Simulations of the Cardiovascular System Using the Cardiovascular Simulation Toolbox

Ortiz-León, Gabriela; Vílchez-Monge, Marta; Montero-Rodríguez, Juan José

doi:10.4230/oasics.mcps.2014.28

Cited by 1 publication

(2 citation statements)

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“…Alternative approach would be to use statistical techniques to invert mathematically formulated forward models (Bach et al, 2018), or fitting HR dynamics to a comprehensive Regulation at lower levels are more deterministic, more investigated and numerous heart-regulation models, tried to include this knowledge, for example models of Ursino (1998) and it's extensions (Ursino and Magosso, 2000;Hsing-Hua Fan and Khoo, 2002;Magosso et al, 2002;Cheng et al, 2010;Albanese et al, 2016;Park et al, 2020), Kotani et al (2005) and it's derivations (e.g. Ishbulatov et al, 2020), and other closed loop models (Van Roon et al, 2004;Ortiz-León et al, 2014) and specific purpose open loop models (Randall et al, 2019) for humans. Additional model linkage with registered biosignals would make the model adaptive and personalizable.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous heart regulation models have been developed, for example, by Ursino (1998), its extensions (Ursino and Magosso, 2000; Hsing-Hua Fan and Khoo, 2002; Magosso et al, 2002; Cheng et al, 2010; Albanese et al, 2016; Park et al, 2020), Kotani et al (2005), its derivations (e.g., Ishbulatov et al, 2020), and other closed-loop models (Van Roon et al, 2004; Ortiz-León et al, 2014) and specific purpose open-loop models (Randall et al, 2019) for humans. Linking models with registered biosignals would make it more adaptive and personalizable .…”

Section: Introductionmentioning

confidence: 99%

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Model-based concept to extract heart beat-to-beat variations beyond respiratory arrhythmia and baroreflex

Baranauskas

Stanikūnas

Kaniušas

et al. 2023

Preprint

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The heart rate (HR) and its variability (HRV) reflect modulation of the autonomous nervous system, especially sympathovagal balance. The aim of this research was to develop a personalizable HR model and an in-silico system that could identify HR regulation parameters associated with respiratory arrhythmia (RSA) and baroreflex, and subsequently capture the residual heart beat-to-beat variations from individual psychophysiological recordings in humans. Here respiration signal, blood pressure signal and time instances of R peaks of EKG are used as input for the model. The model considers traditional lower-order mechanisms of HR dynamic, extracting residual displacements of the modeled R peaks relative to real R peaks. Three components - tonic, spontaneous and 0.1 Hz changes - can be seen in these R peak displacements. These dynamic residuals can help to analyze HRV beyond RSA and baroreflex, whereas our model-based concept suggests that the residuals are not merely modeling errors. The proposed method could help to investigate the presumably additional neural regulation impulses from higher-order brain and other influences.

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