Skip to main content
SpringerLink
Log in

Voice signal processing for detecting possible early signs of Parkinson’s disease in patients with rapid eye movement sleep behavior disorder

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

Abstract

In this study we introduced a method for early detecting of Parkinson’s disease (PD) in patients with rapid eye movement sleep behavior disorder (RBD). Patients suffering from RBD are at extremely high risk (> 80%) for developing PD as well as other related neurodegenerative disorders. The database used in this study contains 30 PD patients in the very early stages, 50 RBD patient and 50 healthy subjects (HS). First, we created a model with a maximal accuracy of 85% of discrimination between PD and HS by testing different combinations of acoustic features along with different kernels of SVM and leave one subject out validation scheme. Based on that model, we tested 50 RBD patients in order to see whether they will belong to PD or HS groups. As a result we found 66% of RBD patients were classified as PD. Based on these foundlings we confirmed the existence of a correlation between RBD patients and early PD patients using speech analysis and thus, early PD signs can be reliably captured. These results will lead to the development of an embedded system for detecting the possible early signs of PD and other neurodegenerative diseases.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Asuncion, A., & Newman, D. (2007). UCI machine learning repository.

  • Benba, A., Jilbab, A., & Hammouch, A. (2016a). Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA. International Journal of Speech Technology, 19(4):743–754.

    Article  Google Scholar 

  • Benba, A., Jilbab, A., & Hammouch, A. (2016b). Discriminating between patients with Parkinson’s and neurological diseases using cepstral analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(10), 1100–1108.

    Article  Google Scholar 

  • Benba, A., Jilbab, A., & Hammouch, A. (2016c). Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people. International Journal of Speech Technology, 19(3):449–456.

    Article  Google Scholar 

  • Benba, A., Jilbab, A., & Hammouch, A. (2017) Using human factor cepstral coefficient on multiple types of voice recordings for detecting patients with Parkinson’s disease. IRBM, 38(6):346–351.

    Article  Google Scholar 

  • Darley, F. L., Aronson, A. E., & Brown, J. R. (1969a). Clusters of deviant speech dimensions in the dysarthrias. Journal of Speech, Language, and Hearing Research, 12(3), 462–496.

    Article  Google Scholar 

  • Darley, F. L., Aronson, A. E., & Brown, J. R. (1969b). Differential diagnostic patterns of dysarthria. Journal of Speech, Language, and Hearing Research, 12(2), 246–269.

    Article  Google Scholar 

  • Darley, F. L., Aronson, A. E., & Brown, J. R. (1975). Audio seminars in speech pathology: Motor speech disorders. Philadelphia: WB Saunders.

    Google Scholar 

  • Duffy, J. (2012). Motor speech disorders substrates, differential diagnosis, and management. St. Louis, MO: Elsevier.

    Google Scholar 

  • Harel, B., Cannizzaro, M., & Snyder, P. J. (2004). Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: A longitudinal case study. Brain and Cognition, 56(1), 24–29.

    Article  Google Scholar 

  • Hlavnička, J., Čmejla, R., Tykalová, T., Šonka, K., Růžička, E., & Rusz, J. (2017). Automated analysis of connected speech reveals early biomarkers of Parkinson’s disease in patients with rapid eye movement sleep behaviour disorder. Scientific Reports, 7(1), 12.

    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), 1350001.

    Google Scholar 

  • Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of neurology, neurosurgery & psychiatry, 79(4):368–376.

    Article  Google Scholar 

  • Jilbab, A., Benba, A., & Hammouch, A. (2017). Quantification system of Parkinson’s disease. International Journal of Speech Technology, 20(1):143–150.

    Article  Google Scholar 

  • Little, M. A., McSharry, P. E., Hunter, E. J., Spielman, J., & Ramig, L. O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 56(4), 1015–1022.

    Article  Google Scholar 

  • Manciocco, A., Chiarotti, F., Vitale, A., Calamandrei, G., Laviola, G., & Alleva, E. (2009). The application of Russell and Burch 3R principle in rodent models of neurodegenerative disease: The case of Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 33(1), 18–32.

    Article  Google Scholar 

  • Postuma, R. B., Lang, A. E., Gagnon, J. F., Pelletier, A., & Montplaisir, J. Y. (2012). How does Parkinsonism start? Prodromal Parkinsonism motor changes in idiopathic REM sleep behaviour disorder. Brain, 135(6):1860–1870.

    Article  Google Scholar 

  • Rusz, J., Hlavnička, J., Tykalová, T., Bušková, J., Ulmanová, O., Růžička, E., & Šonka, K. (2016). Quantitative assessment of motor speech abnormalities in idiopathic rapid eye movement sleep behaviour disorder. Sleep Medicine, 19, 141–147.

    Article  Google Scholar 

  • Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., & Kursun, O. (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 

  • Schenck, C. H., Montplaisir, J. Y., Frauscher, B., Hogl, B., Gagnon, J. F., Postuma, R., Sonka, K., Jennum, P., Partinen, M., Arnulf, I., & de Cock, V. C. (2013). Rapid eye movement sleep behavior disorder: devising controlled active treatment studies for symptomatic and neuroprotective therapy—A consensus statement from the International Rapid Eye Movement Sleep Behavior Disorder Study Group. Sleep Medicine, 14(8), 795–806.

    Article  Google Scholar 

  • Skodda, S., Rinsche, H., & Schlegel, U. (2009). Progression of dysprosody in Parkinson’s disease over time—A longitudinal study. Movement Disorders: Official Journal of the Movement Disorder Society, 24(5):716–722.

    Article  Google Scholar 

  • Tsanas, A., Little, M. A., Fox, C., & Ramig, L. O. (2014). Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(1), 181–190.

    Article  Google Scholar 

  • Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012). Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 59(5), 1264–1271.

    Article  Google Scholar 

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

Download references

Author information

Authors and Affiliations

  1. Electronic Systems, Sensors and Nanobiotechnologies (E2SN), ENSET, Mohammed V University in Rabat, Rabat, Morocco

    Achraf Benba, Abdelilah Jilbab & Ahmed Hammouch

  2. Laboratory of Control and Mechanical Characterization of Materials and Structures, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, B.P 8118, Oasi, Casablanca, Morocco

    Sara Sandabad

Authors
  1. Achraf Benba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelilah Jilbab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara Sandabad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Hammouch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Achraf Benba.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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).

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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., Sandabad, S. et al. Voice signal processing for detecting possible early signs of Parkinson’s disease in patients with rapid eye movement sleep behavior disorder. Int J Speech Technol 22, 121–129 (2019). https://doi.org/10.1007/s10772-018-09588-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-018-09588-0

Keywords

Search

Navigation