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Parkinson Disease Detetion from Speech Articulation Neuromechanics

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F17%3A00067350" target="_blank" >RIV/00159816:_____/17:00067350 - isvavai.cz</a>

  • Alternative codes found

    RIV/65269705:_____/17:00074565

  • Result on the web

    <a href="https://www.frontiersin.org/articles/10.3389/fninf.2017.00056/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fninf.2017.00056/full</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3389/fninf.2017.00056" target="_blank" >10.3389/fninf.2017.00056</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Parkinson Disease Detetion from Speech Articulation Neuromechanics

  • Original language description

    Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech. Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set. Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females). ...

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30210 - Clinical neurology

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Frontiers in Neuroinformatics

  • ISSN

    1662-5196

  • e-ISSN

  • Volume of the periodical

    11

  • Issue of the periodical within the volume

    AUG 25

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    17

  • Pages from-to

  • UT code for WoS article

    000408537700001

  • EID of the result in the Scopus database