Speech analysis for the differential diagnosis between Parkinson's disease, progressive supranuclear palsy and multiple system atrophy

Abstract : Acoustic speech analysis has been shown to have a good potential in differentiation between Parkinson's disease and atypical Parkinsonian syndromes (APS) such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). Objective speech features were able to discriminate between PD and APS with 95% accuracy and between PSP and MSA with 75% accuracy in [7]. However, accuracy between PSP and MSA still has a large space to be improved and the more important aim is to provide more explicit information for differential diagnosis. In [7], 75% accuracy was achieved using support vector machine classifier based on radial basis function kernel. This means it's dffcult to interpret the relation between selected features and decision hyperplane. In this internship, for discrimination between PSP and MSA, 9% higher accuracy (i.e, 84% accuracy) was attained by using support vector machine classifier based on radial basis function kernel and 80% accuracy was attained using linear dimension reduction methods and linear classifier. More importantly, with this strategy, we obtain a better understanding of feature discriminative power. This can be indeed very useful in clinical application.
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Mémoires d'étudiants -- Hal-inria+
Signal and Image Processing. 2017
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Soumis le : jeudi 2 novembre 2017 - 15:06:25
Dernière modification le : vendredi 3 novembre 2017 - 01:11:09
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Gongfeng Li. Speech analysis for the differential diagnosis between Parkinson's disease, progressive supranuclear palsy and multiple system atrophy. Signal and Image Processing. 2017. 〈hal-01627868〉

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