Individualization of Atrial Tachycardia Models for Clinical Applications: Performance of Fiber-Independent Model

One of the challenges in the development of patient-specific models of cardiac arrhythmias for clinical applications has been accounting for myocardial fiber organization. The fiber varies significantly from heart to heart, but cannot be directly measured in live tissue. The goal of this paper is to evaluate in-silico the accuracy of left atrium activation maps produced by a fiber-independent (isotropic) model with tuned diffusion coefficients, compares to a model incorporating myocardial fibers with the same geometry. For this study we utilize publicly available DT-MRI data from 7 ex-vivo hearts. The comparison is carried out in 51 cases of focal and rotor arrhythmias located in different regions of the atria. On average, the local activation time accuracy is 96% for focal and 93% for rotor arrhythmias. Given its reasonably good performance and the availability of readily accessible data for model tuning in cardiac ablation procedures, the fiber-independent model could be a promising tool for clinical applications.

Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium

Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.

Fiber Organization Has Little Effect on Electrical Activation Patterns During Focal Arrhythmias in the Left Atrium

Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.

Learning-‘N-Flying: A Learning-Based, Decentralized Mission-Aware UAS Collision Avoidance Scheme

Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft Systems (UASs) carry out a wide variety of missions (e.g., moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-‘N-Flying (LNF), a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on the fly, and allows autonomous Unmanned Aircraft System (UAS)s managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UASs as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining (1) learning-based decision-making and (2) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UASs on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds) and under certain assumptions has failure rates of less than 1% in the worst case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.

Electroanatomic Mapping to determine Scar Regions in patients with Atrial Fibrillation

Left atrial voltage maps are routinely acquired during electroanatomic mapping in patients undergoing catheter ablation for atrial fibrillation (AF). For patients, who have prior catheter ablation when they are in sinus rhythm (SR), the voltage map can be used to identify low voltage areas (LVAs) using a threshold of 0.2 - 0.45 mV. However, such a voltage threshold for maps acquired during AF has not been well established. A prerequisite for defining a voltage threshold is to maximize the topologically matched LVAs between the electroanatomic mapping acquired during AF and SR. This paper demonstrates a new technique to improve the sensitivity and specificity of the matched LVA. This is achieved by computing omni-directional bipolar voltages and applying Gaussian Process Regression based interpolation to derive the AF map. The proposed method is evaluated on a test cohort of 7 male patients, and a total of 46,589 data points were included in analysis. The LVAs in the posterior left atrium and pulmonary vein junction are determined using the standard method and the proposed method. Overall, the proposed method showed patient-specific sensitivity and specificity in matching LVAs of 75.70% and 65.55% for a geometric mean of 70.69%. On average, there was an improvement of 3.00% in the geometric mean, 7.88% improvement in sensitivity, 0.30% improvement in specificity compared to the standard method. The results show that the proposed method is an improvement in matching LVA. This may help develop the voltage threshold to better identify LVA in the left atrium for patients in AF.

Computer Aided Clinical Trials for Implantaule Cardiac Devices

In this paper we aim to answer the question, "How can modeling and simulation of physiological systems be used to evaluate life-critical implantable medical devices?" Clinical trials for medical devices are becoming increasingly inefficient as they take several years to conduct, at very high cost and suffer from high rates of failure. For example, the Rhythm ID Goes Head-to-head Trial (RIGHT) sought to evaluate the performance of two arrhythmia discriminator algorithms for implantable cardioverter defibrillators, Vitality 2 vs. Medtronic, in terms of time-to-first inappropriate therapy, but concluded with results contrary to the initial hypothesis- after 5 years, 2,000+ patients and at considerble ethical and monetary cost. In this paper, we describe the design and performance of a Computer-aided Clinical Trial (CACT) for Implantable Cardiac Devices where previous trial information, real patient data and closed-loop device models are effectively used to evaluate the trial with high confidence. We formulate the CACT in the context of RIGHT using a Bayesian statistical framework. We define a hierarchical model of the virtual cohort generated from a physiological model which captures the uncertainty in the parameters and allow for the systematic incorporation of information available at the design of the trial. With this formulation, the estimates the inappropriate therapy rate of Vitality 2 compared to Medtronic as 33.22% vs 15.62% $(\mathrm{p}\lt 0.001)$, which is comparable to the original trial. Finally, we relate the outcomes of the computer- aided clinical trial to the primary endpoint of RIGHT.

Data-Driven Model Predictive Control with Regression Trees—An Application to Building Energy Management

Model Predictive Control (MPC) plays an important role in optimizing operations of complex cyber-physical systems because of its ability to forecast system’s behavior and act under system level constraints. However, MPC requires reasonably accurate underlying models of the system. In many applications, such as building control for energy management, Demand Response, or peak power reduction, obtaining a high-fidelity physics-based model is cost and time prohibitive, thus limiting the widespread adoption of MPC. To this end, we propose a data-driven control algorithm for MPC that relies only on the historical data. We use multi-output regression trees to represent the system’s dynamics over multiple future time steps and formulate a finite receding horizon control problem that can be solved in real-time in closed-loop with the physical plant. We apply this algorithm to peak power reduction in buildings to optimally trade-off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics.

In-silico pre-clinical trials for implantable cardioverter defibrillators

Regulatory authorities require that the safety and efficacy of a new high-risk medical device be proven in a Clinical Trial (CT), in which the effects of the device on a group of patients are compared to the effects of the current standard of care. Phase III trials can run for several years, cost millions of dollars, and expose patients to an unproven device. In this paper, we demonstrate how to use a large group of synthetic patients based on computer modeling to improve the planning of a CT so as to increase the chances of a successful trial for implantable cardioverter defibrillators (ICDs). We developed a computer model of the electrical generation and propagation in the heart. This model was used to generate a large group of heart instances capable of producing episodes of 19 different arrhythmias. We also implemented two arrhythmia detection algorithms from the literature: Rhythm ID from Boston Scientific and PR Logic + Wavelet from Medtronic. Using this setup, we conducted multiple in-silico trials to compare the ability of the two algorithms to appropriately discriminate between potentially fatal Ventricular Tachy-arrhythmias (VT) and nonfatal Supra-Ventricular Tachy-arrhythmias (SVTs). The results of our in-silico trial indicate that Rhythm ID was less able to discriminate between SVT and VT and so may lead to more cases of inappropriate therapy. This corroborates the findings of the Rhythm ID Going Head to Head Trial (RIGHT), a clinical trial that compared the two algorithms in patients. We further demonstrated that the result continues to hold if we vary the distribution of arrhythmias in the synthetic population. We also used the same in-silico cohort to explore the sensitivity of the outcome to different parameter settings of the device algorithms, which is not feasible in a real clinical trial. In-silico trials can provide early insight into the factors which affect the outcome of a CT at a fraction of the cost and duration and without the ethical issues.

Model-Driven Safety Analysis of Closed-Loop Medical Systems

In modern hospitals, patients are treated using a wide array of medical devices that are increasingly interacting with each other over the network, thus offering a perfect example of a cyber-physical system. We study the safety of a medical device system for the physiologic closed-loop control of drug infusion. The main contribution of the paper is the verification approach for the safety properties of closed-loop medical device systems. We demonstrate, using a case study, that the approach can be applied to a system of clinical importance. Our method combines simulation-based analysis of a detailed model of the system that contains continuous patient dynamics with model checking of a more abstract timed automata model. We show that the relationship between the two models preserves the crucial aspect of the timing behavior that ensures the conservativeness of the safety analysis. We also describe system design that can provide open-loop safety under network failure.

Modeling cardiac pacemaker malfunctions with the Virtual Heart Model

Implantable cardiac devices such as artificial pacemakers deliver therapies according to the timing information from the heart. Such devices work under the assumptions of perfect sensing, which are: (a) the pacemaker leads remain in place, and (b) the pacing therapy in one chamber (e.g. atrium) is insulated from the other chambers (e.g. ventricles). But there are common cases which violate these assumptions and the mechanisms for imperfect sensing cannot be captured by a simple signal generator. In this paper we use the Penn Virtual Heart Model (VHM) to investigate the spatial and temporal aspects of the electrical conduction system of the heart in a closed-loop with a pacemaker model. We utilize the spatial properties of the heart to model the sensing mechanism, and use clinical cases to show the validity of our sensing model. Such closed-loop evaluation of the pacemaker operation allows for functional testing of pacemaker software, the development of new algorithms for rhythm therapy and also serves as a tool for incoming cardiac electrophysiology fellows.

Using the Virtual Heart Model to validate the mode-switch pacemaker operation

Artificial pacemakers are one of the most widely-used implantable devices today, with millions implanted worldwide. The main purpose of an artificial pacemaker is to treat bradycardia, or slow heart beats, by pacing the atrium and ventricles at a faster rate. While the basic functionality of the device is fairly simple, there are many documented cases of death and injury due to device malfunctions. The frequency of malfunctions due to firmware problems will only increase as the pacemaker operations become more complex in an attempt to expand the use of the device. One reason these malfunctions arise is that there is currently no methodology for formal validation and verification of medical device software, as there are in the safety-critical domains of avionics and industrial control automation. We have developed a timed-automata based Virtual Heart Model (VHM) to act as platform for medical device software validation and verification. Through a case study involving multiple arrhythmias, this investigation shows how the VHM can be used with closed-loop operation of a pacemaker to validate the necessity and functionality of the complex mode-switch pacemaker operation. We demonstrate the correct pacemaker operation, to switch from one rhythm management mode to another, in patients with supraventricular tachycardias. (1).