Novel speech signal processing algorithms for high-accuracy classification of Parkinsons disease

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 5, 2012, Pages 1264-1271

  Tsanas, Athanasios ^a   Little, Max A ^a,b   McSharry, Patrick E ^a   Spielman, Jennifer ^c,d   Ramig, Lorraine O ^c,d  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF COLORADO   (United States)

^d University of Arizona   (United States)

Author keywords

Decision support tool; feature selection (FS); nonlinear speech signal processing; Parkinsons disease (PD); random forests (RF); support vector machines (SVM)

Indexed keywords

DECISION SUPPORT TOOLS; PARKINSONS DISEASE; RANDOM FORESTS; SPEECH SIGNAL PROCESSING; SUPPORT VECTOR MACHINE (SVM);

CLASSIFICATION (OF INFORMATION); DECISION SUPPORT SYSTEMS; DECISION TREES; DIAGNOSIS; FEATURE EXTRACTION; FORESTRY; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

ALGORITHMS;

ADULT; AGED; ALGORITHM; ARTICLE; CLINICAL FEATURE; CONTROLLED STUDY; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; DYSPHONIA; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; NON INVASIVE PROCEDURE; PARKINSON DISEASE; PHONATION; RANDOM FOREST; SIGNAL NOISE RATIO; SIGNAL PROCESSING; SPEECH; SPEECH AUDIOMETRY; SPEECH DISCRIMINATION; SPEECH PERCEPTION; SUPPORT VECTOR MACHINE; VOWEL;

AGED; AGED, 80 AND OVER; CASE-CONTROL STUDIES; DECISION TREES; DYSPHONIA; FEMALE; HUMANS; MALE; MIDDLE AGED; NONLINEAR DYNAMICS; PARKINSON DISEASE; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUPPORT VECTOR MACHINES;

ALGORITHMS; CLASSIFICATION; DECISION THEORY; DIAGNOSIS; DISEASES; NONLINEAR PROGRAMING; RANDOM PROCESSES;

EID: 84860386097     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2012.2183367     Document Type: Article

References (40)

1. M. Rajput, A. Rajput, and A. H. Rajput, "Epidemiology," in Handbook of Parkinson's disease, R. Pahwa and K. E. Lyons, Eds., 4th ed. New York: Informa Healthcare, 2007, ch. 2.
2. A. S. von Campenhausen, B. Bornschein, R.Wick, K. Bötzel, C. Sampaio, W. Poewe, W. Oertel, U. Siebert, K. Berger, and R. Dodel, "Prevalence and incidence of Parkinson's disease in Europe," Eur. Neuropsychopharmacol., vol. 15, pp. 473-490, 2005. (Pubitemid 40949553)
3. A. Schrag, Y. Ben-Schlomo, and N. Quinn, "How valid is the clinical diagnosis of Parkinson's disease in the community?," J. Neurol., Neurosurg. Psychiat., vol. 73, pp. 529-535, 2002.
4. S. K. Van Den Eeden, C.M. Tanner, A. L. Bernstein, R. D. Fross, A. Leimpeter, D. A. Bloch, and L. Nelson, "Incidence of Parkinson's disease: Variation by age, gender, and race/ethnicity," Am. J. Epidem., vol. 157, pp. 1015-1022, 2003. (Pubitemid 36628805)
5. M. A. Little, P. E. McSharry, E. J. Hunter, J. Spielman, and L. O. Ramig, "Suitability of dysphonia measurements for telemonitoring of Parkinson's disease," IEEE Trans. Biomed. Eng., vol. 56, no. 4, pp. 1015-1022, Apr. 2009.
6. P.-F. Guo, P. Bhattacharya, and N. Kharma, "Advances in detecting Parkinson's disease," Med. Biometrics (Lect. Not. Comput. Sci.), vol. 6165, pp. 306-314, 2010.
7. S. Sapir, L.Ramig, J. Spielman, and C. Fox, "Formant CentralizationRatio (FCR): A proposal for a new acoustic measure of dysarthric speech," J. Speech Language Hearing Res., vol. 53, pp. 114-125, 2010.
8. A. Ho, R. Iansek, C. Marigliani, J. Bradshaw, and S. Gates, "Speech impairment in a large sample of patients with Parkinson's disease," Behav. Neurol., vol. 11, pp. 131-137, 1998. (Pubitemid 29537551)
9. B. Harel, M. Cannizzaro, and P.J. Snyder, "Variability in fundamental frequency during speech in prodromal and incipient Parkinson's disease: A longitudinal case study," Brain Cognition, vol. 56, pp. 24-29, 2004. (Pubitemid 39223318)
10. A. Tsanas, M.A. Little, P. E. McSharry, and L. O. Ramig, "Accurate telemonitoring of Parkinson's disease progression using non-invasive speech tests," IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 884-893, Apr. 2012.
11. A. Tsanas, M.A. Little, P. E. McSharry, and L. O. Ramig, "Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity," J. Roy. Soc., vol. 8, pp. 842-855, 2011.
12. A. Tsanas, M.A. Little, P. E. McSharry, and L. O. Ramig, "Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression," in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Dallas, TX, 2010, pp. 594-597.
13. A. Tsanas,M. A. Little, P.E. McSharry, and L. O. Ramig, "New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson's disease symptom severity," in Proc. Int. Symp. Nonlinear Theory Appl., Krakow, Poland, Sep. 5-8, 2010, pp. 457-460.
14. R. J. Baken and R. F. Orlikoff, ClinicalMeasurement of Speech and Voice, 2nd ed. San Diego, CA: Singular Thomson Learning, 2000.
15. I. R. Titze, Principles of Voice Production, 2nd ed. Iowa City: Natl. Center Voice Speech, 2000.
16. J. Schoentgen and R. De Gucteneere, "Time series analysis of jitter," J. Phonetics, vol. 23, pp. 189-201, 1995.
17. P. A. Naylor, A. Kounoudes, J. Gudnason, and M. Brookes, "Estimation of glottal closure instants in voiced speech using the DYPSA algorithm," IEEE Trans. Speech Audio Process., vol. 15, no. 1, pp. 34-43, Jan. 2007.
18. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for non-linear and non stationary time series analysis," Proc. Roy. Soc. London A, vol. 454, pp. 903-995, 1998. (Pubitemid 128733183)
19. J. I. Godino-Llorente, P. Gomez-Vilda, and M. Blanco-Velasco, "Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters," IEEE Trans. Biomed. Eng., vol. 53, no. 10, pp. 1943-1953, Oct. 2006. (Pubitemid 44526241)
20. R. Fraile, N. Saenz-Lechon, J.I. Godino-Llorente, V. Osma-Ruiz, and C. Fredouille, "Automatic detection of laryngeal pathologies in records of sustained vowels by means of mel-frequency cepstral coefficient parameters and differentiation of patients by sex," Folia Phoniatrica et Logopaedica, vol. 61, pp. 146-152, 2009.
21. A. Ihler and M. Mandel, KDE Toolbox, (2003). [Online]. Available: http://www.ics.uci.edu/~ihler/code/kde.html.
22. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York: Springer, 2009.
23. I. Guyon, S. Gunn, M. Nikravesh, L.A. Zadeh, and Eds, Feature Extraction: Foundations and Applications. New York: Springer, 2006.
24. R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Roy. Statist. Soc. B, vol. 58, pp. 267-288, 1996.
25. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and minredundancy," IEEE Trans. Pattern Anal. Mach., vol. 27, no. 8, pp. 1226- 1238, Aug. 2005. (Pubitemid 41245053)
26. K. Kira and L.A. Rendell, "A practical approach to feature selection," in Proc. 9th Int. Conf. Mach. Learn., 1992, pp. 249-256.
27. Y. Sun, S. Todorovic, and S. Goodison, "Local learning based feature selection for high dimensional data analysis," IEEE Pattern Anal. Mach., vol. 32, no. 9, pp. 1610-1626, Sep. 2010.
28. D. Donoho, "For most large underdetermined systems of equations, the minimal L1-norm near-solution approximates the sparsest near-solution," Commun. Pure Appl. Math., vol. 59, no. 7, pp. 904-934, 2006.
29. N. Meinshausen and B.Yu, "Lasso-type recovery of sparse representations for high-dimensional data," Ann. Statist., vol. 37, no. 1, pp. 246-270, 2009.
30. R. Gilad-Bachrach, A. Navot, and N. Tishby, "Margin based feature selection-Theory and algorithms," in Proc. 21st Int. Conf.Mach. Learn., 2004, pp. 43-50.
31. A. Tsanas, M.A. Little, P. E. McSharry, and L. O. Ramig, "Robust parsimonious selection of dysphonia measures for telemonitoring of Parkinson's disease symptom severity," in Proc. 7th Int. Workshop Models Anal. Vocal Emissions Biomed. Appl. (MAVEBA), Florence, Italy, Aug. 25-27, 2011, p. 169-172.
32. L. Breiman, "Random forests," Mach. Learn., vol. 45, p. 5-32, 2001.
33. C. C. Chang and C-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 1-27, 2011.
34. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification," National Taiwan University, Taipei, Taiwan, Tech. Rep., 2010.
35. R. Das, "Classification of Parkinson's disease by using voice measurements," Expert Syst. Appl., vol. 37, pp. 1568-1572, 2010.
36. C. O. Sakar and O. Kursun, "Telediagnosis of Parkinson's disease using measurements of dysphonia," J. Med. Syst., vol. 34, pp. 591-599, 2010.
37. I. Psorakis, T.Damoulas, and M.A. Girolami, "Multiclass relevance vector machines: Sparsity and accuracy," IEEE Trans. Neural Netw., vol. 21, no. 10, pp. 1588-1598, Oct. 2010.
38. B. Shahbaba and R. Neal, "Nonlinear models using Dirichlet process mixtures," J. Mach. Learn. Res., vol. 10, pp. 1829-1850, 2009.
39. D. Meyer, F. Leisch, and K. Hornik, "The support vector machine under test," Neurocomputing, vol. 55, pp. 169-186, 2003. (Pubitemid 37336675)
40. A. Statnikov, L.Wang, andC. F.Aliferis, "Acomprehensive comparison of random forests and support vector machines for microarray-based cancer classification," BMC Bioinformatics, vol. 9, p. 319, 2008.