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Towards Disease-specific Speech Markers for Differential Diagnosis in Parkinsonism

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F19%3A10395872" target="_blank" >RIV/00216208:11110/19:10395872 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21230/19:00334333

  • Result on the web

    <a href="https://doi.org/10.1109/ICASSP.2019.8683887" target="_blank" >https://doi.org/10.1109/ICASSP.2019.8683887</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP.2019.8683887" target="_blank" >10.1109/ICASSP.2019.8683887</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Towards Disease-specific Speech Markers for Differential Diagnosis in Parkinsonism

  • Original language description

    Parkinsonism refers to Parkinson&apos;s Disease (PD) and Atypical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Discrimination between PD and APS and within APS groups in early disease stages is a very challenging task. Interestingly, speech disorder is frequently an early and prominent clinical feature of both PD and APS. This renders speech/voice analysis a promising tool for the development of an objective marker to assist neurologists in their diagnosis. This paper is a continuation of a recent work on speech-based differential diagnosis within APS. We address the difficult problem of defining disease-specific speech features which is crucial in the perspective of early differential diagnosis. We investigate this problem by considering the constraint that only a small amount of training data can be available in this setting. To do so, we perform univariate statistical analysis followed by a supervised learning that forces the designed new features to be 1-dimensional. We carry out experiments using speech recordings of MSA and PSP patients. We show that linear classification models allow the definition of new scalar variables which can be considered as speech features which are specific to each disease, MSA and PSP

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    30103 - Neurosciences (including psychophysiology)

Result continuities

  • Project

    <a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

  • ISBN

    978-1-4799-8131-1

  • ISSN

    1520-6149

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    5846-5850

  • Publisher name

    Institute of Electrical and Electronics Engineers Inc.

  • Place of publication

    Brighton

  • Event location

    Brighton

  • Event date

    May 12, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000482554006015