A framework for the design of a closed-loop gastric pacemaker for treating conduction block.

BACKGROUND AND OBJECTIVE: Gastrointestinal (GI) motility disorders can be significantly detrimental to the quality of life. Pacing, or long pulse gastric electrical stimulation, is a potential treatment option for treating GI motility disorders by modulating the slow wave activity. Open-loop pacing of the GI tract is the current standard for modulating dysrhythmic patterns, but it is known to be suboptimal and inefficient. Recent work on sensing intracellular potentials and pacing accordingly in a closed-loop has been shown to be effective at modulating dysrhythmic patterns. However, capturing intracellular potentials in an in-vivo setting is not viable. Therefore a closed-loop gastric electrical stimulation that can sense extracellular potentials and pace accordingly to modulate dysrhythmic patterns is required. This paper presents a closed-loop Gastric Electrical Stimulator (GES) design framework, which comprises of extracellular potential generation, sensing, and closed-loop actuation. METHODS: This work leverages a pre-existing high-fidelity two-dimensional Interstitial Cells of Cajal (ICC) network modeling framework to mimic several normal and dysrhythmic patterns observed in experimental recordings of patients suffering from GI tract diseases. The activation patterns of the of the ICC network are captured by an extracellular potential generation model and is integrated with the GES in a closed-loop to validate the efficacy of the developed pacing algorithms. The proposed GES pacing algorithms extend existing offline filtering and activation detection methods to process the sensed extracellular potentials in real time. The GES detects bradygastric rhythms based on the sensed extracellular potentials and actuates the ICC network via pacing to rectify dysrhythmic patterns. RESULTS: The proposed GES model is able to sense and process the generated noisy extracellular potentials, detect the bradygastric patterns, and modulate the slow wave activities to normal propagation effectively. CONCLUSIONS: A closed-loop GES design, which can be applied in an experimental and clinical setting is developed and validated through the ICC network model. The proposed GES model has the ability to modulate a variety of bradygastric patterns, including conduction block effectively in a closed-loop.

Development of closed-loop modelling framework for adaptive respiratory pacemakers.

OBJECTIVE: Ventilatory pacing by electrical stimulation of the phrenic nerve has many advantages compared to mechanical ventilation. However, commercially available respiratory pacing devices operate in an open-loop fashion, which require manual adjustment of stimulation parameters for a given patient. Here, we report the model development of a closed-loop respiratory pacemaker, which can automatically adapt to various pathological ventilation conditions and metabolic demands. METHODS: To assist the model design, we have personalized a computational lung model, which incorporates the mechanics of ventilation and gas exchange. The model can respond to the device stimulation where the gas exchange model provides biofeedback signals to the device. We use a pacing device model with a proportional integral (PI) controller to illustrate our approach. RESULTS: The closed-loop adaptive pacing model can provide superior treatment compared to open-loop operation. The adaptive pacing stimuli can maintain physiological oxygen levels in the blood under various simulated breathing disorders and metabolic demands. CONCLUSION: We demonstrate that the respiratory pacing devices with the biofeedback can adapt to individual needs, while the lung model can be used to validate and parametrize the device. SIGNIFICANCE: The closed-loop model-based framework paves the way towards an individualized and autonomous respiratory pacing device development.

Compositional cyber-physical epidemiology of COVID-19.

The COVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis. However, the interactions between the government policy and the disease dynamics are not formally captured. We, for the first time, formally study the interaction between the disease dynamics, which is modelled as a physical process, and the government policy, which is modelled as the adjoining controller. Our approach enables compositionality, where either the plant or the controller could be replaced by an alternative model. Our work is inspired by the engineering approach for the design of Cyber-Physical Systems. Consequently, we term the new framework Compositional Cyber-Physical Epidemiology. We created different classes of controllers and applied these to control the disease in New Zealand and Italy. Our controllers closely follow government decisions based on their published data. We not only reproduce the pandemic progression faithfully in New Zealand and Italy but also show the tradeoffs produced by differing control actions.

A novel approach for model-based design of gastric pacemakers.

Understanding the slow wave propagation patterns of Interstitial Cells of Cajal (ICC) is essential when designing Gastric Electrical Stimulators (GESs) to treat motility disorders. A GES with the ability to both sense and pace, working in closed-loop with the ICC, will enable efficient modulation of Gastrointestinal (GI) dysrhythmias. However, existing GESs targeted at modulating GI dysrhythmias operate in an open-loop and hence their clinical efficacy is uncertain. This paper proposes a novel model-based approach for designing GESs that operate in closed-loop with the GI tract. GES is modelled using Hybrid Input Output Automata (HIOA), a well-known formal model, which is suitable for designing safety-critical systems. A two-dimensional ICC network working in real-time with the GES is developed using the same compositional HIOA framework. The ICC network model is used to simulate normal and diseased action potential propagation patterns akin to those observed during GI dysrhythmias. The efficacy of the proposed GES is then validated by integrating it in closed-loop with the ICC network. Results show that the proposed GES is able to sense the propagation patterns and modulate the dysrhythmic patterns of bradygastria back to its normal state automatically. The proposed design of the GES is flexible enough to treat a variety of diseased dysrhythmic patterns using closed-loop operation.

Cardiac Electrical Modeling for Closed-Loop Validation of Implantable Devices.

OBJECTIVE: Evaluating and testing cardiac electrical devices in a closed-physiologic-loop can help design safety, but this is rarely practical or comprehensive. Furthermore, in silico closed-loop testing with biophysical computer models cannot meet the requirements of time-critical cardiac device systems, while simplified models meeting time-critical requirements may not have the necessary dynamic features. We propose a new high-level (abstracted) physiologically-based computational heart model that is time-critical and dynamic. METHODS: The model comprises cardiac regional cellular-electrophysiology types connected by a path model along a conduction network. The regional electrophysiology and paths are modeled with hybrid automata that capture non-linear dynamics, such as action potential and conduction velocity restitution and overdrive suppression. The hierarchy of pacemaker functions is incorporated to generate sinus rhythms, while abnormal automaticity can be introduced to form a variety of arrhythmias such as escape ectopic rhythms. Model parameters are calibrated using experimental data and prior model simulations. CONCLUSION: Regional electrophysiology and paths in the model match human action potentials, dynamic behavior, and cardiac activation sequences. Connected in closed loop with a pacing device in DDD mode, the model generates complex arrhythmia such as atrioventricular nodal reentry tachycardia. Such device-induced outcomes have been observed clinically and we can establish the key physiological features of the heart model that influence the device operation. SIGNIFICANCE: These findings demonstrate how an abstract heart model can be used for device validation and to design personalized treatment.

Closing the Loop: Validation of Implantable Cardiac Devices With Computational Heart Models.

OBJECTIVE: Cardiovascular Implantable Electronic Devices (CIEDs) are used extensively for treating life-threatening conditions such as bradycardia, atrioventricular block and heart failure. The complicated heterogeneous physical dynamics of patients provide distinct challenges to device development and validation. We address this problem by proposing a device testing framework within the in-silico closed-loop context of patient physiology. METHODS: We develop an automated framework to validate CIEDs in closed-loop with a high-level physiologically based computational heart model. The framework includes test generation, execution and evaluation, which automatically guides an integrated stochastic optimization algorithm for exploration of physiological conditions. CONCLUSION: The results show that using a closed loop device-heart model framework can achieve high system test coverage, while the heart model provides clinically relevant responses. The simulated findings of pacemaker mediated tachycardia risk evaluation agree well with the clinical observations. Furthermore, we illustrate how device programming parameter selection affects the treatment efficacy for specific physiological conditions. SIGNIFICANCE: This work demonstrates that incorporating model based closed-loop testing of CIEDs into their design provides important indications of safety and efficacy under constrained physiological conditions.

Resonant model-A new paradigm for modeling an action potential of biological cells.

Organ level simulation of bioelectric behavior in the body benefits from flexible and efficient models of cellular membrane potential. These computational organ and cell models can be used to study the impact of pharmaceutical drugs, test hypotheses, assess risk and for closed-loop validation of medical devices. To move closer to the real-time requirements of this modeling a new flexible Fourier based general membrane potential model, called as a Resonant model, is developed that is computationally inexpensive. The new model accurately reproduces non-linear potential morphologies for a variety of cell types. Specifically, the method is used to model human and rabbit sinoatrial node, human ventricular myocyte and squid giant axon electrophysiology. The Resonant models are validated with experimental data and with other published models. Dynamic changes in biological conditions are modeled with changing model coefficients and this approach enables ionic channel alterations to be captured. The Resonant model is used to simulate entrainment between competing sinoatrial node cells. These models can be easily implemented in low-cost digital hardware and an alternative, resource-efficient implementations of sine and cosine functions are presented and it is shown that a Fourier term is produced with two additions and a binary shift.

A Formal Approach for Scalable Simulation of Gastric ICC Electrophysiology.

OBJECTIVE: Efficient and accurate organ models are crucial for closed-loop validation of implantable medical devices. This paper investigates bio-electric slow wave modeling of the stomach, so that gastric electrical stimulator (GES) can be validated and verified prior to implantation. In particular, we consider high-fidelity, scalable, and efficient modeling of the pacemaker, Interstitial cells of Cajal (ICC), based on the formal hybrid input output automata (HIOA) framework. METHODS: Our work is founded in formal methods, a collection of mathematically sound techniques originating in computer science for the design and validation of safety-critical systems. We modeled each ICC cell using an HIOA. We also introduce an HIOA path model to capture the electrical propagation delay between cells in a network. The resultant network of ICC cells can simulate normal and diseased action potential propagation patterns, making it useful for device validation. RESULTS: The simulated slow wave of a single ICC cell had high correlation ( ≈ 0.9) with the corresponding biophysical models. CONCLUSIONS: The proposed model is able to simulate the slow wave activity of a network of ICC cells with high-fidelity for device validation. SIGNIFICANCE: The proposed HIOA model is significantly more efficient than the corresponding biophysical models, scales to larger networks of ICC cells, and is capable of simulating varying propagation patterns. This has the potential to enable verification and validation of implantable GESs in closed-loop with gastrointestinal models in the future.

A Parametric Computational Model of the Action Potential of Pacemaker Cells.

OBJECTIVE: A flexible, efficient, and verifiable pacemaker cell model is essential to the design of real-time virtual hearts that can be used for closed-loop validation of cardiac devices. A new parametric model of pacemaker action potential is developed to address this need. METHODS: The action potential phases are modeled using hybrid automaton with one piecewise-linear continuous variable. The model can capture rate-dependent dynamics, such as action potential duration restitution, conduction velocity restitution, and overdrive suppression by incorporating nonlinear update functions. Simulated dynamics of the model compared well with previous models and clinical data. CONCLUSION: The results show that the parametric model can reproduce the electrophysiological dynamics of a variety of pacemaker cells, such as sinoatrial node, atrioventricular node, and the His-Purkinje system, under varying cardiac conditions. SIGNIFICANCE: This is an important contribution toward closed-loop validation of cardiac devices using real-time heart models.