Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemente...

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Detalles Bibliográficos
Autores principales: Yang, Chenxi, Ojha, Banish D., Aranoff, Nicole D., Green, Philip, Tavassolian, Negar
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/PMC7568576/
https://www.ncbi.nlm.nih.gov/pubmed/33067495
http://dx.doi.org/10.1038/s41598-020-74519-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568576/
https://www.ncbi.nlm.nih.gov/pubmed/33067495
http://dx.doi.org/10.1038/s41598-020-74519-6

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