Skip to main content
SpringerLink
Log in

A Multi-feature Fusion Method to Estimate Blood Pressure by PPG

  • Conference paper
  • First Online:
Smart Health (ICSH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10219))

Included in the following conference series:

Abstract

Effective features extracted from Photoplethysmogram (PPG) are the central for estimating accurately blood pressure (BP). To make extracted features have a strong correlation with real blood pressure, a model based on feature fusion is presented to evaluate blood pressure. To divide pulse wave into two types of dicrotic wave and non-dicrotic wave, different types of waveforms use different extracted features, and wavelet transform is used to remove the noise from the extracted features. Linear regression model and neural network are evaluation models, and Matlab system identification toolboxes are used to recognize the model parameters. The experiment results have shown that extracted features have a correlation with systolic pressure (SP) and diastolic pressure (DP). The value of blood pressure can be calculated based on features extracted from PPG. What’s more, the accuracy of the fusion feature model is improved compared with the traditional method only by using one type of extracted feature method for all the pulse waveforms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Gu, Y.Y, Zhang, Y., Zhang, Y.T.: A novel biometric approach in human verification by photoplethysmographic signals. In: International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 13–14 (2003)

    Google Scholar 

  2. Barbe, K., Van, Moer W., Schoors, D.: Analyzing the Windkessel model as a potential candidate for correcting oscillometric blood-pressure measurements. IEEE Trans. Instrum. Meas. 61(2), 411–418 (2012)

    Article  Google Scholar 

  3. Kim, J., Kim, W.S.: A paired stretchable printed sensor system for ambulatory blood pressure monitoring. Sens. Actuators Phys. 238, 329–336 (2016)

    Article  Google Scholar 

  4. Fortino, G., Giampà, V.: PPG-based methods for non invasive and continuous BP measurement: an overview and development issues in body sensor networks. In: Proceedings of IEEE International Workshop on Medical Measurements and Applications (MeMeA 2010), Ottawa, pp. 10–13 (2010)

    Google Scholar 

  5. Biel, L., Pettersson, O., Philipson, L., et al.: ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Meas. 50(3), 808–812 (2001)

    Article  Google Scholar 

  6. Hegde, C., Prabhu, H.R., Sagar, D.S., et al.: Statistical analysis for human authentication using ECG waves. Commun. Comput. Inf. Sci. 141, 287–298 (2011)

    Google Scholar 

  7. Bernardi, L., Gordin, D., Rosengårdbärlund, M., et al.: Arterial function can be obtained by noninvasive finger pressure waveform. Int. J. Cardiol. 175(1), 169–171 (2014)

    Article  Google Scholar 

  8. Pickering, T.G., Hall, J.E., Appel, L., et al.: Response to recommendations for blood pressure measurement in human and experimental animals; Part 1: blood pressure measurement in humans and miscuffing: a problem with new guidelines: addendum. Hypertension 48(1), 686–693 (2006)

    Article  Google Scholar 

  9. He, X., Goubran, R., Liu, X.P.: Secondary peak detection of PPG signal for continuous cuffless arterial blood pressure measurement. IEEE Trans. Instrum. Meas. 63(6), 1431–1439 (2014)

    Article  Google Scholar 

  10. Marques, F., Ribeiro, D., Colunas, M., et al.: A truly wearable medical device for ECG, PPG and blood pressure monitoring. IEEE Trans. Biomed. Eng (2010, submitted)

    Google Scholar 

  11. Bose, S.S.N., Kumar, C.S.: Improving the performance of continuous non-invasive estimation of blood pressure using ECG and PPG. In: IEEE Indicon (2015)

    Google Scholar 

  12. Spulak, D., Cmejla, R., Fabian, V.: Parameters for mean blood pressure estimation based on electrocardiography and photoplethysmography. In: International Conference on Applied Electronics, vol. 123, no. Suppl. 1, pp. 1–4 (2011)

    Google Scholar 

  13. Kachuee, M., Kiani, M.M., Mohammadzade, H., et al.: Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time. In: IEEE International Symposium on Circuits and Systems, pp. 1006–1009. IEEE (2015)

    Google Scholar 

  14. Puke, S., Suzuki, T., Nakayama, K., et al.: Blood pressure estimation from pulse wave velocity measured on the chest. In: International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 6107–6110 (2013)

    Google Scholar 

  15. Kurylyak, Y., Lamonaca, F., Grimaldi, D.: A neural network-based method for continuous blood pressure estimation from a PPG signal. IEEE Instrum. Meas. Technol. Conf. 80(11), 280–283 (2013)

    Google Scholar 

  16. O’Rourke, M.F.: Time domain analysis of the arterial pulse in clinical medicine. Med. Biol. Eng. Comput. 47(2), 119–129 (2009)

    Article  Google Scholar 

  17. Yan, Y.S., Zhang, Y.T.: Noninvasive estimation of blood pressure using photoplethysmographic signals in the period domain. In: Proceedings of 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS 2005), pp. 3583–3584 (2005)

    Google Scholar 

  18. Nitzan, M., Patron, A., Glik, Z., et al.: Automatic noninvasive measurement of systolic blood pressure using photoplethysmography. BioMed. Eng. OnLine 8(1), 1–8 (2009)

    Article  Google Scholar 

  19. Laurent, C., Jonsson, B., Vegfors, M., et al.: Noninvasive monitoring of systolic blood pressure on the arm utilizing photoplethysmography (PPG): clinical report. In: Biomedical Optics. International Society for Optics and Photonics, pp. 99–107 (2004)

    Google Scholar 

  20. Li, Z-J., Wang, C., Zhu, H., Jin, F., Ma, J-L.: The research progress of non-invasive and continuous blood pressure measurement based on photoplethysmography. Chin. J. Biomed. Eng. 31(4), 607–614 (2012)

    Google Scholar 

  21. Yao, R., Zhang, Y., Chen, L., et al.: Identification of parameters of blood pressure measurement based on pulse wave. Med. Health Equip. 37(1), 5–7 (2016)

    Google Scholar 

  22. Lu, H., Yan, Z., Lu, W.: A noninvasive and continuous method for blood pressure measurement using pulse wave. Chin. J. Med. Instrum. (Zhongguo yi liao qi xie za zhi) 35(35), 169–173 (2011)

    Google Scholar 

  23. Mills, A.K.: Device and method for noninvasive continuous determination of physiologic characteristics. US, US6921367[P] (2005)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61672181, 61202090, 61272184), Natural Science Foundation of Heilongjiang Province (No. F2016005), the Science and Technology Innovation Talents Special Fund of Harbin (No. 2016RAXXJ 036, 2015RQQXJ067), the opening found of Key Laboratory of Machine Perception (Ministry of Education), Peking University (K-2016-02).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Nantong Street No. 145, Harbin, 150001, China

    Lifang Meng, Zhiqiang Zhang, Yu Miao, Xiaoqin Xie & Haiwei Pan

Authors
  1. Lifang Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoqin Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haiwei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiqiang Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Chunxiao Xing

  2. Tsinghua University, Beijing, China

    Yong Zhang

  3. Beijing Foreign Studies University, Beijing, China

    Ye Liang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Meng, L., Zhang, Z., Miao, Y., Xie, X., Pan, H. (2017). A Multi-feature Fusion Method to Estimate Blood Pressure by PPG. In: Xing, C., Zhang, Y., Liang, Y. (eds) Smart Health. ICSH 2016. Lecture Notes in Computer Science(), vol 10219. Springer, Cham. https://doi.org/10.1007/978-3-319-59858-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59858-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59857-4

  • Online ISBN: 978-3-319-59858-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation