Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An <i>in-silico</i> Study.

Bikia, Vasiliki; Rovas, Georgios; Pagoulatou, Stamatia; Stergiopulos, Nikolaos

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study.

Bikia, Vasiliki; Rovas, Georgios; Pagoulatou, Stamatia; Stergiopulos, Nikolaos.

Afiliación

Bikia V; Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland.
Rovas G; Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland.
Pagoulatou S; Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland.
Stergiopulos N; Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland.

Front Bioeng Biotechnol ; 9: 649866, 2021.

Article en En | MEDLINE | ID: mdl-34055758

Palabras clave

aorta; arterial stiffness; machine learning; non-invasive monitoring; vascular aging

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Bioeng Biotechnol Año: 2021 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Suiza

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