Parkinson Disease Detection from Speech Articulation Neuromechanics

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dc.contributor.authorGomez-Vilda, Pedrocs
dc.contributor.authorMekyska, Jiřícs
dc.contributor.authorManuel Ferrandez, Josecs
dc.contributor.authorPalacios-Alonso, Danielcs
dc.contributor.authorGómez-Rodellar, Andréscs
dc.contributor.authorRodellar Biarge, María Victoriacs
dc.contributor.authorGaláž, Zoltáncs
dc.contributor.authorSmékal, Zdeněkcs
dc.contributor.authorEliášová, Ilonacs
dc.contributor.authorKošťálová, Milenacs
dc.contributor.authorRektorová, Irenacs
dc.coverage.issue56cs
dc.coverage.volume11cs
dc.date.accessioned2020-08-04T11:01:09Z
dc.date.available2020-08-04T11:01:09Z
dc.date.issued2017-08-25cs
dc.description.abstractAim: 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). Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease.en
dc.formattextcs
dc.format.extent1-17cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationFrontiers in Neuroinformatics. 2017, vol. 11, issue 56, p. 1-17.en
dc.identifier.doi10.3389/fninf.2017.00056cs
dc.identifier.issn1662-5196cs
dc.identifier.other138682cs
dc.identifier.urihttp://hdl.handle.net/11012/70132
dc.language.isoencs
dc.publisherFrontierscs
dc.relation.ispartofFrontiers in Neuroinformaticscs
dc.relation.urihttp://journal.frontiersin.org/article/10.3389/fninf.2017.00056/fullcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1662-5196/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectneurologic diseaseen
dc.subjectParkinson diseaseen
dc.subjectspeech neuromotor activityen
dc.subjectaging voiceen
dc.subjecthypokinetic dysarthriaen
dc.subjectrandom least squares feed-forward networksen
dc.titleParkinson Disease Detection from Speech Articulation Neuromechanicsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-138682en
sync.item.dbtypeVAVen
sync.item.insts2020.08.04 13:01:09en
sync.item.modts2020.08.04 12:48:00en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. oddělení-TKO-SIXcs
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