Saing Paul Hou

Output feedback sliding mode control for a linear multi-compartment lung mechanics system

In this paper, we develop a sliding mode control architecture to control lung volume and minute ventilation in the presence of modelling system uncertainties. Since the applied input pressure to the lungs is, in general, nonnegative and cannot be arbitrarily large, as not to damage the lungs, a sliding mode control with bounded nonnegative control inputs is proposed. The controller only uses output information (i.e., the total volume of the lungs) and automatically adjusts the applied input pressure so that the system is able to track a given reference signal in the presence of parameter uncertainty (i.e., modelling uncertainty of the lung resistances and lung compliances) and system disturbances. Controllers for both matched and unmatched uncertainties are presented. Specifically, a Lyapunov-based approach is presented for the stability analysis of the system and the proposed control framework is applied to a two-compartment lung model to show the efficacy of the proposed control method.

A general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters

In this paper, we develop a nonlinear multicompartment lung mechanics model that accounts for nonlinearities in both the airway resistances and the lung compliances. Many models assume that the airway resistances for a lung mechanics system are constant over the entire range of air flows, and hence, pressure losses due to the airway resistances are assumed to be linear functions of the air flows. In the development of our nonlinear multicompartment lung model, we assume that the resistive losses are characterized by a Rohrer-type model, which can more accurately capture resistive losses as a function of the flows. Several illustrative numerical examples for a two-compartment lung model are presented and the response of the multicompartment lung model with nonlinear resistances and nonlinear compliances is compared to that of a multicompartment lung model with linear resistances and nonlinear compliances.

Output-feedback sliding mode control for a linear multicompartment lung mechanics system

In this paper, an output-feedback sliding mode control (SMC) for a linear multicompartment respiratory system is developed. Since the applied input pressure to the lungs is in general nonnegative and cannot be arbitrarily large, as not to damage the lungs, a sliding mode control with bounded nonnegative control inputs is proposed. The controller only uses output information (i.e., the total volume of the lungs) and automatically adjusts the applied input pressure so that the system is able to track a given reference signal in the presence of parameter uncertainty (i.e., modelling uncertainty of the lung resistances and lung compliances) and system disturbances. Controllers for both matched and unmatched uncertainty are presented. Specifically, a Lyapunov-based approach is presented for the stability analysis of the system and the proposed control framework is applied to a two-compartment lung model to show the efficacy of the proposed control method.

A Neural Field Theory for Loss of Consciousness: Synaptic Drive Dynamics, System Stability, Attractors, Partial Synchronization, and Hopf Bifurcations Characterizing the Anesthetic Cascade

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state—that is, lack of responsiveness to noxious stimuli. In this chapter we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the anesthetic cascade. The proposed synaptic drive firing rate model predicts the conscious-unconscious transition as the implied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincare-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization of neural activity without mean field assumptions. We do this by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.

Partial synchronization of biological neural networks and the anesthetic cascade

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic model for neural activity to study the anesthetic cascade. Furthermore, we address the more general question of synchronization of neural activity without mean field assumptions. This is done by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.

A Mechanistic neural mean field theory of how anesthesia suppresses consciousness: Synaptic drive dynamics, system stability, bifurcations, and attractors

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the anesthetic cascade. The proposed synaptic drive firing rate model predicts the conscious-unconscious transition as the implied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state.

A Mechanistic Neural Field Theory of How Anesthesia Suppresses Consciousness: Synaptic Drive Dynamics, Bifurcations, Attractors, and Partial State Equipartitioning.

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the abrupt transition from consciousness to unconsciousness as the concentration of the anesthetic agent increases. The proposed synaptic drive firing-rate model predicts the conscious–unconscious transition as the applied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré–Andronov–Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization and partial state equipartitioning of neural activity without mean field assumptions. This is done by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.

Optimal Determination of Respiratory Airflow Patterns for a General Multicompartment Lung Mechanics System With Nonlinear Resistance and Compliance Parameters

NPRP Grant No. 4-187-2-060 from the Qatar National Research Fund (a member of theQatar Foundation)