Advertisement

Phonation Biomechanics in Quantifying Parkinson’s Disease Symptom Severity

  • P. Gómez-VildaEmail author
  • A. Álvarez-Marquina
  • A. Tsanas
  • C. A. Lázaro-Carrascosa
  • V. Rodellar-Biarge
  • V. Nieto-Lluis
  • R. Martínez-Olalla
Chapter
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 48)

Abstract

It is known that Parkinson’s Disease (PD) leaves marks in phonation dystonia and tremor. These marks can be expressed as a function of biomechanical characteristics monitoring vocal fold tension and imbalance. These features may assist tracing the neuromotor activity of laryngeal pathways. Therefore these features may be used in grading the stage of a PD patient efficiently, frequently and remotely by telephone or VoIP channels. The present work is devoted to describe and compare the PD symptom severity quantification from neuromotor-sensitive features with respect to other features on a telephone-recorded database. The results of these comparisons are presented and discussed.

Keywords

Neurologic disease Parkinson’s Disease (PD) Speech neuromotor activity Aging voice Dysarthria 

Notes

Acknowledgments

This work is being funded by grants TEC2012-38630-C04-01 and TEC2012-38630-C04-04 from Plan Nacional de I\(+\)D\(+\)i, Ministry of Economic Affairs and Competitiveness of Spain. from Plan Nacional de I\(+\)D\(+\)i, Ministry of Economic Affairs and Competitiveness of Spain. Special thanks are also due to the Patient Voice Analysis Challenge initiative for allowing the use of their data in the present study.

References

  1. 1.
    Dorsey, E.R., et al.: Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68, 384–386 (2007)Google Scholar
  2. 2.
    Falk, T., Chan, W., Shein, F.: Characterization of atypical vocal source excitation, temporal dynamics and prosody for objective measurement of dysarthric word intelligibility. Speech Commun. 54(5), 622–631 (2012)CrossRefGoogle Scholar
  3. 3.
    Yunusova, Y., Weismer, G., Westbury, J., Lindstrom, M.: Articulatory movements during vowels in speakers with dysarthria. J. Speech Lang. Hear. Res. 51, 596–611 (2008)CrossRefGoogle Scholar
  4. 4.
    Tsanas, A.: Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine leaning. Ph.D. thesis, University of Oxford, U.K., June (2012)Google Scholar
  5. 5.
    Tsanas, A., Little, M.A., McSharry, P.E., Spielman, J., Ramig, L.O.: Novel speech signal processing algorithms for high-accuracy classification of Parkinson‘s disease. IEEE Trans. Biomed. Eng. 59, 1264–1271 (2010)CrossRefGoogle Scholar
  6. 6.
    Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., Ramig, L.O.: Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 56(4), 1015–1022 (2009)CrossRefGoogle Scholar
  7. 7.
    Little, M., Wicks, P., Vaughan, T., Pentland, A.: Quantifying short-term dynamics of Parkinson’s disease using self-reported symptom data from an Internet social network. J. Med. Internet Res. 15(1), e20 (2013)CrossRefGoogle Scholar
  8. 8.
    Hoehn, M.M., Yahr, M.D.: Parkinsonism: onset, progression, and mortality. Neurology 17(5), 427–442 (1967)CrossRefGoogle Scholar
  9. 9.
    Chenausky, K., MacAuslan, J., Goldhor, R.: Acoustic analysis of PD speech. Parkinson’s Disease (2011). doi: 10.4061/2011/435232 Google Scholar
  10. 10.
    Gómez, P., et al.: Estimating tremor in vocal fold biomechanics for neurological disease characterization. In: Proceedings of the 18th International Conference on Digital Signal Processing (DSP2013), Santorini, Greece, June 2013, M1C-2Google Scholar
  11. 11.
    Gómez, P., et al.: Characterizing neurological disease from voice quality biomechanical analysis. Cogn. Comput. 5, 399–425 (2013)CrossRefGoogle Scholar
  12. 12.
    Schapira, A.H.V., et al.: Novel pharmacological targets for the treatment of Parkinson’s disease. Nat. Rev. Drug Discov. 5, 845–854 (2006)CrossRefGoogle Scholar
  13. 13.
    Deller, J.R., Proakis, J.G., Hansen, J.H.L.: Discrete-Time Processing of Speech Signals. Macmillan, NewYork (1993)Google Scholar
  14. 14.
    Gómez, P., et al.: Glottal source biometrical signature for voice pathology detection. Speech Commun. 51, 759–781 (2009)CrossRefGoogle Scholar
  15. 15.
    Synapse: Contribute to the cure. https://www.synapse.org. Accessed 24 Feb 2015
  16. 16.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), Article 27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 24 Feb 2015
  17. 17.
    Tsanas, A., Little, M.A., McSharry, P.E., Scanlon, B.K., Papapetropoulos, S.: Statistical analysis and mapping of the unified Parkinson’s disease rating scale to Hoehn and Yahr staging. Parkinsonism Relat. Disord. 18(5), 697–699 (2012)CrossRefGoogle Scholar
  18. 18.
    Abitbol, J., Abitbol, P., Abitbol, B.: Sex hormones and the female voice. J. Voice 13(3), 424–446 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • P. Gómez-Vilda
    • 1
    Email author
  • A. Álvarez-Marquina
    • 1
  • A. Tsanas
    • 2
  • C. A. Lázaro-Carrascosa
    • 1
  • V. Rodellar-Biarge
    • 1
  • V. Nieto-Lluis
    • 1
  • R. Martínez-Olalla
    • 1
  1. 1.Neuromorphic Speech Processing Lab, Center for Biomedical TechnologyUniversidad Politécnica de Madrid, Campus de MontegancedoMadridSpain
  2. 2.Oxford Centre for Industrial and Applied MathematicsUniversity of OxfordOxfordUK

Personalised recommendations