Skip to main content

Advertisement

Log in

A Distinguishing Arterial Pulse Waves Approach by Using Image Processing and Feature Extraction Technique

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Traditional Chinese Medicine (TCM) is based on five main types of diagnoses methods consisting of inspection, auscultation, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by doctor’s fingers for detecting patient’s health state. In this paper, it is carried out by using a specialized pulse measuring instrument to classify one’s pulse type. The measured pulse waves (MPWs) were segmented into the arterial pulse wave curve (APWC) by image proposing method. The slopes and periods among four specific points on the APWC were taken to be the pulse features. Three algorithms are proposed in this paper, which could extract these features from the APWCs and compared their differences between each of them to the average feature matrix, individually. These results show that the method proposed in this study is superior and more accurate than the previous studies. The proposed method could significantly save doctors a large amount of time, increase accuracy and decrease data volume.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Hong, Z.H., Pulse diagnosis: the unique medical diagnostic methods. Lide, Taipei, 1992.

    Google Scholar 

  2. Huang, J.M., Modern pulse diagnosis and pulse graphics. Salon, Taipei, 2007.

    Google Scholar 

  3. Wang, H., and Cheng, Y., “A Quantitative System for Pulse Diagnosis in Traditional Chinese Medicine,” Proceedings of the 27-th Annual Conference on Engineering in Medicine and Biology, pp. 5676–5679, Shanghai, China, 17–18 Jan. 2006.

  4. Wang, B., Guo, H., Zheng, S., and Yang, X., “TCM Pulse-Condition Classification Method Based on BP Neural Network,” Proceedings of first International Conference on Bioinformatics and Biomedical Engineering, pp. 629–632, 2007.

  5. Yang, Y.L., and Chang, H.H., The study of radial sphygmogram in patients with heart failure. J. Chin. Med. 17(3):85–94, 2006.

    Google Scholar 

  6. Ogiela, L., Computational intelligence in cognitive healthcare information systems. Stud. Comput. Intell. 309:347–369, 2010.

    Google Scholar 

  7. Huang, C.M., Wei, C.C., Liao, Y.T., Chang, H.C., Kao, S.T., and Li, T.C., Developing the effective method of spectral harmonic energy ratio to analyze the arterial pulse spectrum. Evid. Based Complement. Alternat. Med. 2011:Article ID 342462 , 2011.7 pages

    Google Scholar 

  8. Wang, C.A., Chen, H.C., and Chen, Y.L., A method using image processing technique to automatically extract the specific arterial pulse wave pattern . Asia University, Taiwan, 2010.Master Thesis

    Google Scholar 

  9. Zhang, D., Zhang, L., Zhang, D., and Zheng, Y., “Wavelet based analysis of doppler ultrasonic wrist-pulse signals,” Proceedings of International Conference on BioMedical Engineering and Informatics, pp. 539–543, 2008.

  10. Xia, C., Liu, R., Wang, Y., Yan, H., and Gewiss, H., “Wrist Pulse Analysis Based on RP and RQA,” Proceedings of 2nd International Conference on Biomedical Engineering and Informatics, pp. 1–5, 2009.

  11. Yoon, Y.Z., Lee, M.H., and Soh, K.S., Pulse type classification by varying contact pressure. IEEE Eng. Med. Biol. Mag. 19(6):106–110, 2000.

    Article  CAS  PubMed  Google Scholar 

  12. Lu, W.A., Lin Wang, Y.Y., and Wang, W.K., Pulse analysis of patients with severe liver problems. Studying pulse spectrums to determine the effects on other organs. IEEE Eng. Med. Biol. Mag. 18(1):73–75, 1999.

    Article  CAS  PubMed  Google Scholar 

  13. Wu, S. C., Chen, H. S., Liou, C. C., Jhong, H. M., and Liou, M. C., “The Study of Optimal Amount Pacemaker Pulse Wave Localization by Using the Homemade the Measuring Instrument of Pulse Wave Conduction Velocity,” National Symposium on Telecommunications, 2007.

  14. Wei, C.C., Huang, C.M., and Liao, Y.T., The exponential decay characteristic of the spectral distribution of blood pressure wave in radial artery. Comput. Biol. Med. 39(5):453–459, 2009.

    Article  PubMed  Google Scholar 

  15. Hachaj, T., and Ogiela, M.R., CAD system for automatic analysis of CT perfusion maps. Opto-Electron. Rev. 19(1):95–103, 2011.

    Article  Google Scholar 

  16. Ogiela, L., and Ogiela, M.R., Cognitive systems and bio-inspired computing in homeland security. J. Netw. Comput. Appl. 38:34–42, 2014.

    Article  Google Scholar 

  17. Ogiela, M.R., and Bodzioch, S., Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions. Opto-Electron. Rev. 19(2):155–168, 2011.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by the Ministry of Science and Technology (MOST), Taiwan, Republic of China, under Grant MOST 104-2221-E-468-002.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hsing-Chung Chen.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, HC., Kuo, SS., Sun, SC. et al. A Distinguishing Arterial Pulse Waves Approach by Using Image Processing and Feature Extraction Technique. J Med Syst 40, 215 (2016). https://doi.org/10.1007/s10916-016-0568-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0568-4

Keywords

Navigation