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'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
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Czech description
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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