Monitoring Parkinson Disease from speech articulation kinematics

Parkinson's disease (PD) is the neurodegenerative disease which affects 2-3 % of the population beyond 65 years of age in EU. When PD treatment is administered early, it is significantly more effective. Unfortunately, it is quite challenging to detect this disease at its early stage and when the symptoms can be recognized it is usually quite late. For this reason there is big motivation for development more accessible and accurate solutions for the detection of PD. One of the early symptoms is so-called hypomimia. This paper introduces an automatic method, which can objectively detect PD. The method is based on analysis of emotion changes during pronunciation defined speech exercises. We achieved balanced accuracy 69 % using XGBoost algorithm. As the exercise we proposed to use a Czech tongue twister-the difficult to pronounce sentence. The features can be explained and thus it can be used in clinical practice. We identified that the most valuable emotion for PD detection in this case is fear. 124 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset

Wang

Yan²,

Shao³

et al. 2022

Front. Aging Neurosci.

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Parkinson’s disease (PD) is a neurodegenerative disorder that negatively affects millions of people. Early detection is of vital importance. As recent researches showed dysarthria level provides good indicators to the computer-assisted diagnosis and remote monitoring of patients at the early stages. It is the goal of this study to develop an automatic detection method based on newest collected Chinese dataset. Unlike English, no agreement was reached on the main features indicating language disorders due to vocal organ dysfunction. Thus, one of our approaches is to classify the speech phonation and articulation with a machine learning-based feature selection model. Based on a relatively big sample, three feature selection algorithms (LASSO, mRMR, Relief-F) were tested to select the vocal features extracted from speech signals collected in a controlled setting, followed by four classifiers (Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Stochastic Gradient Descent) to detect the disorder. The proposed approach shows an accuracy of 75.76%, sensitivity of 82.44%, specificity of 73.15% and precision of 76.57%, indicating the feasibility and promising future for an automatic and unobtrusive detection on Chinese PD. The comparison among the three selection algorithms reveals that LASSO selector has the best performance regardless types of vocal features. The best detection accuracy is obtained by SGD classifier, while the best resulting sensitivity is obtained by LR classifier. More interestingly, articulation features are more representative and indicative than phonation features among all the selection and classifying algorithms. The most prominent articulation features are F1, F2, DDF1, DDF2, BBE and MFCC.

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Monitoring Parkinson Disease from speech articulation kinematics

Cited by 3 publications

References 19 publications

Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey

Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset

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