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Speech-based identification of L-DOPA ON/OFF state in Parkinson’s Disease subjects

View ORCID ProfileR. Norel, C. Agurto, J.J. Rice, B.K. Ho, G.A. Cecchi
doi: https://doi.org/10.1101/420422
R. Norel
1IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA
PhD
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  • ORCID record for R. Norel
C. Agurto
1IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA
PhD
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J.J. Rice
1IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA
PhD
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B.K. Ho
2Department of Neurology, Tufts University School of Medicine and Tufts Medical Center, 800 Washington St, Boston, MA 02111 USA
MD
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G.A. Cecchi
1IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA
PhD
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Abstract

Background Parkinson’s disease patients (PDP) are evaluated using the unified Parkinson’s disease rating scale (UP-DRS) to follow the longitudinal course of the disease. UP-DRS evaluation is performed by a neurologist, and hence its use is limited in the evaluation of short-term (daily) fluctuations. Subjects taking L-DOPA as part of treatment to reduce symptoms exhibit motor fluctuations as a common complication.

Objectives The aim of the study is to assess the use of speech analysis as a proxy to continuously monitor PDP medication state.

Methods We combine acoustic, prosody, and semantic features to characterize three speech tasks (picture description, reverse counting and diadochokinetic rate) of 25 PDP evaluated under different medication states: “ON” and “OFF” L-DOPA.

Results Classification of medication states using features extracted from audio recordings results in cross-validated accuracy rates of 0.88, 0.84 and 0.71 for the picture description, reverse counting and diadochokinetic rate tasks, respectively. When adding feature selection and semantic features, the accuracy rates increase to 1.00, 0.96 and 0.83 respectively; thus reaching very high classification accuracy on 3 different tasks.

Conclusions We show that speech-based features are highly predictive of medication state. Given that the highest performance was obtained with a very naturalistic task (picture description), our results suggest the feasibility of accurate, non-burdensome and high-frequency monitoring of medication effects.

Footnotes

  • Funding information

    This is internally funded work from IBM and Pfizer.

  • Abbreviations
    MFCC
    Mel-Frequency Cepstral Coefficients
    PD
    Parkinson’s Disease
    PDP
    Parkinson’s Disease Patients

  • ↵† Deceased 23 February 2018

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 18, 2018.
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Speech-based identification of L-DOPA ON/OFF state in Parkinson’s Disease subjects
R. Norel, C. Agurto, J.J. Rice, B.K. Ho, G.A. Cecchi
bioRxiv 420422; doi: https://doi.org/10.1101/420422
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Speech-based identification of L-DOPA ON/OFF state in Parkinson’s Disease subjects
R. Norel, C. Agurto, J.J. Rice, B.K. Ho, G.A. Cecchi
bioRxiv 420422; doi: https://doi.org/10.1101/420422

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