CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES

Oumaima Majdoubi

oumaima_majdoubi@um5.ac.ma
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
https://orcid.org/0009-0000-2968-7975

Achraf Benba


Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
http://orcid.org/0000-0001-7939-0790

Ahmed Hammouch


Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
http://orcid.org/0009-0005-8691-6662

Abstract

This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications.


Keywords:

voice analysis, Parkinson’s disease, MFCC, PCA, naive Bayes kernel, machine learning

Abramson E. L. et al.: Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. International journal of medical informatics 81(8), 2012, 539–548.
DOI: https://doi.org/10.1016/j.ijmedinf.2012.02.010   Google Scholar

Andersen T. et al.: Designing for collaborative interpretation in telemonitoring: Re-introducing patients as diagnostic agents. International journal of medical informatics 80(8), 2011, e112–e126.
DOI: https://doi.org/10.1016/j.ijmedinf.2010.09.010   Google Scholar

Baker K. K. et al.: Thyroarytenoid muscle activity associated with hypophonia in Parkinson disease and aging. Neurology 51(6), 1998, 1592–1598.
DOI: https://doi.org/10.1212/WNL.51.6.1592   Google Scholar

Benba A. et al.: Discriminating between patients with Parkinson’s and neurological diseases using cepstral analysis. IEEE transactions on neural systems and rehabilitation engineering 24(10), 2016, 1100–1108.
DOI: https://doi.org/10.1109/TNSRE.2016.2533582   Google Scholar

Benba A. et al.: Hybridization of best acoustic cues for detecting persons with Parkinson's disease. Second World Conference on Complex Systems (WCCS), IEEE, 2014, 622–625.
DOI: https://doi.org/10.1109/ICoCS.2014.7060885   Google Scholar

Benba A. et al.: Using RASTA-PLP for discriminating between different neurological diseases. International Conference on Electrical and Information Technologies (ICEIT), IEEE, 2016, 406–409.
DOI: https://doi.org/10.1109/EITech.2016.7519630   Google Scholar

Boersma P. et al.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. Proceedings of the institute of phonetic sciences, 1993, 97–110.
  Google Scholar

De Colle W.: Voce & Computer – analisi acustica digitale del segnale verbale. Omega, 2001.
  Google Scholar

Gales M. et al.: The application of hidden Markov models in speech recognition. Foundations and Trends in Signal Processing 1(3), 2008, 195–304.
DOI: https://doi.org/10.1561/2000000004   Google Scholar

Gillies G. E., et al.: Sex differences in Parkinson’s disease. Frontiers in neuroendocrinology 35(3), 2014, 370–384.
DOI: https://doi.org/10.1016/j.yfrne.2014.02.002   Google Scholar

Gorris C. et al.: Acoustic analysis of normal voice patterns in Italian adults by using Praat. Journal of Voice 34(6), 2020, 961.e9–961.e18.
DOI: https://doi.org/10.1016/j.jvoice.2019.04.016   Google Scholar

Goyal J. et al.: A hybrid approach for Parkinson’s disease diagnosis with resonance and time-frequency based features from speech signals. Expert Systems with Applications 182, 2021, 115283.
DOI: https://doi.org/10.1016/j.eswa.2021.115283   Google Scholar

Gupta S. et al.: Feature extraction using MFCC. Signal & Image Processing. An International Journal 4(4), 2013, 101–108.
DOI: https://doi.org/10.5121/sipij.2013.4408   Google Scholar

Holi M. S. et al.: Automatic detection of neurological disordered voices using mel cepstral coefficients and neural networks. IEEE Point-of-Care Healthcare Technologies (PHT), IEEE, 2013, 76–79.
  Google Scholar

Islam M. R., Nahiduzzaman M.: Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach. Expert Systems with Applications 195, 2022, 116554.
DOI: https://doi.org/10.1016/j.eswa.2022.116554   Google Scholar

Lieberman P.: Perturbations in vocal pitch. The Journal of the Acoustical Society of America 33, 1961, 597–602.
DOI: https://doi.org/10.1121/1.1908736   Google Scholar

Lieberman P.: Some acoustic measures of the fundamental periodicity of normal and pathologic larynges. The Journal of the Acoustical Society of America 35(3), 1963, 344–353.
DOI: https://doi.org/10.1121/1.1918465   Google Scholar

Little M. et al. Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. Nature Precedings, 2008, 1–1.
DOI: https://doi.org/10.1038/npre.2008.2298.1   Google Scholar

Little M. et al.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Nature Precedings, 2007, 1–1.
DOI: https://doi.org/10.1038/npre.2007.326.1   Google Scholar

Mandal I., Sairam N.: Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system. International journal of medical informatics 82(5), 2013, 359–377.
DOI: https://doi.org/10.1016/j.ijmedinf.2012.10.006   Google Scholar

O'Sullivan S. B., Schmitz T. J.: Parkinson disease. Physical Rehabilitation 5th ed., F. A. Davis Company, Philadelphia 2007, 856–894.
  Google Scholar

Pahuja G., Nagabhushan T. N.: A comparative study of existing machine learning approaches for Parkinson's disease detection. IETE Journal of Research 67(1), 2021, 4–14.
DOI: https://doi.org/10.1080/03772063.2018.1531730   Google Scholar

Parkinson J.: An Essay on Shaking Palsy. Whittingham and Rowland Printing, London 1817.
  Google Scholar

Rahman A. et al.: Parkinson’s disease diagnosis in cepstral domain using MFCC and dimensionality reduction with SVM classifier. Mobile Information Systems 2021, 1–10.
DOI: https://doi.org/10.1155/2021/8822069   Google Scholar

Rahn III. et al.: Phonatory impairment in Parkinson's disease: evidence from nonlinear dynamic analysis and perturbation analysis. Journal of Voice 21(1), 2007, 64–71.
DOI: https://doi.org/10.1016/j.jvoice.2005.08.011   Google Scholar

Sharanyaa S. et al.: An Exploration on Feature Extraction and Classification Techniques for Dysphonic Speech Disorder in Parkinson’s Disease. Inventive Communication and Computational Technologies: Proceedings of ICICCT 2021, Springer Singapore, 2022, 33–48.
DOI: https://doi.org/10.1007/978-981-16-5529-6_4   Google Scholar

Stelzig Y. et al.: Laryngeal manifestations in patients with Parkinson disease. Laryngo-rhino-otologie 78(10), 1999, 544–551.
DOI: https://doi.org/10.1055/s-1999-8758   Google Scholar

Teixeira J. P. et al.: Vocal acoustic analysis–jitter, shimmer and hnr parameters. Procedia Technology 9, 2013, 1112–1122.
DOI: https://doi.org/10.1016/j.protcy.2013.12.124   Google Scholar

Teston B.: L'évaluation objective des dysfonctionnements de la voix et de la parole; 2e partie: les dysphonies. Travaux Interdisciplinaires du Laboratoire Parole et Langage d'Aix-en-Provence (TIPA) 20, 2001, 169–232.
  Google Scholar

Tiwari V.: MFCC and its applications in speaker recognition. International journal on emerging technologies 1(1), 2010, 19–22.
  Google Scholar

Van Den E. et al.: Incidence of Parkinson’s disease: variation by age, gender, and race/ethnicity. American Journal of Epidemiology 157(11), 2003, 1015–1022.
DOI: https://doi.org/10.1093/aje/kwg068   Google Scholar

Wendahl R. W.: Laryngeal analog synthesis of jitter and shimmer auditory parameters of harshness. Folia Phoniatrica et Logopaedica 18(2), 1966, 98–108.
DOI: https://doi.org/10.1159/000263059   Google Scholar

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Published
2023-09-30

Cited by

Majdoubi, O., Benba, A., & Hammouch, A. (2023). CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(3), 16–22. https://doi.org/10.35784/iapgos.3685

Authors

Oumaima Majdoubi 
oumaima_majdoubi@um5.ac.ma
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
https://orcid.org/0009-0000-2968-7975

Authors

Achraf Benba 

Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
http://orcid.org/0000-0001-7939-0790

Authors

Ahmed Hammouch 

Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
http://orcid.org/0009-0005-8691-6662

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