Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Energy-based feature ranking for assessing the dysphonia measurements in Parkinson detection

Energy-based feature ranking for assessing the dysphonia measurements in Parkinson detection

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

  • Author(s): W. Ji 1  and  Y. Li 2
    • View affiliations
    • Affiliations: 1: College of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, People's Republic of China
      2: College of Computer and Institute of Computer Technology, Nanjing University of Posts and Telecommunications, Nanjing, People's Republic of China
  • Source: Volume 6, Issue 4, June 2012, p. 300 – 305
    DOI:  10.1049/iet-spr.2011.0186 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Published

The Parkinson's disease (PD) detection based on dysphonia has been drawn significant attention. However, all dysphonia measurements differ in the uncontrolled acoustic environments. In order to gain as much reliability as possible, measurements should be assessed and the robust ones are chosen. In this study, motivated by statistical learning theory, the problem of PD detection is addressed to classify the participant as healthy or PD using support vector machine (SVM) with the dysphonia measurements as the input feature vector. Therefore an energy-based feature-ranking algorithm is adopted to assess the dysphonia measurements. Moreover, in order to improve the stability of the proposed algorithm, an ensemble version is also presented where multiple feature-ranking results are aggregated. The experimental results on PD data sets have shown the proposed algorithm outperforms other classic ones, and the ensemble version obtain the higher stability than single one.

Inspec keywords: patient diagnosis; reliability; support vector machines; medical computing; diseases

Other keywords: reliability; support vector machine; energy-based feature ranking; statistical learning theory; uncontrolled acoustic environments; feature vector; Parkinson disease detection; stability; multiple feature-ranking; dysphonia measurements

Subjects: Biomedical measurement and imaging; Patient diagnostic methods and instrumentation; Biology and medical computing; Patient care and treatment; Electrodiagnostics and other electrical measurement techniques; Knowledge engineering techniques

References

    1. 1)
      • Kononerko, I.: `Estimating attributes: analysis and extension of RELIEF', Proc. Int. Conf. on Machine Learning, April 1994, Catania, Italy, p. 171–182.
    2. 2)
      • I. Guyon , A. Elisseeff . An introduction to variable and feature selection. J. Mach. Learn. Res. , 157 - 1182
    3. 3)
      • J.A. Logemann , H.B. Fisher , B. Boshes , E.R. Blonsky . Frequency and co-occurrence of vocal-tract dysfunctions in speech of a large sample of Parkinson patients. J. Speech Hear. Disord. , 47 - 57
    4. 4)
      • Kira, K., Rendell, L.: `A practical approach to feature selection', Proc. Int. Conf. on Machine Learning, 1992, Aberdeen, Scotland, UK, p. 249–256.
    5. 5)
      • K.Q. Weinberger , J. Blitzer , L.K. Saul . (2006) Distance metric learning for large margin nearest neighbor classification.
    6. 6)
      • K. Crammer , R.G. Bachrach , A. Navot , N. Tishby . (2002) Margin analysis of the lvq algorithm advances in neural information processing systems.
    7. 7)
      • Frank A., Asuncion A.: ‘UCI machine learning repository’. Available at http://archive.ics.uci.edu/ml, 2010.
    8. 8)
    9. 9)
      • Loscalzo, S., Yu, L., Ding, C.: `Consensus group based stable feature selection', Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD-09), 2009, Paris, France, p. 567–576.
    10. 10)
    11. 11)
    12. 12)
      • J.R. Duffy . (2005) Motor speech disorders: substrates, differential diagnosis, and management.
    13. 13)
      • Y. LeCun , S. Chopra , R. Hadsell , F.J. Huang , M.A. Ranzato , Bakir . (2006) A tutorial on energy-based learning, predicting structured outputs.
    14. 14)
      • I. Guyon , S. Gunn , M. Nikravesh , L. Zadeh . (2006) Feature extraction, foundations and applications.
    15. 15)
    16. 16)
    17. 17)
      • Saeys, Y., Abel, T., Peer, Y.V.: `Robust feature selection using ensemble feature selection techniques', Proc. 25th European Conf. on Machine Learning and Knowledge Discovery in Databases, July 2008, Helsinki, Finland, p. 313–325.
    18. 18)
    19. 19)
      • A.K. Ho , R. Iansek , C. Marigliani , J.L. Bradshaw , S. Gates . Speech impairment in a large sample of patients with Parkinson's disease. Cogn. Behav. Neurol. , 131 - 137
    20. 20)
    21. 21)
    22. 22)
      • Han, Y., Yu, L.: `A variance reduction framework for stable feature selection', Proc. Int. Conf. on Data Mining, 2010, Sydney, Australia, p. 206–215.
    23. 23)
      • Chang C.C., Lin C.J.: ‘LIBSVM: a library for support vector machines’. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz, 2002.
    24. 24)
    25. 25)
      • K.Q. Weinberger , L.K. Saul . Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. , 207 - 214
    26. 26)
      • Bachrach, R.G., Navot, A., Tishby, N.: `Margin based feature selection-theory and algorithm', Proc. Int. Conf. on Machine Learning, 2004, Banff, Canada.
    27. 27)
    28. 28)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2011.0186
Loading

Related content

content/journals/10.1049/iet-spr.2011.0186
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address