Skip to main content
SpringerLink
Log in

Addressing voice recording replications for tracking Parkinson’s disease progression

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Tracking Parkinson’s disease symptom severity by using characteristics automatically extracted from voice recordings is a very interesting and challenging problem. In this context, voice features are automatically extracted from multiple voice recordings from the same subjects. In principle, for each subject, the features should be identical at a concrete time, but the imperfections in technology and the own biological variability result in nonidentical replicated features. The involved within-subject variability must be addressed since replicated measurements from voice recordings can not be directly used in independence-based pattern recognition methods as they have been routinely used through the scientific literature. Besides, the time plays a key role in the experimental design. In this paper, for the first time, a Bayesian linear regression approach suitable to handle replicated measurements and time is proposed. Moreover, a version favoring the best predictors and penalizing the worst ones is also presented. Computational difficulties have been avoided by developing Gibbs sampling-based approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. http://www.parkinsonsvoice.org/.

  2. https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring.

References

  1. Baghai-Ravary L, Beet SW (2013) Automatic speech signal analysis for clinical diagnosis and assessment of speech disorders. springer briefs in electrical and computer engineering: speech technology. Springer, New York

    Book  Google Scholar 

  2. Balakrishnan S, Madigan D (2010) Priors on the variance in sparse Bayesian learning: the demi-Bayesian Lasso. In: Chen M-H, Muller P, Sun D, Ye K (eds) Frontiers of statistical decision making and Bayesian analysis: in honor of James O. Berger. Springer, Berlin, pp 346–359

    Google Scholar 

  3. Buonaccorsi JP (2010) Measurement error: models. Methods and applications. Chapman & Hall/CRC, Boca Raton

    Book  Google Scholar 

  4. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement error in nonlinear models: a modern perspective, second edn. Chapman & Hall/CRC, Boca Raton

    Book  Google Scholar 

  5. Castelli M, Vanneschi L, Silva S (2014) Prediction of the unified Parkinson’s disease rating scale assessment using a genetic programming system with geometric semantic genetic operators. Expert Syst Appl 41(10):4608–4616

    Article  Google Scholar 

  6. Chen M-H, Shao Q-M, Ibrahim JG (2000) Monte Carlo methods in Bayesian computation. Springer, Berlin Series in Statistics

    Book  Google Scholar 

  7. Eskidere Ö, Ertaç F, Hanilçi C (2012) A comparison of regression methods for remote tracking of Parkinson’s disease progression. Expert Syst Appl 39(5):5523–5528

    Article  Google Scholar 

  8. Gamerman D, Lopes HF (2006) Markov Chain Monte Carlo: stochastic simulation for Bayesian inference, 2nd edn. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  9. Goetz CG, Stebbins GT, Wolff D, DeLeeuw W, Bronte-Stewart H, Elble R, Hallett M, Nutt J, Ramig L, Sanger T, Wu AD, Kraus PH, Blasucci LM, Shamim EA, Sethi KD, Spielman J, Kubota K, Grove AS, Dishman E, Taylor CB (2009) 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

    Article  PubMed  PubMed Central  Google Scholar 

  10. Harel B, Cannizzaro M, Snyder PJ (2004) Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: a longitudinal case study. Brain Cogn 56(1):24–29

    Article  PubMed  Google Scholar 

  11. Kadane J, Lazar N (2004) Methods and criteria for model selection. J Am Stat Assoc 99(465):279–290

    Article  Google Scholar 

  12. Leng C, Tran M-N, Nott D (2014) Bayesian adaptive Lasso. Ann Inst Stat Math 66(2):221–244

    Article  Google Scholar 

  13. Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 56(4):1015–1022

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lykou A, Ntzoufras I (2013) On Bayesian LASSO variable selection and the specification of the shrinkage parameter. Stat Comput 23(3):361–390

    Article  Google Scholar 

  15. Mohammadi P, Hatamlou A, Masdari M (2013) A comparative study on remote tracking of Parkinson’s disease progression using data mining methods. Int J Found Comput Sci Technol 3(6):71–83

    Article  Google Scholar 

  16. O’Hara RB, Sillanpää MJ (2009) A review of Bayesian variable selection methods: what, how and which. Bayesian Anal 4(1):85–118

    Article  Google Scholar 

  17. Okada M (1983) Measurement of speech patterns in neurological disease. Med Biol Eng Comput 21:145–148

    Article  CAS  PubMed  Google Scholar 

  18. Pérez CJ, Naranjo L, Martín J, Campos-Roca Y (2014) A latent variable-based Bayesian regression to address recording replication in Parkinson’s disease. In: EURASIP (ed) Proceedings of the 22nd European signal processing conference (EUSIPCO-2014). IEEE, Lisbon, Portugal, pp 1447–1451

  19. Post B, Merkus MP, de Bie RMA, de Haan RJ, Speelman JD (2005) Unified Parkinson’s disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable? Mov Disord 20(12):1577–1584

    Article  PubMed  Google Scholar 

  20. Ramaker C, Marinus J, Stiggelbout AM, van Hilten BJ (2002) Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov Disord 17(5):867–876

    Article  PubMed  Google Scholar 

  21. Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 17(4):828–834

    Article  PubMed  Google Scholar 

  22. Schrag A, Ben-Shlomo Y, Quinn N (2002) How valid is the clinical diagnosis of Parkinson’s disease in the community? J Neurol Neurosurg Psychiatry 73(5):529–534

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Smith BJ (2007) BOA: an R package for MCMC output convergence assessment and posterior inference. J Stat Softw 21(11):1–37

    Article  Google Scholar 

  24. Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J R Stat Soc Ser B 58(1):267–288

    Google Scholar 

  25. Tsanas A, Little MA, McSharry PE, Ramig LO (2010) Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. IEEE Trans Biomed Eng 57(4):884–893

    Article  PubMed  Google Scholar 

  26. Tsanas A, Little MA, McSharry PE , Ramig LO ( 2010) Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson’s disease progression. In: IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, Dallas, pp 594–597

  27. Tsanas A, Little MA, McSharry PE, Ramig LO (2011) Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. R Soc Interface 8(59):842–855

    Article  PubMed  Google Scholar 

  28. Tsanas A, Little MA, McSharry PE, Ramig LO (2011) Robust parsimonious selection of dysphonia measures for telemonitoring of Parkinson’s disease symptom severity. In: Manfredi C (ed) Models and analysis of vocal emissions for biomedical applications: 7th international workshop. Firenze University Press, Firenze, pp 169–172

    Google Scholar 

  29. Tsanas A, Little MA, McSharry PE, Ramig LO (2013) Using the cellular mobile telephone network to remotely monitor Parkinson’s disease symptom severity. IEEE Trans Biomed Eng (submitted)

  30. Zou H (2006) The adaptive LASSO and its oracle properties. J Am Stat Assoc 101(476):1418–1429

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Thanks to Athanasios Tsanas for providing information on the dataset. We thank four reviewers for comments and suggestions which have highly improved this paper. This research has been partially supported by Ministerio de Economía y Competitividad, Spain (Projects MTM2011-28983-C03-02 and MTM2014-56949-C3-3-R), Gobierno de Extremadura, Spain (Projects GR15052 and GR15106), and European Union (European Regional Development Funds).

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Extremadura, Avda. de la Universidad s/n, 10003, Cáceres, Spain

    Lizbeth Naranjo, Carlos J. Pérez & Jacinto Martín

Authors
  1. Lizbeth Naranjo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos J. Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacinto Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lizbeth Naranjo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naranjo, L., Pérez, C.J. & Martín, J. Addressing voice recording replications for tracking Parkinson’s disease progression. Med Biol Eng Comput 55, 365–373 (2017). https://doi.org/10.1007/s11517-016-1512-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-016-1512-y

Keywords

Search

Navigation