Stochastic thermodynamics: dissipativity, accumulativity, energy storage and entropy production

In this paper, we develop an energy-based dynamical system model driven by a Markov input process to present a unified framework for stochastic thermodynamics predicated on a stochastic dynamical systems formalism. Specifically, using a stochastic dissipativity, losslessness and accumulativity theory, we develop a nonlinear stochastic port-Hamiltonian system model characterized by energy conservation and entropy non-conservation laws that are consistent with statistical thermodynamic principles. In particular, we show that the difference between the average stored system energy and the average supplied system energy for our stochastic thermodynamic model is a martingale with respect to the system filtration, whereas the difference between average system entropy production and the average system entropy consumption is a submartingale with respect to the system filtration. This article is part of the theme issue 'Thermodynamics 2.0: Bridging the natural and social sciences (Part 2)'.

A Secure Control Learning Framework for Cyber-Physical Systems Under Sensor and Actuator Attacks

In this article, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. Specifically, we use a bank of observer-based estimators to detect the attacks while introducing a threat-detection level function. Under nominal conditions, the system operates with a nominal-feedback controller with the developed attack monitoring process checking the reliance of the measurements. If there exists an attacker injecting attack signals to a subset of the sensors and/or actuators, then the attack mitigation process is triggered and a two-player, zero-sum differential game is formulated with the defender being the minimizer and the attacker being the maximizer. Next, we solve the underlying joint state estimation and attack mitigation problem and learn the secure control policy using a reinforcement-learning-based algorithm. Finally, two illustrative numerical examples are provided to show the efficacy of the proposed framework.

Closed-Loop Control for Fluid Resuscitation: Recent Advances and Future Challenges

Fluid therapy is extensively used to treat traumatized patients as well as patients during surgery. The fluid therapy process is complex due to interpatient variability in response to therapy as well as other complicating factors such as comorbidities and general anesthesia. These complexities can result in under- or over-resuscitation. Given the complexity of the fluid management process as well as the increased capabilities in hemodynamic monitoring, closed-loop fluid management can reduce the workload of the overworked clinician while ensuring specific constraints on hemodynamic endpoints are met with higher accuracy. The goal of this paper is to provide an overview of closed-loop control systems for fluid management and highlight several key steps in transitioning such a technology from bench to the bedside.

Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol

INTRODUCTION

Objective pain assessment in non-verbal populations is clinically challenging due to their inability to express their pain via self-report. Repetitive exposures to acute or prolonged pain lead to clinical instability, with long-term behavioural and cognitive sequelae in newborn infants. Strong analgesics are also associated with medical complications, potential neurotoxicity and altered brain development. Pain scores performed by bedside nurses provide subjective, observer-dependent assessments rather than objective data for infant pain management; the required observations are labour intensive, difficult to perform by a nurse who is concurrently performing the procedure and increase the nursing workload. Multimodal pain assessment, using sensor-fusion and machine-learning algorithms, can provide a patient-centred, context-dependent, observer-independent and objective pain measure.

METHODS AND ANALYSIS

In newborns undergoing painful procedures, we use facial electromyography to record facial muscle activity-related infant pain, ECG to examine heart rate (HR) changes and HR variability, electrodermal activity (skin conductance) to measure catecholamine-induced palmar sweating, changes in oxygen saturations and skin perfusion, and electroencephalography using active electrodes to assess brain activity in real time. This multimodal approach has the potential to improve the accuracy of pain assessment in non-verbal infants and may even allow continuous pain monitoring at the bedside. The feasibility of this approach will be evaluated in an observational prospective study of clinically required painful procedures in 60 preterm and term newborns, and infants aged 6 months or less.

ETHICS AND DISSEMINATION

The Institutional Review Board of the Stanford University approved the protocol. Study findings will be published in peer-reviewed journals, presented at scientific meetings, taught via webinars, podcasts and video tutorials, and listed on academic/scientific websites. Future studies will validate and refine this approach using the minimum number of sensors required to assess neonatal/infant pain.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT03330496).

Optimal adaptive control of drug dosing using integral reinforcement learning

In this paper, a reinforcement learning (RL)-based optimal adaptive control approach is proposed for the continuous infusion of a sedative drug to maintain a required level of sedation. To illustrate the proposed method, we use the common anesthetic drug propofol used in intensive care units (ICUs). The proposed online integral reinforcement learning (IRL) algorithm is designed to provide optimal drug dosing for a given performance measure that iteratively updates the control solution with respect to the pharmacology of the patient while guaranteeing convergence to the optimal solution. Numerical results are presented using 10 simulated patients that demonstrate the efficacy of the proposed IRL-based controller.

A pilot study evaluating adaptive closed-loop fluid resuscitation during states of absolute and relative hypovolemia in dogs

OBJECTIVE

To evaluate and determine the performance of a partially automated as well as a fully automated closed-loop fluid resuscitation system during states of absolute and relative hypovolemia.

DESIGN

Prospective experimental trial.

SETTING

Research laboratory.

ANIMALS

Five adult Beagle dogs.

METHODS

Isoflurane anesthetized mechanically ventilated dogs were subjected to absolute hypovolemia (controlled: 2 trials; uncontrolled: 3 trials), relative hypovolemia (2 trials), and the combination of relative and absolute controlled hypovolemia (2 trials). Controlled and uncontrolled hypovolemia were produced by withdrawing blood from the carotid or femoral artery. Relative hypovolemia was produced by increasing the isoflurane concentration (1 trial) or by infusion of intravenous sodium nitroprusside (1 trial). Relative hypovolemia combined with controlled absolute hypovolemia was produced by increasing the isoflurane concentration (1 trial) and infusion of IV sodium nitroprusside (1 trial). Hemodynamic parameters including stroke volume variation (SVV) were continuously monitored and recorded in all dogs. A proprietary closed-loop fluid administration system based on fluid distribution and compartmental dynamical systems administered a continuous infusion of lactated Ringers solution in order to restore and maintain SVV to a predetermined target value.

MEASUREMENTS AND MAIN RESULTS

A total of 9 experiments were performed on 5 dogs. Hemodynamic parameters deteriorated and SVV increased during controlled or uncontrolled hypovolemia, relative hypovolemia, and during relative hypovolemia combined with controlled hypovolemia. Stroke volume variation was restored to baseline values during closed-loop fluid infusion.

CONCLUSIONS

Closed-loop fluid administration based on IV fluid distribution and compartmental dynamical systems can be used to provide goal directed fluid therapy during absolute or relative hypovolemia in mechanically ventilated isoflurane anesthetized dogs.

A Microsoft Kinect-Based Point-of-Care Gait Assessment Framework for Multiple Sclerosis Patients

Gait impairment is a prevalent and important difficulty for patients with multiple sclerosis (MS), a common neurological disorder. An easy to use tool to objectively evaluate gait in MS patients in a clinical setting can assist clinicians to perform an objective assessment. The overall objective of this study is to develop a framework to quantify gait abnormalities in MS patients using the Microsoft Kinect for the Windows sensor; an inexpensive, easy to use, portable camera. Specifically, we aim to evaluate its feasibility for utilization in a clinical setting, assess its reliability, evaluate the validity of gait indices obtained, and evaluate a novel set of gait indices based on the concept of dynamic time warping. In this study, ten ambulatory MS patients, and ten age and sex-matched normal controls were studied at one session in a clinical setting with gait assessment using a Kinect camera. The expanded disability status scale (EDSS) clinical ambulation score was calculated for the MS subjects, and patients completed the Multiple Sclerosis walking scale (MSWS). Based on this study, we established the potential feasibility of using a Microsoft Kinect camera in a clinical setting. Seven out of the eight gait indices obtained using the proposed method were reliable with intraclass correlation coefficients ranging from 0.61 to 0.99. All eight MS gait indices were significantly different from those of the controls (p-values less than 0.05). Finally, seven out of the eight MS gait indices were correlated with the objective and subjective gait measures (Pearson's correlation coefficients greater than 0.40). This study shows that the Kinect camera is an easy to use tool to assess gait in MS patients in a clinical setting.

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.

A stochastic mean field model for an excitatory and inhibitory synaptic drive cortical neuronal network

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 develop a mean field synaptic drive firing rate cortical neuronal model and demonstrate how the induction of general anesthesia can be explained using multistability; the property whereby the solutions of a dynamical system exhibit multiple attracting equilibria under asymptotically slowly changing inputs or system parameters. In particular, we demonstrate multistability in the mean when the system initial conditions or the system coefficients of the neuronal connectivity matrix are random variables. Uncertainty in the system coefficients is captured by representing system uncertain parameters by a multiplicative white noise model wherein stochastic integration is interpreted in the sense of Itô. Modeling a priori system parameter uncertainty using a multiplicative white noise model is motivated by means of the maximum entropy principle of Jaynes and statistical analysis.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems

Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in [1] to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations.

Pressure- and work-limited neuroadaptive control for mechanical ventilation of critical care patients

In this paper, we develop a neuroadaptive control architecture to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure - and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multicompartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patient's physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. Finally, the effect of spontaneous breathing is incorporated within the lung model and the control framework.

Classification of astrocytomas and oligodendrogliomas from mass spectrometry data using sparse kernel machines

Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample's histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry.

A compressive sensing approach for glioma margin delineation using mass spectrometry

Surgery, and specifically, tumor resection, is the primary treatment for most patients suffering from brain tumors. Medical imaging techniques, and in particular, magnetic resonance imaging are currently used in diagnosis as well as image-guided surgery procedures. However, studies show that computed tomography and magnetic resonance imaging fail to accurately identify the full extent of malignant brain tumors and their microscopic infiltration. Mass spectrometry is a well-known analytical technique used to identify molecules in a given sample based on their mass. In a recent study, it is proposed to use mass spectrometry as an intraoperative tool for discriminating tumor and non-tumor tissue. Integration of mass spectrometry with the resection module allows for tumor resection and immediate molecular analysis. In this paper, we propose a framework for tumor margin delineation using compressive sensing. Specifically, we show that the spatial distribution of tumor cell concentration can be efficiently reconstructed and updated using mass spectrometry information from the resected tissue. In addition, our proposed framework is model-free, and hence, requires no prior information of spatial distribution of the tumor cell concentration.

A Q-modification neuroadaptive control architecture for discrete-time systems

This brief extends the new neuroadaptive control framework for continuous-time nonlinear uncertain dynamical systems based on a Q -modification architecture to discrete-time systems. As in the continuous-time case, the discrete-time update laws involve auxiliary terms, or Q-modification terms, predicated on an estimate of the unknown neural network weights which in turn involve a set of auxiliary equations characterizing a set of affine hyperplanes. In addition, we show that the Q -modification terms in the discrete-time update law are designed to minimize an error criterion involving a sum of squares of the distances between the update weights and the family of affine hyperplanes.

Agitation and pain assessment using digital imaging

Pain assessment in patients who are unable to verbally communicate with medical staff is a challenging problem in patient critical care. The fundamental limitations in sedation and pain assessment in the intensive care unit (ICU) stem from subjective assessment criteria, rather than quantifiable, measurable data for ICU sedation and analgesia. This often results in poor quality and inconsistent treatment of patient agitation and pain from nurse to nurse. Recent advancements in pattern recognition techniques using a relevance vector machine algorithm can assist medical staff in assessing sedation and pain by constantly monitoring the patient and providing the clinician with quantifiable data for ICU sedation. In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners.

Relevance vector machine learning for neonate pain intensity assessment using digital imaging

Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent "pure" facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners.

A new neuroadaptive control architecture for nonlinear uncertain dynamical systems: beyond sigma- and e-modifications

This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving time window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress and cancel system uncertainty without the need for persistency of excitation. A nonlinear parametrization of the system uncertainty is considered and state and output feedback neuroadaptive controllers are developed. To illustrate the efficacy of the proposed approach we apply our results to a spacecraft model with unknown moment of inertia and compare our results with standard neuroadaptive control methods.

Neural network adaptive output feedback control for intensive care unit sedation and intraoperative anesthesia

The potential applications of neural adaptive control for pharmacology, in general, and anesthesia and critical care unit medicine, in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery.

[Macular pigment and age-related macular degeneration. Clinical implications]

The potential impact of macular pigment on the development of age-related macular degeneration (AMD) is currently a major research avenue. The role of oxidative damage in the pathogenesis of AMD has been recently confirmed by the results of a large randomized clinical trial, the AREDS (Age-Related Eye Disease Study). This study has established that high-dose supplementation with vitamins C and E, beta carotene, and zinc might prevent AMD progression and visual acuity loss in a large but determined subset of patients. Macular pigment components (mainly lutein and zeaxanthin) are highly resistant to free radicals. Moreover, extensive data from clinical, epidemiological and experimental studies suggest that lutein and zeaxanthin might protect against the development of AMD. Furthermore, an additional intake of lutein and/or zeaxanthin seems to induce an increase of the density of the macular pigment. However, a careful review of the available data suggest that only future randomized clinical trials will allow to determine the exact role of lutein and zeaxanthin in the prevention of AMD.

Neural network adaptive control for nonlinear nonnegative dynamical systems

Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a full-state feedback neural adaptive control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions.

Passivity-Based Neural Network Adaptive Output Feedback Control for Nonlinear Nonnegative Dynamical Systems

The potential clinical applications of adaptive neural network control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear nonnegative systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegatiye orthant of the state-space for nonnegative initial conditions. Finally, a numerical example involving the infusion of the anesthetic drug midazolam for maintaining a desired constant level of depth of anesthesia for noncardiac surgery is provided to demonstrate the efficacy of the proposed approach.

On nonoscillation and monotonicity of solutions of nonnegative and compartmental dynamical systems

Nonnegative and compartmental dynamical system models are widespread in biological, physiological, and ecological sciences and play a key role in understanding these processes. In the specific field of pharmacokinetics involving the study of drug concentrations (in various tissue groups) as a function of time and dose, nonnegative and compartmental models are vital in understanding system wide effects of pharmacological agents. Since drug concentrations are often assumed to monotonically decline after discontinuation of drug administration, standard pharmacokinetic modeling may ignore the possibility of system oscillation. However, nonnegative and compartmental system models may exhibit nonmonotonic solutions resulting in differences between model predictions and experimental data. In this paper, we present necessary and sufficient conditions for identifying nonnegative and compartmental systems that only admit nonoscillatory and monotonic solutions.

[Decompensation of a cirsoid aneurysm: a case report]

We report a case of a 27-year-old woman with a cirsoid aneurysm, also called congenital arteriovenous communication of the retina. The cirsoid aneurysm, stage I according to Archer's classification, was revealed by a serous retinal detachment. The exudation resolved, and acuity was recovered after a short time, but exudation recurred during the 5 years of follow-up. Exudation of type I cirsoid aneurysm is rare. The clinical and pathogenic features are discussed.

Eligibility for treatment and angiographic features at the early stage of exudative age related macular degeneration

AIMS

To determine the eligibility for laser photocoagulation treatment or for photodynamic therapy (PDT) with verteporfin in eyes at the earliest stage (first month of symptoms) of exudative age related macular degeneration (AMD) based on fluorescein angiographic (FA) features; to evaluate the potential contribution of indocyanine green angiography (ICG-A) for occult choroidal neovascularisation (CNV) at this stage.

METHODS

Retrospective review of 252 consecutive patients (269 eyes) examined within the first month of symptoms of exudative AMD.

RESULTS

On FA, 97 eyes (36%) had classic CNV alone. Occult CNV associated with fibrovascular retinal pigment epithelium detachments (PEDs) was observed in 71 eyes (26%) and without fibrovascular PED in 101 eyes (38%). 91 eyes (34%) met the Macular Photocoagulation Study criteria for laser photocoagulation. 53 eyes (20%) met the Verteporfin In PDT (VIP) or Treatment of AMD with PDT (TAP) studies criteria. By ICG-A, occult CNV was visualised as focal spots in 49% of eyes examined within 15 days v 32% of eyes examined between 16 and 30 days after the onset of symptoms (p=0.07). 8.5% of late staining plaques disclosed in eyes examined within 15 days were combined with focal spots v 36% in eyes examined between 16 and 30 days (p<0.01).

CONCLUSIONS

Early examination of eyes with exudative AMD would allow the treatment of 47% of eyes. 60% of eyes with subfoveal CNV would be eligible for PDT with verteporfin. Up to half of eyes with occult CNV would be converted by ICG-A into well delineated focal spots.