NDLI: Analysis of phonation in patients with Parkinson's disease using empirical mode decomposition

Content Provider	IEEE Xplore Digital Library
Author	Smekal, Z. Mekyska, J. Galaz, Z. Mzourek, Z. Rektorova, I. Faundez-Zanuy, M.
Copyright Year	2015
Description	Author affiliation: Escola Univ. Politec. de Mataro, Barcelona, Spain (Faundez-Zanuy, M.) \|\| Dept. of Telecommun., Brno Univ. of Technol., Brno, Czech Republic (Smekal, Z.; Mekyska, J.; Galaz, Z.; Mzourek, Z.) \|\| First Dept. of Neurology, St. Anne's Univ. Hosp., Brno, Czech Republic (Rektorova, I.)
Abstract	This paper deals with an acoustic analysis of hypokinetic dysarthria in patients with Parkinson's disease (PD). The analysis is based on parametrization of five basic Czech vowels using conventional features and parameters based on empirical mode decomposition (EMD). Experimental dataset consists of 84 PD patients with different disease progress and 49 healthy controls. From the single-vowel-analysis point of view we observed that sustained vowels pronounced with minimum intensity (not whispering) outperformed the other vowels' realization (including the most popular sustained vowel [a] pronounced with normal intensity). Then we employed a classification along with feature selection and again obtained the best results in the case of silent sustained vowels (accuracy ACC = 84 %, sensitivity SEN = 86% and specificity SPE = 82 %). Finally we considered classification of PD using different vowels' realization and reached accuracy = 94 %, sensitivity = 96% and specificity = 90 %. Features based on EMD significantly improved the results.
Starting Page	1
Ending Page	4
File Size	648425
Page Count	4
File Format	PDF
e-ISBN	9781467374880
DOI	10.1109/ISSCS.2015.7203931
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-09
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensitivity Accuracy Empirical mode decomposition Correlation Parkinson's disease Speech Acoustics
Content Type	Text
Resource Type	Article

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