Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Using high-dimensional features for high-accuracy pulse diagnosis

Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan

Accurate pulse diagnosis is often based on extensive clinical experience. Recently, modern computer-aided pulse diagnostic methods have been developed to help doctors to quickly determine patients’ physiological conditions. Most pulse diagnostic methods used low-dimensional feature vectors to classify pulse types. Therefore, some important but subtle pulse information might be ignored. In this study, a novel high-dimensional pulse classification method was developed to improve pulse diagnosis accuracy. To understand the underlying physical meaning or implications hidden in pulse discrimination, 71 pulse features were extracted from the time, spatial, and frequency domains to cover as much pulse information as possible. Then, Principal Component Analysis (PCA) was applied to extract the most representative components. Artificial neural networks were trained to classify 10 different pulse types. The results showed that PCA accounted for 95% of the total variances achieved the highest accuracy of 98.2% in pulse classification. The results also showed that pulse energy, local instantaneous characteristics, main frequency, and waveform complexity were the major factors determining pulse discriminability. This study demonstrated that using high-dimensional features could retain more pulse information and thus, effectively improve pulse diagnostic accuracy.
  Figure/Table
  Supplementary
  Article Metrics

References

1. A. C. Y. Tang, Review of traditional Chinese medicine pulse diagnosis quantification, Complementary Ther. Contemp. Healthcare, 2012 (2012), 61-80.

2. P. Wang, W. Zuo, D. Zhang, A compound pressure signal acquisition system for multichannel wrist pulse signal analysis, IEEE Trans. Instrum. Meas., 63 (2014), 1556-1565.

3. Q. L. Guo, K. Q. Wang, D. Y. Zhang, N. M. Li, A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis, 2008 International Conference on Wavelet Analysis and Pattern Recognition, 2008. Available from: https://ieeexplore.ieee.org/abstract/document/4635834.

4. D. Wang, D. Zhang, G. Lu, An optimal pulse system design by multichannel sensors fusion, IEEE J. Biomed. Health Infor., 20 (2016), 450-459.

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

6. D. Zhang, L. Zhang, D. Zhang, Y. Zheng, Wavelet based analysis of doppler ultrasonic wrist-pulse signals, International Conference on BioMedical Engineering and Informatics, 2008. Available from: https://ieeexplore.ieee.org/abstract/document/4549232/.

7. Y. Chen, L. Zhang, D. Zhang, D. Zhang, Computerized wrist pulse signal diagnosis using modified auto-regressive models, J. Med. Syst., 35 (2011), 321-328.

8. D. Y. Zhang, W. M. Zuo, D. Zhang, H. Z. Zhang, N. M. Li, Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features, J. Biomed. Sci. Eng., 3 (2010), 361-366.

9. L. Liu, N. Li, W. Zuo, D. Zhang, H. Zhang, Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis, Intelligent Science and Intelligent Data Engineering, 2012. Available from: https://link.springer.com/chapter/10.1007/978-3-642-36669-7_58.

10. C. Y. Tang, W. Y. Chung, K. S. Wong, Validation of a novel traditional Chinese medicine pulse diagnostic model using an artificial neural network, J. Evidence Based Complementary Altern. Med., 2012 (2012), 1-7.

11. Y. C. Du, A. Stephanus, Levenberg-marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor, Sensors, 18 (2018), 2322.

12. Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature, 521 (2015), 436-444.

13. G. Li, K. Watanabe, H. Anzai, X. Song, A. Qiao, M. Ohta, Pulse-wave-pattern classification with a convolutional neural network, Sci. Rep., 9 (2019), 14930.

14. J. Y. Lee, M. Jang, S. H. Shin, Study on the depth, rate, shape, and strength of pulse with cardiovascular simulator, J. Evidence Based Complementary Altern. Med., 2017 (2017), 1-11.

15. L. Xu, M. Q. Meng, K. Q. Wang, Pulse image recognition using fuzzy neural network, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0957417408001437.

16. H. Wang, Y. Cheng, A quantitative system for pulse diagnosis in traditional Chinese medicine, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005. Available from: https://ieeexplore.ieee.org/abstract/document/1615774/.

17. L. Xu, D. Zhang, K. Wang, L. Wang, Arrhythmic pulses detection using lempel-ziv complexity analysis, EURASIP J. Appl. Signal Proc., 2006 (2006), 1-12.

18. L. Xu, M. Q. Meng, K. Wang, W. Lu, N. Li, Pulse images recognition using fuzzy neural network, Expert Syst. Appl., 36 (2009), 3805-3811.

19. J. J. Shu, Y. Sun, Developing classification indices for Chinese pulse diagnosis, Complementary Ther. Med, 15 (2007), 190-198.

20. S. M. Pincus, Approximate entropy as a measure of system complexity, Proc. Natl. Acad. Sci., 88 (1991), 2297-2301.

21. M. Costa, A. L. Goldberger, C. K. Peng, Multiscale entropy analysis of complex physiologic time series, Phys. Rev. Let., 89 (2002), 068102.

22. K. Goyal, R. Agarwal, Pulse based sensor design for wrist pulse signal analysis and health diagnosis, Biomed. Res., 28 (2017), 5187-5195.

23. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London, Ser. A, 454 (1998), 903-995.

24. G. Wang, X. Chen, F. L. Qiao, Z. Wu, N. Huang, On intrinsic mode function, Adv. Adapt. Data Anal., 2 (2010), 277-293.

25. N. Arunkumar, M. K. M. Sirajudeen, Approximate entropy based ayurvedic pulse diagnosis for diabetics-a case study, The 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011), 2011. Available from: https://ieeexplore.ieee.org/abstract/document/6169099/.

26. J. Nie, M. Ji, Y. Chu, X. Meng, Y. Wang, J. Zhong, et al., Human pulses reveal health conditions by a piezoelectret sensor via the approximate entropy analysis, Nano Energy, 58 (2019), 528-535.

27. J. S. Richman, J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Phys. Heart Circ. Phys., 278 (2000), H2039-H2049.

28. I. Daubechies, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Infor. Theory, 36 (1990), 961-1005.

29. D. Cvetkovic, E. D. Übeyli, I. Cosic, Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study, Digital Signal Proc., 18 (2008), 861-874.

30. I. Daubechies, Ten Lectures on Wavelets, Society for industrial and applied mathematics, 1992.

31. L. Xu, K. Q. Wang, L. Wang, Pulse waveforms classification based on wavelet network, IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005. Available from: https://ieeexplore.ieee.org/abstract/document/1615493/.

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved