Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction: Computational Pulse Diagnosis Read chapter Read first chapter

2018 | OriginalPaper | Chapter

12. Modified Gaussian Models and Fuzzy C-Means

Authors : David Zhang, Wangmeng Zuo, Peng Wang

Published in: Computational Pulse Signal Analysis

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this chapter, a systematic approach is proposed to analyze the computation wrist pulse signals, with the focus placed on the feature extraction and pattern classification. The wrist pulse signals are first collected and preprocessed. Considering that a typical pulse signal is composed of periodically systolic and diastolic waves, a modified Gaussian model is adopted to fit the pulse signal and the modeling parameters are then taken as features. Consequently, a feature selection scheme is proposed to eliminate the tightly correlated features and select the disease-sensitive ones. Finally, the selected features are fed to a Fuzzy C-Means (FCM) classifier for pattern classification. The proposed approach is tested on a dataset which includes pulse signals from 100 healthy persons and 88 patients. The results demonstrate the effectiveness of the proposed approach in computation wrist pulse diagnosis.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Edit Distance for Pulse Diagnosis
next chapter Modified Auto-regressive Models
Literature
1.
Lukman S, He Y, Hui S. “Computational methods for traditional Chinese medicine: a survey,” Computer Methods and Programs in Biomedicine 2007;88:283–94.CrossRef
2.
Hammer L. Chinese pulse diagnosis—contemporary approach. Eastland Press; 2001.
3.
Zhu L, Yan J, Tang Q, Li Q. “Recent progress in computerization of TCM,” Journal of Communication and Computer 2006;3(7).
4.
Wang K, Xu L, Zhang D, Shi C. “TCPD based pulse monitoring and analyzing,” In: Proceedings of the 1st ICMLC conference. 2002.
5.
Wang H, Cheng Y. “A quantitative system for pulse diagnosis in Traditional Chinese Medicine,” In: Proceedings of the 27th IEEE EMB conference. 2005.
6.
Lau E, Chwang A. “Relationship between wrist-pulse characteristics and body conditions,” In: Proceedings of the EM2000 conference. 2000.
7.
Lu W, Wang Y, Wang W. “Pulse analysis of patients with severe liver problems,” IEEE Engineering in Medicine and Biology Magazine 1999;18(January/February (1)):73–5.
8.
Wang B, Luo J, Xiang J, Yang Y. “Power spectral analysis of human pulse and study of traditional Chinese medicine pulse-diagnosis mechanism,” Journal of Northwestern University (Natural Science Edition) 2001;31(1):22–5.
9.
Wang Y, Wu X, Liu B, Yi Y. “Definition and application of indices in Doppler ultrasound sonogram,” Journal of Biomedical Engineering of Shanghai 1997;18:26–9.
10.
Ruano M, Fish P. “Cost/benefit criterion for selection of pulsed Doppler ultrasound spectral mean frequency and bandwidth estimators,” IEEE Transactions on BME 1993;40:1338–41.CrossRef
11.
Leonard P, Beattie TF, Addison PS, Watson JN. “Wavelet analysis of pulse oximeter waveform permits identification of unwell children,” Journal of Emergency Medicine 2004;21:59–60.CrossRef
12.
Zhang Y, Wang Y, Wang W, Yu J. “Wavelet feature extraction and classification of Doppler ultrasound blood flow signals,” Journal of Biomedical Engineering 2002;19(2):244–6.
13.
Zhang D, Zhang L, Zhang D, Zheng Y. “Wavelet based analysis of Doppler ultrasonic wrist-pulse signals,” In: Proceedings of the ICBBE 2008 conference, vol. 2. 2008. p. 539–43.
14.
Chen B, Wang X, Yang S, McGreavy C. “Application of wavelets and neural networks to diagnostic system development, 1, feature extraction,” Computers and Chemical Engineering 1999;23:899–906.CrossRef
15.
Heral A, Hou Z. “Application of wavelet approach for ASCE structural health monitoring benchmark studies,” Journal of Engineering Mechanics 2004;1:96–104.CrossRef
16.
Burges C. “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery 1998;2:121–67.CrossRef
17.
Chiu C, Yeh S, Yu Y. “Classification of the pulse signals based on self-organizing neural network for the analysis of the autonomic nervous system,” Chinese Journal of Medical and Biological Engineering 1996;16: 461–76.
18.
Chen Y, Zhang L, Zhang D, Zhang D. “Pattern classification for Doppler ultrasonic wrist pulse signals,” In: 5th ICBBE conference. 2009.CrossRef
19.
Yoon Y, Lee M, Soh K. “Pulse type classification by varying contact pressure,” IEEE Engineering in Medicine and Biology Magazine 2000;19:106–10.
20.
Xu L, Zhang D, Wang K. “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Transactions on Biomedical Engineering 2005;52(11):1973–5.CrossRef
21.
Xia C, Li Y, Yan J, Wang Y, Yan H, Guo R, et al. “A practical approach to wrist pulse segmentation and single-period average waveform estimation,” In: The ICBEI conference. 2008. p. 334–8.CrossRef
22.
Walsh S, King E. Pulse diagnosis: a clinical guide. Elsevier; 2007.
23.
Shu J, Sun Y. “Developing classification indices for Chinese pulse diagnosis,” Complementary Therapies in Medicine 2007;15:190–8.CrossRef
24.
More JJ. “Recent developments in algorithms and software for trust region methods,” In: Mathematical programming. NY: Springer-Verlag; 1983. p. 258–87.
25.
Mor JJ. The Levenberg–Marquardt algorithm: implementation and theory. Berlin/Heidelberg: Springer; 2006.
26.
Jorge N, Stephen W. Numerical optimization. New York: Springer; 1999.MATH
27.
Bezdek JC. Pattern recognition with fuzzy objective function algorithms, New York: Plenum Press; 1981.CrossRef
28.
Wang X, Wang Y, Wang L. “Improving fuzzy c-means clustering based on feature weight learning,” Pattern Recognition Letters 2004;25:1123–32.CrossRef
Metadata
Title
Modified Gaussian Models and Fuzzy C-Means
Authors
David Zhang
Wangmeng Zuo
Peng Wang
Copyright Year
2018
Publisher
Springer Singapore
Book
Computational Pulse Signal Analysis

Print ISBN: 978-981-10-4043-6

Electronic ISBN: 978-981-10-4044-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-4044-3_12

Premium Partner

    Image Credits