pattern on automated clinical blood pressure measurement: an observational study with normotensive subjects. Clin. Hypertens. 23, 15 (2017).

    Article  Google Scholar

  49. McEniery, C. M., Cockcroft, J. R., Roman, M. J., Franklin, S. S. & Wilkinson, I. B. Central blood pressure: current evidence and clinical importance. Eur. Heart J. 35, 1719–1725 (2014).

    Article  Google Scholar

  50. Asayama, K. et al. Nocturnal blood pressuremeasured by home devices: Evidence and perspective for clinical application. J. Hypertens. 37, 905–916 (2019).

    Article  CAS  Google Scholar

  51. Gaffey, A. E., Schwartz, J. E., Harris, K. M., Hall, M. H. & Burg, M. M. Effects of ambulatory blood pressure monitoring on sleep in healthy, normotensive men and women. Blood Press. Monit. 26, 93–101 (2021).

  52. Soleimani, E., Mokhtari-Dizaji, M., Fatouraee, N. & Saberi, H. Assessing the blood pressure waveform of the carotid artery using an ultrasound image processing method. Ultrasonography 36, 144–152 (2017).

    Article  Google Scholar

  53. Kemmotsu, O. et al. Blood pressure measurement by arterial tonometry in controlled Hypotension. Anesth. Analg. 73, 54–58 (1991).

    Article  CAS  Google Scholar

  54. Lee, J. Y., Choi, E. Y., Jeong, H. J., Kim, K. H. & Park, J. C. Blood pressure measurement using finger cuff. Conf. Proc. IEEE Eng. Med. Biol. Soc. 7 S, 3575–3577 (2005).

  55. Mazoteras Pardo, V., Losa Iglesias, M. E., López Chicharro, J. & Becerro de Bengoa Vallejo, R. The QardioArm app in the assessment of blood pressure and heart rate: reliability and validity study. JMIR mHealth uHealth 5, e198 (2017).

    Article  Google Scholar

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Acknowledgements

The work was supported in part by the Office of Naval Research under grant number N00014-18-1-2706, the Temple Foundation Endowed Professorship, the National Science Foundation under grant number 1738293 and the National Institute of Health under grant number 1R01EB028106. R.J. acknowledges useful discussions with the former founding director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the NIH, R. I. Pettigrew. We acknowledge J. Wozniak at the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (http://www.tacc.utexas.edu) for creating Fig. 1a. The authors have permission to use and publish the image.

Author information

Author notes

  1. These authors contributed equally: Dmitry Kireev, Kaan Sel.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA

    Dmitry Kireev, Neelotpala Kumar & Deji Akinwande

  2. Microelectronics Research Center, The University of Texas, Austin, TX, USA

    Dmitry Kireev & Deji Akinwande

  3. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    Kaan Sel, Bassem Ibrahim & Roozbeh Jafari

  4. Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA

    Ali Akbari & Roozbeh Jafari

  5. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Roozbeh Jafari

Contributions

D.K., K.S., R.J. and D.A. conceived the idea of using GET and designed the experiments. B.I. and R.J. designed the instrumentation for bioimpedance acquisition. D.K. fabricated and characterized the GETs. K.S. and B.I. optimized the XL-board. D.K., K.S., B.I. and N.K. performed the BP experiments. B.I. and A.A. developed and utilized the machine learning algorithm. D.K. and K.S. compiled and analysed the data. The manuscript was written with the contributions of all authors. All authors have approved the final version of the manuscript.

Corresponding authors

Correspondence to Roozbeh Jafari or Deji Akinwande.

Ethics declarations

Competing interests

R.J. and B.I. filed a patent (US 2020/0138303 titled 'System and method for cuff-less blood pressure monitoring') related to this research; this patent is licensed to SpectroBeat LLC.

Peer review

Peer review information

Nature Nanotechnology thanks Yingying Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Kireev, D., Sel, K., Ibrahim, B. et al. Continuous cuffless monitoring of arterial blood pressure via graphene bioimpedance tattoos. Nat. Nanotechnol. 17, 864–870 (2022). https://doi.org/10.1038/s41565-022-01145-w

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