Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings

In voice research and clinical assessment, many objective parameters are in use. However, there is no commonly used set of parameters that reflect certain voice disorders, such as functional dysphonia (FD); i.e. disorders with no visible anatomical changes. Hence, 358 high-speed videoendoscopy (HSV)...

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Detalles Bibliográficos
Autores principales: Schlegel, Patrick, Kniesburges, Stefan, Dürr, Stephan, Schützenberger, Anne, Döllinger, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324600/
https://www.ncbi.nlm.nih.gov/pubmed/32601277
http://dx.doi.org/10.1038/s41598-020-66405-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324600/
https://www.ncbi.nlm.nih.gov/pubmed/32601277
http://dx.doi.org/10.1038/s41598-020-66405-y

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