141
Views
0
CrossRef citations to date
0
Altmetric
Medical Electronics

Estimation of Severity in Parkinson’s Disease Using Acoustic Features of Phonatory Tasks

ORCID Icon &

References

  • M. Politis, K. Wu, S. Molloy, P. G. Bain, K. R. Chaudhuri, and P. Piccini, “Parkinson's disease symptoms: The patient's perspective,” Mov. Disord., Vol. 25, no. 11, pp. 1646–51, 2010.
  • C. Ramaker, J. Marinus, A. M. Stiggelbout, and B. J. Van Hilten, “Systematic evaluation of rating scales for impairment and disability in Parkinson's disease,” Mov. Disord.: Off. J. Mov. Disord. Soc., Vol. 17, no. 5, pp. 867–76, 2002.
  • 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. 2129–70, 2008.
  • B. Kostek, K. Kaszuba, P. Zwan, P. Robowski, and J. Slawek, “Automatic assessment of the motor state of the Parkinson's disease patient – a case study,” Diagn. Pathol., Vol. 7, no. 1, pp. 18, 2012.
  • N. Piro, L. Piro, J. Kassubek, and R. Blechschmidt-Trapp, “Analysis and visualization of 3D motion data for UPDRS rating of patients with Parkinson’s disease,” Sensors, Vol. 16, no. 6, pp. 930, 2016.
  • A. J. Espay, et al., “Technology in Parkinson's disease: Challenges and opportunities,” Mov. Disord., Vol. 31, no. 9, pp. 1272–82, 2016.
  • M. Giuberti, et al., “Automatic UPDRS evaluation in the sit-to-stand task of Parkinsonians: Kinematic analysis and comparative outlook on the leg agility task,” IEEE. J. Biomed. Health. Inform., Vol. 19, no. 3, pp. 803–14, 2015.
  • F. L. Darley, A. E. Aronson, and J. R. Brown. Motor Speech Disorders. First Edition. Philadelphia: Saunders, 1975.
  • J. R. Duffy. Motor Speech Disorders: Substrates, Differential Diagnosis, and Management. 3rd ed. St.Louis: Elsevier Health Sciences, 2013.
  • L. Brabenec, J. Mekyska, Z. Galaz, and I. Rektorova, “Speech disorders in Parkinson’s disease: Early diagnostics and effects of medication and brain stimulation,” J. Neural. Transm., Vol. 124, no. 3, pp. 303–34, 2017.
  • 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 Cogn., Vol. 56, no. 1, pp. 24–9, 2004.
  • L. A. Ramig, R. C. Scherer, I. R. Titze, and S. P. Ringel, “Acoustic analysis of voices of patients with neurologic disease: Rationale and preliminary data,” Ann. Otol. Rhinol. Laryngol., Vol. 97, no. 2 Pt 1, pp. 164–72, Mar–Apr 1988.
  • C. M. Fox, and L. O. Ramig, “Vocal sound pressure level and self-perception of speech and voice in men and women with idiopathic Parkinson disease,” Am J Speech Lang Pathol., Vol. 6, no. 2, pp. 85–94, 1997.
  • J. Gamboa, et al., “Acoustic voice analysis in patients with Parkinson's disease treated with dopaminergic drugs,” J. Voice, Vol. 11, no. 3, pp. 314–20, 1997.
  • R. J. Holmes, J. M. Oates, D. J. Phyland, and A. J. Hughes, “Voice characteristics in the progression of Parkinson's disease,” Int. J. Lang. Commun. Disord., Vol. 35, no. 3, pp. 407–18, 2000.
  • S. Skodda, W. Grönheit, N. Mancinelli, and U. Schlegel, “Progression of voice and speech impairment in the course of Parkinson's disease: A longitudinal study,” Parkinson’s Dis., Vol. 2013, pp. 1–8, 2013.
  • S. Skodda, H. Rinsche, and U. Schlegel, “Progression of dysprosody in Parkinson's disease over time – a longitudinal study,” Mov. Disord., Vol. 24, no. 5, pp. 716–22, 2009.
  • S. S. Narayanan, and A. A. Alwan, “A nonlinear dynamical systems analysis of fricative consonants,” J. Acoust. Soc. Am., Vol. 97, no. 4, pp. 2511–24, 1995.
  • A. Tsanas, M. A. Little, P. E. McSharry, J. Spielman, and L. O. Ramig, “Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease,” IEEE Trans. Biomed. Eng., Vol. 59, no. 5, pp. 1264–71, 2012.
  • I. Midi, M. Dogan, M. Koseoglu, G. Can, M. Sehitoglu, and D. Gunal, “Voice abnormalities and their relation with motor dysfunction in Parkinson’s disease,” Acta Neurol. Scand., Vol. 117, no. 1, pp. 26–34, 2008.
  • F. Majdinasab, S. Karkheiran, M. Soltani, N. Moradi, and G. Shahidi, “Relationship between voice and motor disabilities of Parkinson's disease,” J. Voice, Vol. 30, no. 6, pp. 768. e17–768. e22, 2016.
  • 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. R. Soc. Interface, Vol. 8, no. 59, pp. 842–55, 2011.
  • A. Bayestehtashk, M. Asgari, I. Shafran, and J. McNames, “Fully automated assessment of the severity of Parkinson's disease from speech,” Comput. Speech. Lang., Vol. 29, no. 1, pp. 172–85, Jan 2015.
  • A. Tsanas, “Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning,” DPhil, University of Oxford, 2012.
  • T. Peterek, P. Dohnálek, P. Gajdoš, and M. Šmondrk, “Performance evaluation of random forest regression model in tracking Parkinson's disease progress,” in 13th International Conference on Hybrid Intelligent Systems (HIS 2013), Gammarth, Tunisia; 2013, pp. 83–7.
  • Ö Eskidere, F. Ertaş, and C. Hanilçi, “A comparison of regression methods for remote tracking of Parkinson’s disease progression,” Expert. Syst. Appl., Vol. 39, no. 5, pp. 5523–8, 2012.
  • R. Viswanathan, S. P. Arjunan, P. Kempster, S. Raghav, and D. Kumar, “Estimation of Parkinson’s disease severity from voice features of vowels and consonant,” in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, Canada, 2020, pp. 3666–9.
  • R. Viswanathan, et al., “Efficiency of voice features based on consonant for detection of Parkinson's disease,” in 2018 IEEE Life Sciences Conference (LSC), Montreal, Canada; 2018, pp. 49–52. IEEE.
  • J. Mekyska, et al., “Assessing progress of Parkinson's disease using acoustic analysis of phonation,” in Presented at the 4th International Work Conference on Bioinspired Intelligence (IWOBI), Donostia-San Sebastian, Spain; 2015.
  • A. J. Hughes, S. E. Daniel, L. Kilford, and A. J. Lees, “Accuracy of clinical diagnosis of idiopathic Parkinson's disease: A clinico-pathological study of 100 cases,” J. Neurol. Neurosurg. Psychiatry, Vol. 55, no. 3, pp. 181–4, 1992.
  • Dinesh Kumar, Kempster Peter, Raghav Sanjay, Viswanthan Rekha, Zham Poonam, and Arjunan Sridhar. Screening Parkinson's Diseases Using Sustained Phonemes. RMIT University. Dataset, 2020. doi:10.25439/rmt.12618755.v1
  • D. W. Paul Boersma, Praat: Doing Phonetics by Computer, January 22. Available: http://www.praat.org./.
  • VoiceSauce. Available: http://www.phonetics.ucla.edu/voicesauce/documentation/contents.html.
  • M. Airas, “TKK aparat: An environment for voice inverse filtering and parameterization,” Logop. Phoniatr. Vocology, Vol. 33, no. 1, pp. 49–64, 2008.
  • D. Michaelis, T. Gramss, and H. W. Strube, “Glottal-to-noise excitation ratio – a new measure for describing pathological voices,” Acta Acust. United. Acust., Vol. 83, no. 4, pp. 700–6, 1997.
  • M. A. Little, “Biomechanically informed nonlinear speech signal processing,” DPhil, University of Oxford, 2007.
  • R. Tibshirani, “Regression shrinkage and selection via the lasso,” J. R. Stat. Soc.: B (Methodol.), Vol. 58, no. 1, pp. 267–88, 1996.
  • V. Fonti, and E. Belitser, “Feature selection using lasso,” VU Amsterdam Research Paper in Business Analytics, 2017.
  • V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY: Springer Science & Business Media, 1995.
  • D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process. Lett. Rev., Vol. 11, no. 10, pp. 203–24, 2007.
  • C.-H. Ho, and C.-J. Lin, “Large-scale linear support vector regression,” J. Mach. Learn. Res., Vol. 13, no. Nov, pp. 3323–48, 2012.
  • L. Breiman, “Random forests,” Mach. Learn., Vol. 45, no. 1, pp. 5–32, 2001.
  • A. Liaw, and M. Wiener, “Classification and regression by randomForest,” R News, Vol. 2, no. 3, pp. 18–22, 2002.
  • Y. Freund, and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci, Vol. 55, no. 1, pp. 119–39, 1997.
  • A. K. Ho, J. L. Bradshaw, and R. Iansek, “For better or worse: The effect of levodopa on speech in Parkinson's disease,” Mov. Disord, Vol. 23, no. 4, pp. 574–80, 2008.
  • C. Mawdsley, and C. Gamsu, “Periodicity of speech in parkinsonism,” Nature, Vol. 231, no. 5301, pp. 315, 1971.
  • H. Im, S. Adams, A. Abeyesekera, M. Pieterman, G. Gilmore, and M. Jog, “Effect of Levodopa on speech dysfluency in Parkinson's disease,” Mov. Disord. Clin. Pract., Vol. 6, no. 2, pp. 150–4, 2019.
  • T. Tykalová, et al., “Effect of dopaminergic medication on speech dysfluency in Parkinson’s disease: A longitudinal study,” J. Neural. Transm., Vol. 122, no. 8, pp. 1135–42, 2015.
  • J. Rusz, T. Tykalová, J. Klempíř, R. Čmejla, and E. Růžička, “Effects of dopaminergic replacement therapy on motor speech disorders in Parkinson’s disease: Longitudinal follow-up study on previously untreated patients,” J. Neural. Transm., Vol. 123, no. 4, pp. 379–87, 2016.
  • İ Cantürk, and F. Karabiber, “A machine learning system for the diagnosis of Parkinson’s disease from speech signals and its application to multiple speech signal types,” Arab. J. Sci. Eng., Vol. 41, no. 12, pp. 5049–59, 2016.
  • M. Asgari, and I. Shafran, “Extracting cues from speech for predicting severity of Parkinson'S disease,” in 2010 IEEE International Workshop on Machine Learning for Signal Processing, Kittilä, Finland; 2010, pp. 462–7.
  • M. Nespor, M. Peña, and J. Mehler, “On the different roles of vowels and consonants in speech processing and language acquisition,” Lingue Linguaggio, Vol. 2, no. 2, pp. 203–30, 2003.
  • K. M. Kurowski, S. E. Blumstein, C. L. Palumbo, R. S. Waldstein, and M. W. Burton, “Nasal consonant production in Broca's and Wernicke's aphasics: Speech deficits and neuroanatomical correlates,” Brain Lang., Vol. 100, no. 3, pp. 262–75, Mar 2007.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.