Skip to main content

Advertisement

SpringerLink
Log in

Study of continuous blood pressure estimation based on pulse transit time, heart rate and photoplethysmography-derived hemodynamic covariates

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

It is widely recognized that pulse transit time (PTT) can track blood pressure (BP) over short periods of time, and hemodynamic covariates such as heart rate, stiffness index may also contribute to BP monitoring. In this paper, we derived a proportional relationship between BP and PPT−2 and proposed an improved method adopting hemodynamic covariates in addition to PTT for continuous BP estimation. We divided 28 subjects from the Multi-parameter Intelligent Monitoring for Intensive Care database into two groups (with/without cardiovascular diseases) and utilized a machine learning strategy based on regularized linear regression (RLR) to construct BP models with different covariates for corresponding groups. RLR was performed for individuals as the initial calibration, while recursive least square algorithm was employed for the re-calibration. The results showed that errors of BP estimation by our method stayed within the Association of Advancement of Medical Instrumentation limits (− 0.98 ± 6.00 mmHg @ SBP, 0.02 ± 4.98 mmHg @ DBP) when the calibration interval extended to 1200-beat cardiac cycles. In comparison with other two representative studies, Chen’s method kept accurate (0.32 ± 6.74 mmHg @ SBP, 0.94 ± 5.37 mmHg @ DBP) using a 400-beat calibration interval, while Poon’s failed (− 1.97 ± 10.59 mmHg @ SBP, 0.70 ± 4.10 mmHg @ DBP) when using a 200-beat calibration interval. With additional hemodynamic covariates utilized, our method improved the accuracy of PTT-based BP estimation, decreased the calibration frequency and had the potential for better continuous BP estimation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Mendis S, Puska P, Norrving B (2011) Global atlas on cardiovascular disease prevention and control. World Health Organization, Geneva

    Google Scholar 

  2. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M et al (2013) 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Blood Press 22(4):193–278

    Article  Google Scholar 

  3. Peter L, Noury N, Cerny M (2014) A review of methods for non-invasive and continuous blood pressure monitoring: pulse transit time method is promising? Irbm 35(5):271–282

    Article  Google Scholar 

  4. Mukkamala R, Hahn J-O, Inan OT, Mestha LK, Kim C-S, Töreyin H et al (2015) Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans Biomed Eng 62(8):1879–1901

    Article  PubMed  PubMed Central  Google Scholar 

  5. O’Brien E, Asmar R, Beilin L, Imai Y, Mallion J-M, Mancia G et al (2003) European Society of Hypertension recommendations for conventional, ambulatory and home blood pressure measurement. J Hypertens 21(5):821–848

    Article  PubMed  Google Scholar 

  6. Dueck R, Goedje O, Clopton P (2012) Noninvasive continuous beat-to-beat radial artery pressure via TL-200 applanation tonometry. J Clin Monit Comput 26(2):75–83

    Article  PubMed  Google Scholar 

  7. Lee B, Jeong J, Kim J, Kim B, Chun K (2014) Cantilever arrayed blood pressure sensor for arterial applanation tonometry. IET Nanobiotechnol 8(1):37–43

    Article  PubMed  CAS  Google Scholar 

  8. Saugel B, Dueck R, Wagner JY (2014) Measurement of blood pressure. Best Pract Res Clin Anaesthesiol 28(4):309–322

    Article  PubMed  Google Scholar 

  9. Imholz BP, Wieling W, van Montfrans GA, Wesseling KH (1998) Fifteen years experience with finger arterial pressure monitoring: assessment of the technology. Cardiovasc Res 38(3):605–616

    Article  PubMed  CAS  Google Scholar 

  10. Wesseling K, De Wit B, Van der Hoeven G, Van Goudoever J, Settels J (1995) Physiocal, calibrating finger vascular physiology for Finapres. Homeost Health Dis 36(2–3):67

    Google Scholar 

  11. Sato T, Nishinaga M, Kawamoto A, Ozawa T, Takatsuji H (1993) Accuracy of a continuous blood pressure monitor based on arterial tonometry. Hypertension 21(6 Pt 1):866–874

    Article  PubMed  CAS  Google Scholar 

  12. Dilpreet B, Jean-Michel R, Mehmet Rasit Y (2015) A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time. Physiol Meas 36(3):R1–R26

    Article  Google Scholar 

  13. Can Y, Kilic H, Akdemir R, Acar B, Edem E, Kocyigit I et al (2016) An investigation of pulse transit time as a blood pressure measurement method in patients undergoing carotid artery stenting. Blood Press Monit 21(3):168–170

    Article  PubMed  Google Scholar 

  14. Poon CC, Zhang Y-T, Liu Y (2006) Modeling of pulse transit time under the effects of hydrostatic pressure for cuffless blood pressure measurements. In: 3rd IEEE/EMBS international summer school on medical devices and biosensors, pp 65–68

  15. Lopez G, Shuzo M, Ushida H, Hidaka K, Yanagimoto S, Imai Y et al (2010) Continuous blood pressure monitoring in daily life. J. Adv. Mech. Des. Syst. Manuf. 4(1):179–186

    Article  Google Scholar 

  16. Chen MW, Kobayashi T, Ichikawa S, Takeuchi Y, Togawa T (2000) Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration. Med Biol Eng Comput 38(5):569–574

    Article  PubMed  CAS  Google Scholar 

  17. Escobar B, Torres R (2014) Feasibility of non-invasive blood pressure estimation based on pulse arrival time: a MIMIC database study. In: Computing in cardiology 2014, 7–10 Sept 2014, pp 1113–1116

  18. Poon CCY, Zhang YT (2005) Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time. In: 2005 IEEE Engineering in Medicine and Biology 27th annual conference, 17–18 Jan 2006, pp 5877–5880)

  19. Zheng YL, Yan BP, Zhang YT, Poon CCY (2014) An armband wearable device for overnight and cuff-less blood pressure measurement. IEEE Trans Biomed Eng 61(7):2179–2186

    Article  PubMed  Google Scholar 

  20. Puke S, Suzuki T, Nakayama K, Tanaka H, Minami S (2013) Blood pressure estimation from pulse wave velocity measured on the chest. In: 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3–7 July 2013, pp 6107–6110

  21. Gesche H, Grosskurth D, Küchler G, Patzak A (2012) Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. Eur J Appl Physiol 112(1):309–315

    Article  PubMed  Google Scholar 

  22. Zheng DC, Murray A (2009) Non-invasive quantification of peripheral arterial volume distensibility and its non-linear relationship with arterial pressure. J Biomech 42(8):1032–1037

    Article  PubMed  Google Scholar 

  23. Chen Y, Wen CY, Tao GC, Bi M, Li GQ (2009) Continuous and noninvasive blood pressure measurement: a novel modeling methodology of the relationship between blood pressure and pulse wave velocity. Ann Biomed Eng 37(11):2222–2233

    Article  PubMed  Google Scholar 

  24. Zhang G, Gao M, Xu D, Olivier NB, Mukkamala R (2011) Pulse arrival time is not an adequate surrogate for pulse transit time as a marker of blood pressure. J Appl Physiol 111(6):1681–1686

    Article  PubMed  Google Scholar 

  25. Muehlsteff J, Aubert X, Schuett M (2006) Cuffless estimation of systolic blood pressure for short effort bicycle tests: the prominent role of the pre-ejection period. In: 28th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2006 (EMBS’06), pp 5088–5092

  26. Jadooei A, Zaderykhin O, Shulgin VI (2013) Adaptive algorithm for continuous monitoring of blood pressure using a pulse transit time. In: 2013 IEEE XXXIII international scientific conference electronics and nanotechnology (ELNANO), 16–19 April 2013, pp 297–301

  27. Cattivelli FS, Garudadri H (2009) Noninvasive cuffless estimation of blood pressure from pulse arrival time and heart rate with adaptive calibration. In: 2009 sixth international workshop on wearable and implantable body sensor networks, 3–5 June 2009, pp 114–119

  28. Ma HT (2014) A blood pressure monitoring method for stroke management. Biomed Res Int 2014:7

    Google Scholar 

  29. McCarthy B, Vaughan C, O’Flynn B, Mathewson A, Mathúna C (2013) An examination of calibration intervals required for accurately tracking blood pressure using pulse transit time algorithms. J Hum Hypertens 27(12):744–750

    Article  PubMed  CAS  Google Scholar 

  30. Mitchell GF, Parise H, Benjamin EJ, Larson MG, Keyes MJ, Vita JA et al (2004) Changes in arterial stiffness and wave reflection with advancing age in healthy men and women: the Framingham Heart Study. Hypertension 43(6):1239–1245

    Article  PubMed  CAS  Google Scholar 

  31. Foo JYA, Lim CS (2006) Pulse transit time as an indirect marker for variations in cardiovascular related reactivity. Technol Health Care 14(2):97–108

    PubMed  Google Scholar 

  32. Schiffrin EL (2004) Vascular stiffening and arterial complianceImplications for systolic blood pressure. Am J Hypertens 17(S3):39S–48S

    Article  PubMed  CAS  Google Scholar 

  33. Yamashina A, Tomiyama H, Arai T, Koji Y, Yambe M, Motobe H et al (2003) Nomogram of the relation of brachial-ankle pulse wave velocity with blood pressure. Hypertens Res 26(10):801–806

    Article  PubMed  Google Scholar 

  34. Hughes DJ, Babbs CF, Geddes LA, Bourland JD (1979) Measurements of Young’s modulus of elasticity of the canine aorta with ultrasound. Ultrason Imaging 1(4):356–367

    Article  PubMed  CAS  Google Scholar 

  35. Nichols W, O’Rourke M, Vlachopoulos C (2011) McDonald’s blood flow in arteries: theoretical, experimental and clinical principles. Hodder Arnold, London

    Google Scholar 

  36. Geddes LA, Voelz M, James S, Reiner D (1981) Pulse arrival time as a method of obtaining systolic and diastolic blood pressure indirectly. Med Biol Eng Comput 19(5):671–672

    Article  PubMed  CAS  Google Scholar 

  37. Tanaka H, Heiss G, McCabe EL, Meyer ML, Shah AM, Mangion JR et al (2016) Hemodynamic correlates of blood pressure in older adults: the Atherosclerosis Risk in Communities (ARIC) Study. J Clin Hypertens 18(12):1222–1227

    Article  Google Scholar 

  38. Nürnberger J, Keflioglu-Scheiber A, Saez AMO, Wenzel RR, Philipp T, Schäfers RF (2002) Augmentation index is associated with cardiovascular risk. J Hypertens 20(12):2407–2414

    Article  PubMed  Google Scholar 

  39. Davies JI, Struthers AD (2003) Pulse wave analysis and pulse wave velocity: a critical review of their strengths and weaknesses. J Hypertens 21(3):463–472

    Article  PubMed  CAS  Google Scholar 

  40. Kelly R, Hayward C, Avolio A, O’rourke M (1989) Noninvasive determination of age-related changes in the human arterial pulse. Circulation 80(6):1652–1659

    Article  PubMed  CAS  Google Scholar 

  41. Millasseau S, Kelly R, Ritter J, Chowienczyk P (2002) Determination of age-related increases in large artery stiffness by digital pulse contour analysis. Clin Sci 103(4):371–377

    Article  PubMed  CAS  Google Scholar 

  42. Zaidi SN, Collins SM (2016) Orthostatic stress and area under the curve of photoplethysmography waveform. Biomed Phys Eng Express 2(4):045006

    Article  Google Scholar 

  43. Voelz M (1981) Measurement of the blood-pressure constant k, over a pressure range in the canine radial artery. Med Biol Eng Comput 19(5):535–537

    Article  PubMed  CAS  Google Scholar 

  44. Elgendi M (2012) On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 8(1):14–25

    Article  PubMed  PubMed Central  Google Scholar 

  45. Esper SA, Pinsky MR (2014) Arterial waveform analysis. Best Pract Res Clin Anaesthesiol 28(4):363–380

    Article  PubMed  Google Scholar 

  46. Moody GB, Mark RG (1996) A database to support development and evaluation of intelligent intensive care monitoring. In: Computers in cardiology 1996, 8–11 Sept 1996, pp 657–660

  47. Pitzalis MV, Mastropasqua F, Massari F, Passantino A, Colombo R, Mannarini A et al (1998) Effect of respiratory rate on the relationships between RR interval and systolic blood pressure fluctuations: a frequency-dependent phenomenon. Cardiovasc Res 38(2):332–339

    Article  PubMed  CAS  Google Scholar 

  48. Bernardi L, Leuzzi S, Radaelli A, Passino C, Johnston JA, Sleight P (1994) Low-frequency spontaneous fluctuations of RR interval and blood pressure in conscious humans: a baroreceptor or central phenomenon? Clin Sci 87(6):649–654

    Article  PubMed  CAS  Google Scholar 

  49. Wang R, Jia W, Mao ZH, Sclabassi RJ, Sun M (2014) Cuff-free blood pressure estimation using pulse transit time and heart rate. In 2014 12th international conference on signal processing (ICSP), 19–23 Oct 2014, pp 115–118

  50. Reusz GS, Cseprekal O, Temmar M, Kis É, Cherif AB, Thaleb A et al (2010) Reference values of pulse wave velocity in healthy children and teenagers. Hypertension 56(2):217–224

    Article  PubMed  CAS  Google Scholar 

  51. Tartiere JM, Logeart D, Beauvais F, Chavelas C, Kesri L, Tabet JY et al (2007) Non-invasive radial pulse wave assessment for the evaluation of left ventricular systolic performance in heart failure. Eur J Heart Fail 9(5):477–483

    Article  PubMed  Google Scholar 

  52. Lopez G, Ushida H, Hidaka K, Shuzo M, Delaunay JJ, Yamada I et al (2009) Continuous blood pressure measurement in daily activities. In: 2009 IEEE sensors, 25–28 Oct 2009, pp 827–831

  53. Lillie JS, Liberson AS, Borkholder DA (2016) Improved blood pressure prediction using systolic flow correction of pulse wave velocity. Cardiovasc Eng Technol 7:439–447

    Article  PubMed  Google Scholar 

  54. Yoon Y, Cho JH, Yoon G (2009) Non-constrained blood pressure monitoring using ECG and PPG for personal healthcare. J Med Syst 33(4):261–266

    Article  PubMed  Google Scholar 

  55. Wibmer T, Denner C, Fischer C, Schildge B, Rudiger S, Kropf-Sanchen C et al (2015) Blood pressure monitoring during exercise: comparison of pulse transit time and volume clamp methods. Blood Press 24(6):353–360

    Article  PubMed  Google Scholar 

  56. Mitchell GF, Pfeffer MA, Finn PV, Pfeffer JM (1997) Comparison of techniques for measuring pulse-wave velocity in the rat. J Appl Physiol 82(1):203–210

    Article  PubMed  CAS  Google Scholar 

  57. Chiu YC, Arand PW, Shroff SG, Feldman T, Carroll JD (1991) Determination of pulse wave velocities with computerized algorithms. Am Heart J 121(5):1460–1470

    Article  PubMed  CAS  Google Scholar 

  58. Harris WS, Schoenfeld CD, Weissler AM (1967) Effects of adrenergic receptor activation and blockade on the systolic preejection period, heart rate, and arterial pressure in man. J Clin Investig 46(11):1704–1714

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Liu Q, Yan BP, Yu C-M, Zhang Y-T, Poon CC (2014) Attenuation of systolic blood pressure and pulse transit time hysteresis during exercise and recovery in cardiovascular patients. IEEE Trans Biomed Eng 61(2):346–352

    Article  PubMed  Google Scholar 

  60. Muehlsteff J, Aubert XA, Morren G (2008) Continuous cuff-less blood pressure monitoring based on the pulse arrival time approach: the impact of posture. In: 2008 30th annual international conference of the IEEE Engineering in Medicine and Biology Society, 20–25 Aug 2008, pp 1691–1694

  61. Payne R, Symeonides C, Webb D, Maxwell S (2006) Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure. J Appl Physiol 100(1):136–141

    Article  PubMed  CAS  Google Scholar 

  62. Virtanen R, Jula A, Kuusela T, Airaksinen J (2004) Beat-to-beat oscillations in pulse pressure. Clin Physiol Funct Imaging 24(5):304–309

    Article  PubMed  Google Scholar 

  63. Sola J, Proenca M, Ferrario D, Porchet J-A, Falhi A, Grossenbacher O et al (2013) Noninvasive and nonocclusive blood pressure estimation via a chest sensor. IEEE Trans Biomed Eng 60(12):3505–3513

    Article  PubMed  Google Scholar 

Download references

Funding

This study was funded by National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2013ZX03005008), and National Key Research and Development Program of China (No. 2017YFF0210803).

Author information

Authors and Affiliations

  1. Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Jingjie Feng, Zhongyi Huang, Congcong Zhou & Xuesong Ye

  2. State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Xuesong Ye

Authors
  1. Jingjie Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongyi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Congcong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuesong Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuesong Ye.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors. The datasets analysed during the current study are available in the PhysioNet repository, https://physionet.org/physiobank/database/mimicdb/.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, J., Huang, Z., Zhou, C. et al. Study of continuous blood pressure estimation based on pulse transit time, heart rate and photoplethysmography-derived hemodynamic covariates. Australas Phys Eng Sci Med 41, 403–413 (2018). https://doi.org/10.1007/s13246-018-0637-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-018-0637-8

Keywords

Search

Navigation