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Physical Constitution Discrimination Based on Pulse Characteristics

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1160))

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Abstract

Pulse palpation is an important diagnostic tool in Traditional Chinese Medicine (TCM) and related Oriental medicine systems. Pulse wave contains a lot of physiological and pathological information. How to effectively extract the information contained in pulse wave has been concerned at home and abroad. In this paper, a comprehensive introduction about the pulse wave characteristic is given. Furthermore, a new method of distinguishing students’ physical constitution based on pulse characteristic information is proposed. First, pulse data were collected, preprocessed and pulse cycles were segmented. Second, time domain and pulse features coefficients of pulse wave were extracted. Finally, useful pulse wave features were evaluated and the features are classified to distinguish students’ constitution by SVM classifier. Number experiments have proved the correctness and feasibility of the proposed theory.

Supported by key project at central government level (2060302).

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References

  1. Wu, H.K., Ko, Y.S., Lin, Y.S., Wu, H.T., Tsai, T.H., Chang, H.H.: The correlation between pulse diagnosis and constitution identification in traditional Chinese medicine. Complement. Ther. Med. 30, 107–112 (2017)

    Article  Google Scholar 

  2. Velik, R.: An objective review of the technological developments for radial pulse diagnosis in traditional Chinese medicine. Eur. J. Integr. Med. 7(4), 321–331 (2015)

    Article  Google Scholar 

  3. De, M.N., Cordovil, I., De, S.F.A.: Traditional Chinese medicine wrist pulse-taking is associated with pulse waveform analysis and hemodynamics in hypertension. J. Integr. Med. 14(2), 100–113 (2016)

    Article  Google Scholar 

  4. Zhang, Z., Zhang, Y., Yao, L., Song, H., Kos, A.: A sensor-based wrist pulse signal processing and lung cancer recognition. J. Biomed. Inform. 79, 107–116 (2018)

    Article  Google Scholar 

  5. Qiao, L.J., et al.: The association of radial artery pulse wave variables with the pulse wave velocity and echocardiographic parameters in hypertension. Evid.-Based Complement. Altern. Med. 2018 (2018). Article ID 5291759

    Google Scholar 

  6. Leonard, P., Beattie, T.F., Addison, P.S., Watson, J.N.: Wavelet analysis of pulse oximeter waveform permits identification of unwell children. J Energ. Med. 21, 59–60 (2004)

    Google Scholar 

  7. Xu, L., Wang, K., Li, Y.: Modern researches on traditional Chinese pulse diagnosis. Eur. J. Orient. Med. 8(1), 56–63 (2004)

    Google Scholar 

  8. Jeon, Y.J., et al.: A clinical study of the pulse wave characteristics at the three pulse diagnosis positions of Chon, Gwan and Cheok. Evid.-Based Complement. Altern. Med. 2011 (2011). Article ID 904056

    Google Scholar 

  9. Bae, J.H., Jeon, Y.J., Kim, J.Y., Kim, J.U.: New assessment model of pulse depth based on sensor displacement in pulse diagnostic devices. Evid.-Based Complement. Altern. Med. 2013 (2013). Article ID 938641

    Google Scholar 

  10. Yallapragada, V.J., Rigneault, H., Oron, D.: Spectrally narrow features in a supercontinuum generated by shaped pulse trains. Opt. Express 26(5), 5694–5700 (2018)

    Article  Google Scholar 

  11. Khanna, A., Paul, M., Sandhu, J.S.: Efficacy of two relaxation techniques in reducing pulse rate among highly stressed females. Calicut Med. J. 5(2), 23–25 (2007)

    Google Scholar 

  12. RibeirodeMoura, N.G., Cordovil, I., de S\(\acute{a^{}}\) Ferreira, A.: Traditional Chinese medicine wrist pulse-taking is associated with pulse waveform analysis and hemodynamics in hypertension. J. Integr. Med. 14, 100–113 (2016)

    Google Scholar 

  13. Moura, N.G.R., Ferreira, A.: Pulse waveform analysis of Chinese pulse images and its association with disability in hypertension. JAMS J. Acupunct. Meridian Stud. 9, 93–98 (2016)

    Article  Google Scholar 

  14. Xu, J., Yang, Y.: Traditional Chinese medicine in the Chinese health care system. Health Policy 90(2–3), 133–139 (2009)

    Article  Google Scholar 

  15. Nestler, G.: Traditional Chinese medicine. Med. Clin. 86(1), 63–73 (2002)

    Google Scholar 

  16. Bilton, K., Zaslawski, C.: Reliability of manual pulse diagnosis methods in traditional East Asian medicine: a systematic narrative literature review. J. Altern. Complement. Med. 22(8), 599–609 (2016)

    Article  Google Scholar 

  17. Hajar, R.: The pulse in ancient medicine part 1. Heart Views Off. J. Gulf Heart Assoc. 19(1), 36 (2018)

    Google Scholar 

  18. Tang, A.C.Y., Chung, J.W.Y., Wong, T.K.S.: Validation of a novel traditional chinese medicine pulse diagnostic model using an artificial neural network. Evid. Based Complement Altern. Med. 2012 (2012). Article ID 685094

    Google Scholar 

  19. Huan, E.Y., et al.: Deep convolutional neural networks for classifying body constitution based on face image. Comput. Math. Methods Med. 2017 (2017). Article ID 9846707

    Google Scholar 

  20. Li, X., et al.: Computerized wrist pulse signal diagnosis using gradient boosting decision tree. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine, pp. 1941–1947. IEEE, Madrid, Spain (2018)

    Google Scholar 

  21. Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G., Chen, J.: Detection of malicious code variants based on deep learning. IEEE Trans. Industr. Inf. 14(7), 3187–3196 (2018)

    Article  Google Scholar 

  22. Cui, Z., Du, L., Wang, P., Cai, X., Zhang, W.: Malicious code detection based on CNNs and multi-objective algorithm. J. Parallel Distrib. Comput. 129, 50–58 (2019)

    Article  Google Scholar 

  23. Chen, W.H., Hsu, S.H., Shen, H.P.: Application of SVM and ANN for intrusion detection. Comput. Oper. Res. 32(10), 2617–2634 (2005)

    Article  Google Scholar 

  24. Lu, X., Fan, B., Huang, M.: A novel LS-SVM modeling method for a hydraulic press forging process with multiple localized solutions. IEEE Trans. Industr. Inf. 11(3), 663–670 (2015)

    Article  Google Scholar 

  25. Yu, P.S., Chen, S.T., Chang, I.F.: Support vector regression for real-time flood stage forecasting. J. Hydrol. 328(3–4), 704–716 (2006)

    Article  Google Scholar 

  26. Salcedo, S.S., Deo, R.C., Carro, C.L.: Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoret. Appl. Climatol. 125(1–2), 13–25 (2016)

    Article  Google Scholar 

  27. Yang, X.B., Liang, Z.H., Zhang, G.: A classification algorithm for TCM syndromes based on P-SVM. In: International Conference on Machine Learning and Cybernetics, pp. 3692–3697. IEEE, Guangzhou, China (2005)

    Google Scholar 

  28. Cho, B.H., Yu, H., Kim, K.W., Kim, T.H., Kim, I.Y., Kim, S.I.: Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artif. Intell. Med. 42(1), 37–53 (2008)

    Article  Google Scholar 

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Acknowledgments

The work is supported by key project at central government level (Grant No. 2060302).

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Correspondence to Luqi Huang .

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Li, N., Zhao, Y., Mao, X., Wang, Y., Shang, Y., Huang, L. (2020). Physical Constitution Discrimination Based on Pulse Characteristics. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_30

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  • DOI: https://doi.org/10.1007/978-981-15-3415-7_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3414-0

  • Online ISBN: 978-981-15-3415-7

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