Skip to main content
SpringerLink
Log in

A mixed attention-gated U-Net for continuous cuffless blood pressure estimation

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Blood pressure (BP) is an important vital sign of the human body. The traditional cuff measurement methods are mainly intermittent, which cannot meet the clinical practice well. Continuous measurements of BP are of great significance for monitoring vital signs of patients. In order to achieve BP estimation in a continuous, cuffless and non-invasive way, this paper proposes a BP estimation model based on mixed attention gating U-Net (MAGU), which can effectively improve the accuracy and efficiency of BP estimation. The photoplethysmography signal is fed into the improved U-Net to extract features. A mixed attention gating mechanism is added between up-sampling and down-sampling, as well as the residual blocks are added in down-sampling to prevent the vanishing gradient, so as to improve the feature extraction efficiency in the deep network. The performance of the MAGU is validated against those state-of-the-art results on the MIMIC-II public dataset. The mean absolute error and standard deviation of systolic blood pressure predicted by the proposed method are 3.49 mmHg and 4.13 mmHg respectively, and those of diastolic blood pressure (DBP) are 2.11 mmHg and 2.49 mmHg. The comparison shows that the proposed method outperforms those state-of-the-art methods.

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
Fig. 7
Fig. 8

Similar content being viewed by others

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Tan, Z., Xu, Y.: The study of blood pressure measurement system based on PPG and machine learning. Med. Equip. 33, 26–28 (2020)

    Google Scholar 

  2. Yamakoshi, K.-I., Shimazu, H., Togawa, T.: Indirect measurement of instantaneous arterial blood pressure in the human finger by the vascular unloading technique. IEEE Trans. Biomed. Eng. BME–27, 150–155 (1980)

  3. Janelle, G.M., Gravenstein, N.: An accuracy evaluation of the T-Line Tensymeter (continuous noninvasive blood pressure management device) versus conventional invasive radial artery monitoring in surgical patients. Anesth. Analg. 102, 484–490 (2006)

    Article  Google Scholar 

  4. Fiori, G., Fuiano, F., Scorza, A., Conforto, S., Sciuto, S.A.: Non-invasive methods for PWV measurement in blood vessel stiffness assessment. IEEE Rev. Biomed. Eng. 15, 169–183 (2022)

    Article  Google Scholar 

  5. Teng, X., Zhang, Y.: Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439) 4, 3153–3156 (2003)

  6. Sen, Y., Morgan, S. P., Cho, S.-Y., Yaping, Z.: The influence of physiological characteristics on blood pressure estimation using only PPG signals. In: 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) 1694–1700 (2019)

  7. Hassani, A., Foruzan, A.H.: Improved PPG-based estimation of the blood pressure using latent space features. SIViP 13, 1141–1147 (2019)

    Article  Google Scholar 

  8. Yao, L.-P., Pan, Z.-L.: Cuff-less blood pressure estimation from photoplethysmography signal and electrocardiogram. Phys. Eng. Sci. Med. 44, 397–408 (2021)

    Article  Google Scholar 

  9. Yen, C.-T., Liao, J.-X., Huang, Y.-K.: Applying a deep learning network in continuous physiological parameter estimation based on photoplethysmography sensor signals. IEEE Sens. J. 22, 385–392 (2022)

    Article  Google Scholar 

  10. Jean Effil, N., Rajeswari, R.: Wavelet scattering transform and long short-term memory network-based noninvasive blood pressure estimation from photoplethysmograph signals. SIViP 16, 1–9 (2022)

    Article  Google Scholar 

  11. El-Hajj, C., Kyriacou, P.: Deep learning models for cuffless blood pressure monitoring from ppg signals using attention mechanism. Biomed. Signal Process. Control 65, 102301 (2021)

    Article  Google Scholar 

  12. El-Hajj, C., Kyriacou, P.: Cuffless blood pressure estimation from PPG signals and its derivatives using deep learning models. Biomed. Signal Process. Control 70, 102984 (2021)

    Article  Google Scholar 

  13. Tanveer, M.S., Hasan, M.K.: Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ann-lstm network. Biomed. Signal Process. Control 51, 382–392 (2019)

    Article  Google Scholar 

  14. Rong, M., Li, K.: A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography. Biomed. Signal Process. Control 68, 102772 (2021)

    Article  Google Scholar 

  15. Yu, M., Huang, Z., Zhu, Y., Zhou, P., Zhu, J.: Attention-based residual improved u-net model for continuous blood pressure monitoring by using photoplethysmography signal. Biomed. Signal Process. Control 75, 103581 (2022)

    Article  Google Scholar 

  16. Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2011–2023 (2020)

    Article  Google Scholar 

  17. Yang, B., Wu, M., Teizer, W.: Modified unet++ with attention gate for graphene identification by optical microscopy. Carbon 195, 246–252 (2022)

    Article  Google Scholar 

  18. Woo, S., Park, J., Lee, J.-Y., Kweon, I. S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV) 3–19 (2018)

  19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (2016)

  20. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. Med. Image Comput. Comput.-Assist. Interv. MICCAI 2015, 234–241 (2015)

    Google Scholar 

  21. Saeed, M., Villarroel, M., Reisner, A.T., Clifford, G., Lehman, L.W., Moody, G., Heldt, T., Kyaw, T.H., Moody, B., Mark, R.G.: Multiparameter intelligent monitoring in intensive care II: a public-access intensive care unit database. Crit. Care Med. 39, 952–960 (2011)

    Article  Google Scholar 

  22. Mukkamala, R., Hahn, J.-O., Inan, O.T., Mestha, L.K., Kim, C.-S., Töreyin, H., Kyal, S.: Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans. Biomed. Eng. 62, 1879–1901 (2015)

    Article  Google Scholar 

  23. Schlesinger, O., Vigderhouse, N., Eytan, D., Moshe, Y.: Blood pressure estimation from ppg signals using convolutional neural networks and siamese network. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1135–1139 (2020)

  24. Sharifi, I., Goudarzi, S., Khodabakhshi, M.B.: A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals. Artif. Intell. Med. 97, 143–151 (2019)

    Article  Google Scholar 

  25. Chen, Y., Zhang, D., Karimi, H.R., Deng, C., Yin, W.: A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation. Neural Netw. 152, 181–190 (2022)

    Article  Google Scholar 

  26. Khawaja, R.A., Qureshi, R., Mansure, A.H., Yahya, M.E.: Validation of datascope accutorr plus\(^{\text{ TM }}\) using british hypertension society (BHS) and association for the advancement of medical instrumentation (aami) protocol guidelines. J. Saudi Heart Assoc. 22, 1–5 (2010)

    Article  Google Scholar 

  27. Kachuee, M., Kiani, M.M., Mohammadzade, H., Shabany, M.: Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Trans. Biomed. Eng. 64, 859–869 (2017)

  28. Baek, S., Jang, J., Yoon, S.: End-to-end blood pressure prediction via fully convolutional networks. IEEE Access 7, 185458–185468 (2019)

    Article  Google Scholar 

  29. Hasanzadeh, N., Ahmadi, M.M., Mohammadzade, H.: Blood pressure estimation using photoplethysmogram signal and its morphological features. IEEE Sens. J. 20, 4300–4310 (2020)

    Article  Google Scholar 

  30. Maqsood, S., Xu, S., Springer, M., Mohawesh, R.: A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (ppg). IEEE Access 9, 138817–138833 (2021)

    Article  Google Scholar 

  31. Haddad, S., Boukhayma, A., Caizzone, A.: Continuous PPG-based blood pressure monitoring using multi-linear regression. IEEE J. Biomed. Health Inform. 26, 2096–2105 (2022)

    Article  Google Scholar 

  32. Wang, W., Mohseni, P., Kilgore, K.L., Najafizadeh, L.: Cuff-less blood pressure estimation from photoplethysmography via visibility graph and transfer learning. IEEE J. Biomed. Health Inform. 26, 2075–2085 (2022)

    Article  Google Scholar 

  33. Huang, B., Chen, W., Lin, C.-L., Juang, C.-F., Wang, J.: MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-mixer neural networks. Biomed. Signal Process. Control 73, 103404 (2022)

    Article  Google Scholar 

  34. Hu, Q., Wang, D., Yang, C.: PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning. Biomed. Signal Process. Control 78, 103891 (2022)

    Article  Google Scholar 

  35. Zhang, L., Ji, Z., Yang, F., Chen, G.: Noninvasive continuous blood pressure estimation with fewer parameters based on RA-ReliefF feature selection and MPGA-BPN models. Biomed. Signal Process. Control 84, 104757 (2023)

    Article  Google Scholar 

  36. Panwar, M., Gautam, A., Biswas, D., Acharyya, A.: PP-net: a deep learning framework for PPG-based blood pressure and heart rate estimation. IEEE Sens. J. 20, 10000–10011 (2020)

    Article  Google Scholar 

  37. Biswas, D., Everson, L., Liu, M., Panwar, M., Verhoef, B.-E., Patki, S., Kim, C.H., Acharyya, A., Van Hoof, C., Konijnenburg, M., Van Helleputte, N.: Cornet: deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment. IEEE Trans. Biomed. Circuits Syst. 13, 282–291 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang Province, China

    Yiting Zhong, Yongyi Chen & Dan Zhang

  2. Department of Physical Examination Center, Zhejiang Hospital, Hangzhou, 310013, Zhejiang Province, China

    Yanghui Xu

  3. Department of Mechanical Engineering, Politecnico di Milano, 20156, Milan, Italy

    Hamid Reza Karimi

Authors
  1. Yiting Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanghui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamid Reza Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ: contributed to conceptualization, methodology, software, data curation, writing—original draft preparation. YC contributed to conceptualization, software, data curation, methodology, validation, writing—original draft preparation. DZ contributed to methodology, visualization, investigation, resources, supervision, writing—reviewing and editing. YX contributed to methodology, writing—reviewing and editing, validation, formal analysis, data curation. HRK contributed to writing—reviewing and editing, formal analysis, investigation, validation, supervision.

Corresponding author

Correspondence to Hamid Reza Karimi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, Y., Chen, Y., Zhang, D. et al. A mixed attention-gated U-Net for continuous cuffless blood pressure estimation. SIViP 17, 4143–4151 (2023). https://doi.org/10.1007/s11760-023-02646-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02646-4

Keywords

Search

Navigation