Machine Learning Based Automatic Prediction of Parkinson’s Disease Using Speech Features-Reference-Cited by-同舟云学术

Machine Learning Based Automatic Prediction of Parkinson’s Disease Using Speech Features

Published:2020-07-02 Issue: Volume: Page:351-362
ISSN:2194-5357
Container-title:Proceedings of International Conference on Artificial Intelligence and Applications
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Author:

Jain Deepali,Mishra Arnab Kumar,Das Sujit Kumar

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-4992-2_33

Reference13 articles.

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2. R.K. Sharma, Early detection of Parkinson’s disease through Voice, in 2014 International Conference on Advances in Engineering and Technology (ICAET) (IEEE, 2014), pp. 1–5

3. J. Rusz, R. Cmejla, H. Ruzickova, E. Ruzicka, Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease. J. Acoust. Soc. Am. 129(1), 350–367 (2011)

4. B.T. Harel, M.S. Cannizzaro, H. Cohen, N. Reilly, P.J. Snyder, Acoustic characteristics of Parkinsonian speech: a potential biomarker of early disease progression and treatment. J. Neurolinguist. 17(6), 439–453 (2004)

5. C.O. Sakar, S. Gorkem, A. Gunduz, H.C. Tunc, H. Nizam, B.E. Sakar, M. Tutuncu, T. Aydin, M. Erdem Isenkul, H. Apaydin, A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput. 74, 255–263 (2019)

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