Synthetic pulse wave dataset for analysis of vascular ageing in elderly patients

This paper presents a methodology to generate synthetic pulse wave database. Each virtual subject is generated with the help of one-dimensional hemodynamics model of systemic circulation with lumped model of the left heart. This paper describes and compares two parameter optimization methods: unscen...

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Published inMathematical modelling of natural phenomena Vol. 19; p. 20
Main Authors Rogov, Artem, Gamilov, Timur, Bragina, Anna, Abdullaev, Magomed, Druzhinina, Natalia, Rodionova, Yuliya, Shikhmagomedov, Rustam, Tyulin, Maksim, Podzolkov, Valeriy
Format Journal Article
LanguageEnglish
Published 2024
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Abstract This paper presents a methodology to generate synthetic pulse wave database. Each virtual subject is generated with the help of one-dimensional hemodynamics model of systemic circulation with lumped model of the left heart. This paper describes and compares two parameter optimization methods: unscented Kalman filter and Bayesian optimization. As a case study, an experiment is conducted to predict cardio-ankle vascular index (CAVI) values for real individuals with a machine learning algorithm trained on a synthetic population. The average error of 6.5% is achieved
AbstractList This paper presents a methodology to generate synthetic pulse wave database. Each virtual subject is generated with the help of one-dimensional hemodynamics model of systemic circulation with lumped model of the left heart. This paper describes and compares two parameter optimization methods: unscented Kalman filter and Bayesian optimization. As a case study, an experiment is conducted to predict cardio-ankle vascular index (CAVI) values for real individuals with a machine learning algorithm trained on a synthetic population. The average error of 6.5% is achieved
Author Abdullaev, Magomed
Bragina, Anna
Tyulin, Maksim
Rodionova, Yuliya
Shikhmagomedov, Rustam
Rogov, Artem
Podzolkov, Valeriy
Gamilov, Timur
Druzhinina, Natalia
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