Skip to main content

Continuous Blood Pressure Estimation Using PPG and ECG Signal

  • Conference paper
  • First Online:
Advances in Body Area Networks I

Part of the book series: Internet of Things ((ITTCC))

Abstract

Continuous blood pressure monitor can detect the potential risk of cardiovascular disease and provide a gold standard for clinical diagnosis. The features extracted from photoplethysmography (PPG) and electrocardiogram (ECG) signals can reflect the dynamics of cardiovascular system. In this paper, 39 features are extracted from PPG and ECG signals and 10 features are chosen by analyzing their correlations with blood pressure. Several machine learning algorithms are used to predict the continuous and cuff-less estimation of the diastolic blood pressure and systolic blood pressure. The results shows that compared with linear regression and support vector regression methods, the artificial neural network optimized by genetic algorithm gives a better accuracy for 1 h prediction under Advancement of Medical Instrumentation and the British Hypertension Society standard.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. World Health Statistics 2016 (2016)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Isebree Moens, A.: Die Pulscurve

    Google Scholar 

  5. Yan, Y.S., Zhang, Y.T.: A model-based calibration method for noninvasive and cuffless measurement of arterial blood pressure. In: Biomedical Circuits and Systems Conference, 2006, BioCAS, pp. 234–236

    Google Scholar 

  6. Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28, R1–39 (2007)

    Article  Google Scholar 

  7. Kachuee, M., Kiani, M.M., Mohammadzade, H., Shabany, M.: Cuff-Less Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring, pp. 1–1 (2016)

    Google Scholar 

  8. Kurylyak, Y., Lamonaca, F., Grimaldi, D.: A neural network-based method for continuous blood pressure estimation from a PPG signal. In: IEEE International Instrumentation and Measurement Technology Conference, pp. 280–283

    Google Scholar 

  9. Monte-Moreno, E.: Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif. Intell. Med. 53, 127–138 (2011)

    Article  Google Scholar 

  10. Ghosh, S., Banerjee, A., Ray, N., Wood, P.W., Boulanger, P., Padwal, R.: Continuous blood pressure prediction from pulse transit time using ECG and PPG signals. In: IEEE Healthcare Innovation Point-of-Care Technologies Conference, pp. 188–191

    Google Scholar 

  11. Su, P., Ding, X., Zhang, Y., Miao, F., Zhao, N.: Learning to Predict Blood Pressure with Deep Bidirectional LSTM Network (2017)

    Google Scholar 

  12. Imholz, B.P., Wieling, W., van Montfrans, G.A., Wesseling, K.H.: Fifteen years experience with finger arterial pressure monitoring: assessment of the technology. Cardiovasc. Res. 38, 605–616 (1998)

    Article  Google Scholar 

  13. Singh, B.N., Tiwari, A.K.: Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process. 16, 275–287 (2006)

    Article  Google Scholar 

  14. Dorlas, J.C., Nijboer, J.A.: Photo-electric plethysmography as a monitoring device in anaesthesia. Application and interpretation. Br. J. Anaesth. 57, 524–530 (1985)

    Article  Google Scholar 

  15. Chua, E.C., Redmond, S.J., Mcdarby, G., Heneghan, C.: Towards using photo-plethysmogram amplitude to measure blood pressure during sleep. Ann. Biomed. Eng. 38, 945–954 (2010)

    Article  Google Scholar 

  16. Wang, L., Pickwell-Macpherson, E., Liang, Y.P., Zhang, Y.T.: Noninvasive cardiac output estimation using a novel photoplethysmogram index. In: International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 1746–1749

    Google Scholar 

  17. Takazawa, K., Tanaka, N., Fujita, M., Matsuoka, O., Saiki, T., Aikawa, M., Tamura, S., Ibukiyama, C.: Assesment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform. Hypertension 32, 365–370 (1998)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Yang, H., Zhou, Q., Xiao, J.: Relationship between vascular elasticity and human pulse waveform based on FFT analysis of pulse waveform with different age. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4

    Google Scholar 

  20. Mahmoodabadi, S.Z., Ahmadian, A., Abolhasani, M.D., Eslami, M.: ECG feature extraction based on multiresolution wavelet transform. In: IEEE Engineering in Medicine & Biology Conference, pp. 3902–3905

    Google Scholar 

  21. Antonelli, L., Ohley, W., Khamlach, R.: Dicrotic notch detection using wavelet transform analysis. In: Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the International Conference of the IEEE, vol. 1212, pp. 1216–1217

    Google Scholar 

  22. He, R., Huang, Z.P., Ji, L.Y., Wu, J.K.: Beat-to-beat ambulatory blood pressure estimation based on random forest. In: IEEE International Conference on Wearable and Implantable Body Sensor Networks, pp. 194–198

    Google Scholar 

  23. Chang, C.C., Lin, C.J.: LIBSVM: A library for Support Vector Machines. ACM (2011)

    Google Scholar 

  24. Peng, L.I., Liu, M., Zhang, X., Xiaohui, H.U., Pang, B., Yao, Z., Nhen, H.: Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography. Sci. China Inf. Sci. 59, 1–10 (2016)

    Google Scholar 

  25. Goldberg, D.E.: Genetic Algorithm in Search, Optimization, and Machine Learning, vol. xiii, pp. 2104–2116 (1989)

    Google Scholar 

  26. O’brien, E., Petrie, J., Littler, W., Padfield, P.L., O’Malley, K., Jamieson, M., Altman, D., Bland, M., Atkins, N.: The British Hypertension Society protocol for the evaluation of automated and semi-automated blood pressure measuring devices with special reference to ambulatory systems. J. Hypertens. 8, 607–619 (1990)

    Google Scholar 

  27. White, W.B., Berson, A.S., Robbins, C., Jamieson, M.J., Prisant, L.M., Roccella, E., Sheps, S.G.: National standard for measurement of resting and ambulatory blood pressures with automated sphygmomanometers. Hypertension 21, 504 (1993)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Special Fund for Scientific Research Cooperation of University Chinese Academy of Sciences.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhipei Huang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, B., Huang, Z., Wu, J., Liu, Z., Liu, Y., Zhang, P. (2019). Continuous Blood Pressure Estimation Using PPG and ECG Signal. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02819-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02818-3

  • Online ISBN: 978-3-030-02819-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics