Abstract
Recent studies have shown that the early detection of neurodegenerative diseases (such as Parkinson) can significantly improve the effectiveness of treatments that increase quality of life, reducing the costs associated with the disease. In this paper, the proposed methodology consists in detecting early signs of Parkinson’s disease through speech, with the presence of background noise. The approach uses machine learning algorithms and signal processing techniques to correctly distinguish between healthy controls and Parkinson’s disease patients. In order to detect early signs of the disease, a database with patients at different stages of the Parkinson’s disease is used. The learning algorithms were optimized for generalization and accuracy. An analysis of the results obtained from the proposed methodology show potential uses of machine learning algorithms in biomedical applications to detect early signs of Parkinson’s disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
MIT Brain Disorders: By the Numbers (2017). https://mcgovern.mit.edu/brain-disorders/by-the-numbers. Accessed 7 Mar 2017
World Health Organization: Global burden of disease 2004 update: disability weights for diseases and conditions (2004). http://www.who.int/healthinfo/global_burden_disease/GBD2004_DisabilityWeights.pdf. Accessed 21 Mar 2017
Pagan, F.L.: Improving outcomes through early diagnosis of Parkinson’s disease. Am. J. Manag. Care 18, S176–S182 (2012)
Baasch, C., Schmidt, G., Heute, U., Nebel, A., Deuschl, G.: Parkinson-speech analysis: methods and aims. In: 12 ITG Symposium on Speech Communication, pp. 1–5 (2016)
Tsanas, A., Little, M.A., McSharry, P.E., Spielman, J., Ramig, L.O.: Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s (2011)
Oh, H., Rizo, C., Enkin, M., Jadad, A.: What is eHealth (3): A systematic review of published definitions. J. Med. Internet Res. 7, e1 (2005)
World Health Organization: WHO | eHealth at WHO. In: WHO (2017). http://www.who.int/ehealth/about/en/. Accessed 18 Oct 2017
European Commission: Policy - Public Health. In: Public Health (2017). https://ec.europa.eu/health/ehealth/policy_en. Accessed 7 Mar 2017
Little, M., Tsanas, T., Ravaru, L.: Parkinson’s Voice Initiative (2017). http://www.parkinsonsvoice.org/science.php. Accessed 7 Mar 2017
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York (2014)
Dobra, A.: Data mining. In: Data Mining, pp. 2–27 (2017)
Nebeker, F.: Fifty Years of Signal Processing (2000)
Dixit, M.V., Sharma, Y.: Voice Parameter Analysis for the disease detection, pp. 48–55 (2014)
Teixeira, J.P., Oliveira, C., Lopes, C.: Vocal acoustic analysis – Jitter, Shimmer and HNR Parameters. Procedia Technol. 9, 1112–1122 (2013)
Proença, J., Perdigão, F., Veira, A., Candeias, S., Lemos, J., Januário, C.: Characterizing Parkinson’s disease speech by acoustic and phonetic features. In: Computational Processing of the Portuguese Language, pp. 24–35 (2014)
Boersma, P., Weenink, D.: Praat: doing Phonetics by Computer (2017). http://www.fon.hum.uva.nl/praat/. Accessed 29 May 2017
Scikit Learn: Scikit-Learn 0.19.1 documentation (2017). http://scikit-learn.org/stable/. Accessed 25 Oct 2017
Silva, E.: ANADI - Correlação e Regressão - Regressão Linear Múltipla (2015)
Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
Acknowledgements
A special thanks to APPACDM of Vila Nova de Gaia for hel** with the collection of participants for the recording sessions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Braga, D., Madureira, A.M., Coelho, L., Abraham, A. (2018). Neurodegenerative Diseases Detection Through Voice Analysis. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-76351-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76350-7
Online ISBN: 978-3-319-76351-4
eBook Packages: EngineeringEngineering (R0)