Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease

Keywords: Speech articulation, Classification, Hilbert-Huang, Parkinson’s Disease

Abstract

Most patients with Parkinson’s Disease (PD) develop speech deficits, including reduced sonority, altered articulation, and abnormal prosody. This article presents a methodology to automatically classify patients with PD and Healthy Control (HC) subjects. In this study, the Hilbert-Huang Transform (HHT) and Mel-Frequency Cepstral Coefficients (MFCCs) were considered to model modulated phonations (changing the tone from low to high and vice versa) of the vowels /a/, /i/, and /u/. The HHT was used to extract the first two formants from audio signals with the aim of modeling the stability of the tongue while the speakers were producing modulated vowels. Kruskal-Wallis statistical tests were used to eliminate redundant and non-relevant features in order to improve classification accuracy. PD patients and HC subjects were automatically classified using a Radial Basis Support Vector Machine (RBF-SVM). The results show that the proposed approach allows an automatic discrimination between PD and HC subjects with accuracies of up to 75 % for women and 73 % for men.

Author Biographies

Felipe O. López-Pabón*, Universidad de Antioquia, Colombia

Ingeniero Electrónico, Facultad de Ingeniería, Universidad de Antioquia, Medellín-Colombia, forlando.lopez@udea.edu.co

Tomas Arias-Vergara, Universidad de Antioquia, Colombia

MSc. en Ingeniería, Facultad de Ingeniería, Universidad de Antioquia, Laboratorio de reconocimiento de patrones (LME), Medellín-Colombia,  Universidad de Erlangen-Núremberg, Erlangen-Germany, Universidad de Múnich, Múnich-Germany, tomas.arias@udea.edu.co

Juan R. Orozco-Arroyave, Universidad de Antioquia, Colombia

PhD. en Ciencias de la Computación, Grupo de investigación en Telecomunicaciones aplicadas (GITA), Facultad de Ingeniería, Universidad de Antioquia, Laboratorio de reconocimiento de patrones (LME), Medellín-Colombia, Universidad de Erlangen-Núremberg, Erlangen-Germany, rafael.orozco@udea.edu.co

References

S. Anand and C. E. Stepp, “Listener Perception of Monopitch, Naturalness, and Intelligibility for Speakers With Parkinson’s Disease,” J. Speech, Lang. Hear. Res., vol. 58, no. 4, pp. 1134–1144, Aug. 2015.

http://pubs.asha.org/doi/10.1044/2015_JSLHR-S-14-0243

S. Fahn, “Description of Parkinson’s Disease as a Clinical Syndrome,” Ann. N. Y. Acad. Sci., vol. 991, no. 1, pp. 1–14, no. 991, pp. 1-14, Jun. 2003.

https://doi.org/10.1111/j.1749-6632.2003.tb07458.x

J. A. Logemann, H. B. Fisher, B. Boshes, and E. R. Blonsky, “Frequency and Cooccurrence of Vocal Tract Dysfunctions in the Speech of a Large Sample of Parkinson Patients,” J. Speech Hear. Disord., vol. 43, no. 1, pp. 47–57, Feb. 1978. https://doi.org/10.1044/jshd.4301.47

R. D. Kent, G. Weismer, J. F. Kent, and J. C. Rosenbek, “Toward Phonetic Intelligibility Testing in Dysarthria,” J. Speech Hear. Disord., vol. 54, no. 4, pp. 482–499, Nov. 1989. https://doi.org/10.1044/jshd.5404.482

D. Hemmerling, J. R. Orozco-Arroyave, A. Skalski, J. Gajda, and E. Nöth, “Automatic Detection of Parkinson’s Disease Based on Modulated Vowels,” in proc Interspeech, San francisco, 2016, pp. 1190–1194.

https://doi.org/10.21437/Interspeech.2016-1062

J. Rusz et al., “Imprecise vowel articulation as a potential early marker of Parkinson’s disease: Effect of speaking task,” J. Acoust. Soc. Am., vol. 134, no. 3, pp. 2171–2181, Aug. 2013. https://doi.org/10.1121/1.4816541

S. Skodda, W. Visser, and U. Schlegel, “Vowel Articulation in Parkinson’s Disease,” J. Voice, vol. 25, no. 4, pp. 467–472, Jul. 2011. https://doi.org/10.1016/j.jvoice.2010.01.009

J. R. Orozco-Arroyave et al., “NeuroSpeech: An open-source software for Parkinson’s speech analysis,” Digit. Signal Process., vol. 77, pp. 207–221, Jun. 2018. https://doi.org/10.1016/j.dsp.2017.07.004

R. R. Zhang, S. Ma, and S. Hartzell, “Signatures of the Seismic Source in EMD-Based Characterization of the 1994 Northridge, California, Earthquake Recordings,” Bull. Seismol. Soc. Am., vol. 93, no. 1, pp. 501–518, Feb. 2003. https://doi.org/10.1785/0120010285

N. E. Huang and Z. Wu, “A review on Hilbert-Huang transform: Method and its applications to geophysical studies,” Rev. Geophys., vol. 46, no. 2, pp. 1-23, Jun. 2008. https://doi.org/10.1029/2007RG000228

J. R. Orozco-Arroyave, J. D. Arias-Londoño, J. F. V. Bonilla, M. C. Gonzalez-Rátiva, and E. Nöth, “New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease.,” in Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, 2014, pp. 342–347. Available: https://www.aclweb.org/anthology/L14-1549/

C. G. Goetz et al., “Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results,” Mov. Disord., vol. 23, no. 15, pp. 2129–2170, Nov. 2008. https://doi.org/10.1002/mds.22340

J. C. Vásquez-Correa, J. R. Orozco-Arroyave, T. Bocklet, and E. Nöth, “Towards an automatic evaluation of the dysarthria level of patients with Parkinson’s disease,” J. Commun. Disord., vol. 76, pp. 21–36, Nov. 2018.

https://doi.org/10.1016/j.jcomdis.2018.08.002

N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci., vol. 454, no. 1971, pp. 903–995, Mar. 1998. https://doi.org/10.1098/rspa.1998.0193

J. C. Catford, "A practical introduction to phonetics", ed. Second, Oxford: Clarendon Press, 1998. Available: https://global.oup.com/academic/product/a-practical-introduction-to-phonetics-9780199246359?cc=co&lang=en&

J. R. Orozco-Arroyave, F. Hönig, J. D. Arias-Londoño, J. F. Vargas-Bonilla, and E. Nöth, “Spectral and cepstral analyses for Parkinson’s disease detection in Spanish vowels and words,” Expert Syst., vol. 32, no. 6, pp. 688–697, Dec. 2015. https://doi.org/10.1111/exsy.12106

L. R. Rabiner and R. W. Schafer, “Introduction to Digital Speech Processing,” Found. Trends® Signal Process., vol. 1, no. 1–2, pp. 1–194, Dec. 2007. Available: http://research.iaun.ac.ir/pd/mahmoodian/pdfs/UploadFile_2643.pdf

E. Mendoza, N. Valencia, J. Muñoz, and H. Trujillo, “Differences in voice quality between men and women: Use of the long-term average spectrum (LTAS),” J. Voice, vol. 10, no. 1, pp. 59–66, Jan. 1996.

https://doi.org/10.1016/S0892-1997(96)80019-1

I. Hertrich and H. Ackermann, “Gender-Specific Vocal Dysfunctions in Parkinson’s Disease: Electroglottographic and Acoustic Analyses,” Ann. Otol. Rhinol. Laryngol., vol. 104, no. 3, pp. 197–202, Mar. 1995. https://doi.org/10.1177/000348949510400304

N. Sáenz-Lechón, J. I. Godino-Llorente, V. Osma-Ruiz, and P. Gómez-Vilda, “Methodological issues in the development of automatic systems for voice pathology detection,” Biomed. Signal Process. Control, vol. 1, no. 2, pp. 120–128, Apr. 2006. https://doi.org/10.1016/j.bspc.2006.06.003

How to Cite
López-Pabón, F. O., Arias-Vergara, T., & Orozco-Arroyave, J. R. (2020). Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease. TecnoLógicas, 23(47), 93-108. https://doi.org/10.22430/22565337.1401

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Published
2020-01-30
Section
Research Papers