PNEUMA - a comprehensive cardiorespiratory model

Abstract

11. MODEL DESCRIPTION Absnaa-The dynamic interactions among the peripheral inputs that originate from the chemoreflexes, baroreflexes and pulmonary stretch receptors, together with central autonomic Cardiovascular System -Components in this system include coupling between respiratory and cardiovascular efferents, a nonlinear carotid sinus compartment stimulated hy the play important roles in determining the physiological effects arterial blood pressure, a sino-atrial node component that that accompany sleep-disordered breathing. However, regulates the heart rate with both parasympathetic and previous modeling studies have focussed primarily on specific sympathetic controls, a peripheral resistance component regions of interest. The purpose ofthis study was to integrate controlled the alpha-sympathetic activity,,a hemodynamic submodel based on a simple two-element Windkessel the key features of existing cardiovascular and respiratory models into a comprehensive model that is capable of simulating the dynamics of cardiorespiratory control during model, and a component that controk changes in stroke wakefulness and sleep. PNEUMA is implemented using volume via changes in the venous return and cardiac SIMULINP (The Mathworks, Natick, MA), which provides a contractility [I].graphical environment that allows the nser to easily convert Respiratory System -The respiratory compartments include control block diagrams into networks of blocks of both the mechanical and the gas-exchange components. mathematical functions. The advantages of PNEUMA lie in: (1) the modularity of its various components; (2) the flexibility with which changes can be made to selected portions of the program; and (3) transportability ofthe model across different computer platforms and operating systems.

Ventilatory oscillations at exercise in hypoxia: A mathematical model

“…Longobardo (Longobardo et al, 1982) and Khoo (Khoo et al, 1982) developed models describing the mechanisms of ventilatory oscillations in sleep apnea syndrome (SAS) and chronic heart failure (CHF). Models became more complex with the addition of numerous cardiorespiratory and neural inputs, and the technological and computing progress (Fan and Khoo, 2002). These simulations have brought valuable clinical insights in our understanding of breathing disorders in SAS and CHF patients, both in their intrinsic mechanisms and in the potential treatments by O 2 or CO 2 inhalation (Cherniack, 2005).…”

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

“…There are also attempts to create patient specific models (Gray and Pathmanathan, 2018) and the trend is to create more universal models suitable for either normal or pathological – personalized – conditions paving the path from the virtual physiological human to the clinic (Hoekstra et al, 2018). One of popular models in MATLAB SIMULINK environment – PNEUMA (Hsing-Hua Fan and Khoo, 2002; Cheng et al, 2010) although is highly elaborated, can simulate multiple influences even within heart period, however it is too complex and would be too slow for search of parameters combinations for beat-to-beat personalization. Some newer models relying on the knowledge of lower-level mechanisms, propose more elaborated neural regulations at relatively higher levels; for example, Park et al (2020) model included more detailed parasympathetic activity regulation via NTS, NAmb and dorsal motor nucleus of the vagus – but this is still about regulation at lower-order medulla oblongata level.…”

“…Complex underlying regulation mechanisms and personal peculiarities are still not fully disclosed by traditional HRV measures, and other than traditional HRV measures are not always more helpful in clinical context though research on new ways to evaluate HR dynamics is still encouraged (Sassi et al, 2015)). 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.…”

“…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.…”

“…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 .…”