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The novel three-dimensional pulse images analyzed by dynamic L-cube polynomial model

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Abstract

A dynamic L-cube polynomial is proposed to analyze dynamic three-dimensional pulse images (d3DPIs), as an extension of the previous static L-cube polynomial. In this paper, a weighted least squares (WLS) method is proposed to fit the amplitude C(t) of d3DPI at four physiological key points in addition to the best fit of L-cube polynomials to the measured normal and cold-pressor-test (CPT)-induced taut 3DPIs. Compared with other two fitting functions, C(t) of a dynamic L-cube polynomial can be well matched by the proposed WLS method with the least relative error at four physiological key points in one beat with statistical significance, in addition to the best fit of the measured 3DPIs. Therefore, a dynamic L-cube polynomial can reflect dynamic time characteristics of normal and CPT-induced hypertensive taut 3DPIs, which can be used as an evidence of hypertension diagnosis.

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References

  1. Luo CH, Chung YF, Hu CS, Yeh CC, Si XC, Feng DH, Lee YC, Huang SI, Yeh SM, Liang CH (2012) Possibility of quantifying TCM finger-reading sensations: I. Bi-sensing pulse diagnosis instrument. Eur J Integr Med 4:e255–e262

    Article  Google Scholar 

  2. Chu YW, Luo CH, Chung YF, Hu CS, Yeh CC (2014) Using an array sensor to determine differences in pulse diagnosis-three positions and nine indicators. Eur J Integr Med 6:516–523

    Article  Google Scholar 

  3. Hu CS, Chung YF, Yeh CC, Luo CH (2012) Temporal and spatial properties of arterial pulsation measurement using pressure sensor array. Evid-Based Compl Alt Med 745127:10

    Google Scholar 

  4. Huang TY (2014) Radial pulse detection and analysis of local cold stimulation test by multiple dimension pulse mapping method. In: National Cheng Kung University, Tainan, pp. 49–50

  5. Sandwell DT (1987) Biharmonic spline interpolation of GEOS-3 and SEASAT altimeter data. Geophys Res Lett 14:139–142

    Article  Google Scholar 

  6. O’Rourke MF (2009) Time domain analysis of the arterial pulse in clinical medicine. Med Biol Eng Comput 47:119–129

    Article  Google Scholar 

  7. Melis MD, Morbiducci U, Rietzschel ER, Buyzere MD, Qasem A, Bortel LV, Claessens T, Montevecchi FM, Avolio A, Segers P (2009) Blood pressure waveform analysis by means of wavelet transform. Med Biol Eng Comput 47:165–173

    Article  Google Scholar 

  8. Avolio AP, Butlin M, Walsh A (2010) Arterial blood pressure measurement and pulse wave analysis—their role in enhancing cardiovascular assessment. Physiol Meas 31:R1–R47

    Article  Google Scholar 

  9. Gao MW, Zhang GQ, Olivier NB, Mukkamala R (2014) Improved pulse wave velocity estimation using an arterial tube-load model. IEEE Trans Biomed Eng 61:848–858

    Article  Google Scholar 

  10. Sommermeyer D, Zou D, Ficker JH, Randerath WJ, Fischer C, Penzel T, Sanner B, Hedner J, Grote L (2015) Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm. Med Biol Eng Comput 54:1111–1121

    Article  Google Scholar 

  11. Tsai YN, Huang YC, Lin SJS, Lee SM, Cheng YY, Chang YH, Su YC (2018) Different harmonic characteristics were found at each location on TCM radial pulse diagnosis by spectrum analysis. Evid-Based Compl Alt Med 9018271:10

    Google Scholar 

  12. Shu JJ, Sun YG (2007) Developing classification indices for Chinese pulse diagnosis. Complement Ther Med 15:190–198

    Article  Google Scholar 

  13. Chen YH, Zhang L, Zhang D, Zhang DY (2009) Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-means classification. Med Eng Phys 31:1283–1289

    Article  Google Scholar 

  14. Chen YH, Zhang L, Zhang D, Zhang DY (2011) Computerized wrist pulse signal diagnosis using modified auto-regressive models. J Med Syst 35:321–328

    Article  Google Scholar 

  15. Wang L, Xu LS, Feng FT, Meng MQH, Wang KQ (2013) Multi-Gaussian fitting for pulse waveform using weighted least squares and multi-criteria decision-making method. Comput Biol Med 43:1661–1672

    Article  Google Scholar 

  16. Jiang ZX, Zhang D, Lu GM (2019) Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series. Comput Methods Prog Biomed 174:25–31

    Article  Google Scholar 

  17. Wang DM, Zhang D, Lu GM (2017) Generalized feature extraction for wrist pulse analysis: from 1-D time series to 2-D matrix. IEEE J Biomed Health 21:978–985

    Article  Google Scholar 

  18. Lu GM, Jiang ZX, Ye LY, Huang YT (2014) Pulse feature extraction based on improved Gaussian model. Inter Conf Med Biome 2014:90–94

    Google Scholar 

  19. Su CJ, Huang TY, Luo CH (2016) Arterial pulse analysis of multiple dimension pulse mapping by local cold stimulation for arterial stiffness. IEEE Sensors J 16:8288–8294

    Article  Google Scholar 

  20. Cui J, Tu LP, Zhang JF, Zhang SL, Zhang ZF, Xu JT (2019) Analysis of pulse signals based on array pulse volume. Chin J Integr Med 25:103–107

    Article  Google Scholar 

  21. Peng B, Luo CH, Sinha N, Tai CC, Xie XH, Xie HQ (2019) Fourier series analysis for novel spatiotemporal pulse waves: normal, taut, and slippery pulse images. Evid-Based Compl Alt Med 5734018:9

    Google Scholar 

  22. Luo CH, Ye JW, Lin CY, Lee TL, Tsai LM, Shieh MD (2018) L-cube polynomial for the recognition of normal and hypertensive string-like pulse mappings in Chinese medicine. Infor Med Unlocked 12:27–33

    Article  Google Scholar 

  23. Chung YF, Hu CS, Yeh CC, Luo CH (2013) How to standardize the pulse-taking method of traditional Chinese medicine pulse diagnosis. Comput Biol Med 43:342–349

    Article  Google Scholar 

  24. Fei ZF (2003) Modern pulse diagnosis of traditional Chinese medicine. In: People’s Medical Publishing House, Beijing, pp. 162–163

  25. Segers P, Rietzschel ER, De Buyzere ML, De Bacquer D, Van Bortel LM, De Backer G, Gillebert TC, Verdonck PR (2007) Assessment of pressure wave reflection: getting the timing right! Physiol Meas 28:1045–1056

    Article  Google Scholar 

  26. Zhang DY (2010) Research on classification of pulse signal and blood flow signal for pulse diagnosis. In: Harbin Institute of Technology, Harbin, pp. 72–75

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Funding

This work was supported by National Natural Science Foundation of China (62071497); Sun Yat-sen University, China, under Scientific Initiation Project for High-level Experts (67000-18841203).

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Correspondence to Ching-Hsing Luo or Xiaohua Xie.

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Ching-Hsing Luo and Zhan Zhang are co-first authors.

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Luo, CH., Zhang, Z., Peng, B. et al. The novel three-dimensional pulse images analyzed by dynamic L-cube polynomial model. Med Biol Eng Comput 59, 315–326 (2021). https://doi.org/10.1007/s11517-020-02289-4

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