Speech-based characterization of dopamine replacement therapy in people with Parkinson's disease.
Neurology
Signs and symptoms
Journal
NPJ Parkinson's disease
ISSN: 2373-8057
Titre abrégé: NPJ Parkinsons Dis
Pays: United States
ID NLM: 101675390
Informations de publication
Date de publication:
2020
2020
Historique:
received:
26
09
2019
accepted:
19
05
2020
entrez:
23
6
2020
pubmed:
23
6
2020
medline:
23
6
2020
Statut:
epublish
Résumé
People with Parkinson's (PWP) disease are under constant tension with respect to their dopamine replacement therapy (DRT) regimen. Waiting too long between doses results in more prominent symptoms, loss of motor function, and greater risk of falling per step. Shortened pill cycles can lead to accelerated habituation and faster development of disabling dyskinesias. The Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard for monitoring Parkinson's disease progression but requires a neurologist to administer and therefore is not an ideal instrument to continuously evaluate short-term disease fluctuations. We investigated the feasibility of using speech to detect changes in medication states, based on expectations of subtle changes in voice and content related to dopaminergic levels. We calculated acoustic and prosodic features for three speech tasks (picture description, reverse counting, and diadochokinetic rate) for 25 PWP, each evaluated "ON" and "OFF" DRT. Additionally, we generated semantic features for the picture description task. Classification of ON/OFF medication states using features generated from picture description, reverse counting and diadochokinetic rate tasks resulted in cross-validated accuracy rates of 0.89, 0.84, and 0.60, respectively. The most discriminating task was picture description which provided evidence that participants are more likely to use action words in ON than in OFF state. We also found that speech tempo was modified by DRT. Our results suggest that automatic speech assessment can capture changes associated with the DRT cycle. Given the ease of acquiring speech data, this method shows promise to remotely monitor DRT effects.
Identifiants
pubmed: 32566741
doi: 10.1038/s41531-020-0113-5
pii: 113
pmc: PMC7293295
doi:
Types de publication
Journal Article
Langues
eng
Pagination
12Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsR. Norel, C. Agurto, S. Heisig, J.J. Rice, R. Ostrand, G.A. Cecchi disclose that their employer, IBM Research, is the research branch of IBM Corporation. R. Norel, G.A. Cecchi, S. Heisig own stock in IBM Corporation. H. Zhang, P.W. Wacnik, V.L. Ramos disclose that their employer, Pfizer Digital Medicine & Translational Imaging: Early Clinical Development, is a research branch of Pfizer Corporation. B.K. Ho is employed by the Department of Neurology, Tufts University School of Medicine and Tufts Medical Center.
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