[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abramson, E. L., et al. (2012). Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. International Journal of Medical Informatics, 81(8), 539–548.

    Article  Google Scholar 

  • Andersen, T., et al. (2011). Designing for collaborative interpretation in telemonitoring: Re-introducing patients as diagnostic agents. International Journal of Medical Informatics, 80(8), e112–e126.

    Article  Google Scholar 

  • Atal, B. S., & Hanauer, S. L. (1971). Speech analysis and synthesis by linear prediction of the speech wave. The Journal of the Acoustical Society of America, 50(2B), 637–655.

    Article  Google Scholar 

  • Benba, A., Jilbab, A., & Hammouch, A. (2014a). Voice analysis for detecting persons with Parkinson’s disease using MFCC and VQ. In The 2014 international conference on circuits, systems and signal processing, Saint Petersburg, 23–25 September 2014. Saint Petersburg: Saint Petersburg State Polytechnic University.

  • Benba, A., Jilbab, A., & Hammouch, A. (2014b). Voice analysis for detecting persons with Parkinson’s disease using PLP and VQ. Journal of Theoretical and Applied Information Technology, 70(3), 443–450.

    Google Scholar 

  • Benba, A., Jilbab, A., & Hammouch, A. (2014c). Voiceprint analysis using Perceptual Linear Prediction and Support Vector Machines for detecting persons with Parkinson’s disease. In The 3rd international conference on health science and biomedical systems, Florence, 22–24 November 2014.

  • Benba, A., Jilbab, A., & Hammouch, A. (2014d). Hybridization of best acoustic cues for detecting persons with Parkinson’s disease. In The 2nd World conference on complex system. Agadir: IEEE.

  • Benba, A., Jilbab, A., & Hammouch, A. (2015a). Detecting patients with Parkinson’s disease using Mel Frequency Cepstral Coefficients and Support Vector Machines. International Journal on Electrical Engineering and Informatics, 7(2), 297.

    Article  Google Scholar 

  • Benba, A., Jilbab, A., & Hammouch, A. (2015b). Detecting patients with Parkinson’s disease using PLP and VQ. In The 7th International conference on information technology, Amman, 12–15 May 2015.

  • Benba, A., et al. (2015c). Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson’s disease. In 2015 International conference on electrical and information technologies (ICEIT). Marrakech: IEEE.

  • Boersma, P., & Weenink, D. (2001). Praat, a system for doing phonetics by computer. Glot International, 5, 341–345.

    Google Scholar 

  • Chen, H.-L., et al. (2013). An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Systems with Applications, 40(1), 263–271.

    Article  Google Scholar 

  • De Lau, L. M. L., Lau, D., & Breteler, M. (2006). Epidemiology of Parkinson’s disease. The Lancet Neurology, 5(6), 525–535.

    Article  Google Scholar 

  • Den Eeden, V., Stephen, K., et al. (2003). Incidence of Parkinson’s disease: Variation by age, gender, and race/ethnicity. American Journal of Epidemiology, 157(11), 1015–1022.

    Article  Google Scholar 

  • Farrús, M., Hernando, J., & Ejarque, P. (2007). Jitter and shimmer measurements for speaker recognition. In INTERSPEECH.

  • Grimmett, G., & Stirzaker, D. (2001). Probability and random processes. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Hansen, J. H. L., Gavidia-Ceballos, L., & Kaiser, J. F. (1998). A nonlinear operator-based speech feature analysis method with application to vocal fold pathology assessment. IEEE Transactions on Biomedical Engineering, 45(3), 300–313.

    Article  Google Scholar 

  • Hermansky, H. (1990). Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America, 87(4), 1738–1752.

    Article  Google Scholar 

  • Hermansky, H., et al. (1992). RASTA-PLP speech analysis technique. In ICASSP.

  • Ho, A. K., et al. (1999). Speech impairment in a large sample of patients with Parkinson’s disease. Behavioural Neurology, 11(3), 131–137.

    Article  Google Scholar 

  • Huse, D. M., et al. (2005). Burden of illness in Parkinson’s disease. Movement Disorders, 20(11), 1449–1454.

    Article  Google Scholar 

  • Jafari, A. (2013). Classification of Parkinson’s disease patients using nonlinear phonetic features and Mel-Frequency Cepstral analysis. Biomedical Engineering: Applications, Basis and Communications, 25(4), 1350043.

    Google Scholar 

  • Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery and Psychiatry, 79(4), 368–376.

    Article  Google Scholar 

  • Kumar, C., & Mallikarjuna, P. R. (2011). Design of an automatic speaker recognition system using MFCC, Vector Quantization and LBG algorithm. International Journal on Computer Science and Engineering, 3(8), 2942–2954.

    Google Scholar 

  • Langston, J., et al. (2002). Parkinson’s disease: Current and future challenges. Neurotoxicology, 23(4), 443–450.

    Article  Google Scholar 

  • Lasierra, N., et al. (2012). Lessons learned after a three-year store and forward teledermatology experience using internet: Strengths and limitations. International Journal of Medical Informatics, 81(5), 332–343.

    Article  Google Scholar 

  • Lin, X., et al. (2012). Conditional expression of Parkinson’s disease-related mutant α-synuclein in the midbrain dopaminergic neurons causes progressive neurodegeneration and degradation of transcription factor nuclear receptor related. The Journal of Neuroscience, 32(27), 9248–9264.

    Article  Google Scholar 

  • Little, M. A., et al. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 56(4), 1015–1022.

    Article  Google Scholar 

  • Logemann, J. A., et al. (1978). Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients. Journal of Speech and Hearing Disorders, 43(1), 47–57.

    Article  MathSciNet  Google Scholar 

  • Mandal, I., & Sairam, N. (2013). Accurate telemonitoring of Parkinson’s disease diagnosis using robust inference system. International Journal of Medical Informatics, 82(5), 359–377.

    Article  Google Scholar 

  • Martinez, J., Perez, H., Escamilla, E., & Suzuki, M. M. (2012). Speaker recognition using Mel Frequency Cepstral Coefficients (MFCC) and Vactor Quantization (VQ) techniques. In IEEE electrical communications and computers (pp. 248–251), Cholula, February 2012.

  • Niu, L., et al. (2012). Effect of bilateral deep brain stimulation of the subthalamic nucleus on freezing of gait in Parkinson’s disease. Journal of International Medical Research, 40(3), 1108–1113.

    Article  Google Scholar 

  • O’Sullivan, S. B., & Schmitz, T. J. (2007). Parkinson disease. In Physical rehabilitation (pp. 856–894). Philadelphia, PA: F. A. Davis Company.

  • Rahn, I. I. I., Douglas, A., et al. (2007). Phonatory impairment in Parkinson’s disease: Evidence from nonlinear dynamic analysis and perturbation analysis. Journal of Voice, 21(1), 64–71.

    Article  Google Scholar 

  • Sakar, B. E., et al. (2013). Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics, 17(4), 828–834.

    Article  Google Scholar 

  • Sapir, S., et al. (2007). Effects of intensive voice treatment (the Lee Silverman Voice Treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: Acoustic and perceptual findings. Journal of Speech, Language, and Hearing Research, 50(4), 899–912.

    Article  MathSciNet  Google Scholar 

  • Scholz, M., Fraunholz, M., & Selbig, J. (2008). Nonlinear principal component analysis: Neural network models and applications. In Principal manifolds for data visualization and dimension reduction (pp. 44–67). Berlin: Springer.

  • Skowronski, M. D., & Harris, J. G. (2004). Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition. The Journal of the Acoustical Society of America, 116(3), 1774–1780.

    Article  Google Scholar 

  • Tang, L., et al. (2012). Meta-analysis of association between PITX3 gene polymorphism and Parkinson’s disease. Journal of the Neurological Sciences, 317(1), 80–86.

    Article  Google Scholar 

  • Viallet, F., & Teston, B. (2007). La dysarthrie dans la maladie de Parkinson. In Les dysarthries (pp. 169–174). Marseille: Solal.

  • Wielgat, R., et al. (2007). HFCC based recognition of bird species. In IEEE 15th signal processing algorithms, architectures, arrangements and applications.

  • Wilkins, E. J., et al. (2012). A DNA resequencing array for genes involved in Parkinson’s disease. Parkinsonism & Related Disorders, 18(4), 386–390.

    Article  Google Scholar 

  • http://www.cnel.ufl.edu/~markskow/software/HFCC.zip. Accessed 19 June 2015.

  • Yang, Z. R., et al. (2005). RONN: The bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics, 21(16), 3369–3376.

    Article  Google Scholar 

  • Young, S., Evermann, G., Hain, T., Kershaw, D., Liu, X., Moore, G., et al. (2001–2006). The HTK Book (for HTK Version 3.4). Cambridge: Cambridge University Engineering Department.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Achraf Benba.

Ethics declarations

Conflict of interest

Achraf Benba declares that he has no conflict of interest. And he doesn’t have any financial relationship with the organization. Abdelilah Jilbab declares that he has no conflict of interest. And he doesn’t have any financial relationship with the organization. Ahmed Hammouch declares that he has no conflict of interest. And he doesn’t have any financial relationship with the organization. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benba, A., Jilbab, A. & Hammouch, A. Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA. Int J Speech Technol 19, 743–754 (2016). https://doi.org/10.1007/s10772-016-9367-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-016-9367-z

Keywords

Navigation