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Automated analysis of connected speech reveals early biomarkers of Parkinson's disease in patients with rapid eye movement sleep behaviour disorder

J. Hlavnička, R. Čmejla, T. Tykalová, K. Šonka, E. Růžička, J. Rusz,

. 2017 ; 7 (1) : 12. [pub] 20170202

Language English Country England, Great Britain

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc19013181

Grant support
NV15-28038A MZ0 CEP Register
NV16-28914A MZ0 CEP Register

For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson's disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.

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$a For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson's disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.
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$a Čmejla, Roman $u Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Circuit Theory, Technická 2, 166 27, Prague 6, Czech Republic.
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$a Tykalová, Tereza $u Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Circuit Theory, Technická 2, 166 27, Prague 6, Czech Republic.
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$a Šonka, Karel $u Charles University in Prague, First Faculty of Medicine, Department of Neurology and Centre of Clinical Neuroscience, Kateřinská 30, 120 00, Prague 2, Czech Republic.
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$a Růžička, Evžen $u Charles University in Prague, First Faculty of Medicine, Department of Neurology and Centre of Clinical Neuroscience, Kateřinská 30, 120 00, Prague 2, Czech Republic.
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$a Rusz, Jan $u Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Circuit Theory, Technická 2, 166 27, Prague 6, Czech Republic. rusz.mz@gmail.com. Charles University in Prague, First Faculty of Medicine, Department of Neurology and Centre of Clinical Neuroscience, Kateřinská 30, 120 00, Prague 2, Czech Republic. rusz.mz@gmail.com.
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