Skip to main content
SpringerLink
Log in

Diagnosing Parkinson’s Disease with the Use of a Reduced Set of Patients’ Voice Features Samples

  • Conference paper
  • First Online:
Computer Information Systems and Industrial Management (CISIM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11703))

Abstract

The paper proposes a method for diagnosing Parkinson’s disease using a reduced set of patients’ voice features samples. The Sequential Forward Selection and Sequential Backward Selection methods were used to reduce features. The data reduced were classified separately with the use of over a dozen popular classifiers. The effectiveness of diagnosing Parkinson’s disease on a reduced set of data was determined for each classifier. Then it was compared with the results obtained for the data without any reduction of features.

The experiments carried out showed that, depending on the classifier used, the reduction of the set even to few features allowed increasing the effectiveness of classification. The research also allowed indicating the classifiers and features, with the use of which the best results of classification were obtained. The experiments were carried out on a publicly available database containing voice samples of patients with Parkinson’s disease and of healthy patients.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Golbe, L.I., Mark, M.H., Sage, J.I.: Parkinson’s Disease Handbook. The American Parkinson Disease Association Inc., New York (2010)

    Google Scholar 

  2. Parkinson, J.: An essay on the shaking palsy. London (1817)

    Google Scholar 

  3. Grosset, D., Fernandez, H., Grosset, K., Okun, M.: Parkinson’s Disease Clinician’s Desk Reference. CRC Press, Boca Raton (2009)

    Book  Google Scholar 

  4. Lee, S.-H., Lim, J.S.: Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction. Expert Syst. Appl. 39(8), 7338–7344 (2012)

    Article  Google Scholar 

  5. Hariharan, M., Polat, K., Sindhu, R.: A new hybrid intelligent system for accurate detection of Parkinson’s disease. Comput. Methods Programs Biomed. 113(3), 904–913 (2014)

    Article  Google Scholar 

  6. Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. J. R. Soc. Interface 8(59), 842–55 (2011)

    Article  Google Scholar 

  7. Sakar, B.E., Kursun, O.: Telemonitoring of changes of unified Parkinson’s disease rating scale using severity of voice symptoms. In: Proceedings of the 2nd International Conference on E-Health and TeleMedicine, Istanbul, pp. 114–119 (2014)

    Google Scholar 

  8. Froelich, W., Wrobel, K., Porwik, P.: Diagnosing Parkinson’s disease using the classification of speech signals. J. Med. Inform. Technol. 23, 187–193 (2014)

    Google Scholar 

  9. Froelich, W., Wrobel, K., Porwik, P.: Diagnosis of Parkinson’s disease using speech samples and threshold-based classification. J. Med. Imaging Health Inf. 5(6), 1358–1363 (2015)

    Article  Google Scholar 

  10. Rouzbahani, H.K., Daliri, M.R.: Diagnosis of Parkinson’s disease in human using voice signals. Basic Clin. Neurosci. 2(3), 12–20 (2011)

    Google Scholar 

  11. Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Trans. Biomed. Eng. 57(4), 884–93 (2010)

    Article  Google Scholar 

  12. Goetz, C.G., et al.: Testing objective measures of motor impairment in early Parkinson’s disease: feasibility study of an at-home testing device. Mov. Disord. 24(4), 551–556 (2009)

    Article  Google Scholar 

  13. Morales, D.A., et al.: Predicting dementia development in Parkinson’s disease using Bayesian network classifiers. Psychiatry Res. NeuroImaging 213(2), 92–8 (2013)

    Article  Google Scholar 

  14. Peker, M., Sen, B., Delen, D.: Computer-aided diagnosis of Parkinson’s disease using complex-valued neural networks and mRMR feature selection algorithm. J. Healthc. Eng. 6(3), 281–302 (2015)

    Article  Google Scholar 

  15. Ene, M.: Neural network-based approach to discriminate healthy people from those with Parkinson’s disease. Ann. Univ. Craiova 35, 112–116 (2008)

    MathSciNet  MATH  Google Scholar 

  16. Peker, M.: A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. J. Med. Syst. 40(5), 1–6 (2016)

    Article  Google Scholar 

  17. Chen, H.-L., et al.: An efficient diagnosis system for detection of Parkinson’s disease using fuzzy \(k\)-nearest neighbor approach. Expert Syst. Appl. 40(1), 263–271 (2013)

    Article  Google Scholar 

  18. Guruler, H.: A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput. Appl. 28(7), 1657–1666 (2017)

    Article  Google Scholar 

  19. Wrobel, K., Doroz, R., Palys, M.: A method of lip print recognition based on sections comparison. In: IEEE International Conference on Biometrics and Kansei Engineering (ICBAKE), pp. 47–52 (2013)

    Google Scholar 

  20. Palys, M., Doroz, R., Porwik, P.: On-line signature recognition based on an analysis of dynamic feature. In: IEEE International Conference on Biometrics and Kansei Engineering (ICBAKE), pp. 103–107 (2013)

    Google Scholar 

  21. Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 377–388. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07617-1_34

    Chapter  Google Scholar 

  22. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  23. Ferri, F.J., Pudil, P., Hatef, M., Kittler, J.: Comparative study of techniques for large-scale feature selection. Mach. Intell. Pattern Recogn. 16, 403–413 (1994)

    Article  Google Scholar 

  24. Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  25. Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed. Eng. Online 6(1), 23 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, ul. Bedzinska 39, 41-200, Sosnowiec, Poland

    Krzysztof Wrobel

Authors
  1. Krzysztof Wrobel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Wrobel .

Editor information

Editors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. University of Calcutta, Calcutta, India

    Rituparna Chaki

  3. Mihajlo Pupin Institute, Belgrade, Serbia

    Valentina Janev

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wrobel, K. (2019). Diagnosing Parkinson’s Disease with the Use of a Reduced Set of Patients’ Voice Features Samples. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-28957-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28956-0

  • Online ISBN: 978-3-030-28957-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation