Abbas, Houssam
Email Address
ORCID
Disciplines
relationships.isProjectOf
relationships.isOrgUnitOf
Position
Introduction
Research Interests
Collection
23 results
Search Results
Now showing 1 - 10 of 23
Publication Computer Aided Clinical Trials for Implantable Cardiac Devices(2016-08-19) Abbas, Houssam; Jiang, Zhihao; Jang, Kuk Jin; Beccani, Marco; Liang, Jackson; Dixit, Sanjay; Mangharam, RahulIn this effort we investigate the design and use of physiological and device models to conduct pre-clinical trials to provide early insight in the design and execution of the actual clinical trial. Computer models of physiological phenomena like cardiac electrical activity can be extremely complex. However, when the purpose of the model is to interact with a medical device, then it becomes sufficient to model the measurements that the device makes, e.g. the intra-cardiac electrograms (EGMs) that an Implantable Cardioverter Defibrillator (ICD) measures. We present a probabilistic generative model of EGMs, capable of generating exemplars of various arrhythmias. The model uses deformable shape templates, or motifs, to capture the variability in EGM shapes within one EGM channel, and a cycle length parameter to capture the variability in cycle length in one EGM channel. The relation between EGM channels, which is essential for determining whether the current arrhythmia is potentially fatal, is captured by a time-delayed Markov chain, whose states model the various combinations of (learned) motifs. The heart model is minimally parameterized and is learned from real patient data. Thus the statistics of key features reflect the statistics of a real cohort, but the model can also generate rare cases and new combinations from the inferred probabilities. On the device end, algorithms for signal sensing, detection and discrimination for major ICD manufacturers have been implemented both in simulation and on hardware platforms. The generated arrhythmia episodes are used as input to both the modeled ICD algorithms and real ICDs as part of a Computer Aided Clinical Trial (CACT). In a CACT, a computer model simulates the inputs to the device (such as a new, investigational ICD), and the device’s performance is evaluated. By incorporating these results into the appropriate statistical framework, the Computer Aided Clinical Trial results can serve as regulatory evidence when planning and executing an actual clinical trial. We demonstrate this by conducting a mock trial similar to the 2005-2010 RIGHT trial which compared the discrimination algorithms from two major ICD manufacturers. The results of the CACT clearly demonstrate that the failed outcome of the RIGHT trial could have been predicted and provides statistical support for deeper results that could have been captured prior to the trial.Publication Technical Report: Abstraction-Tree For Closed-loop Model Checking of Medical Devices(2015-05-06) Jiang, Zhihao; Abbas, Houssam; Mosterman, Pieter J; Mangharam, RahulPublication Real-time Decision Policies with Predictable Performance(2018-01-06) Abbas, Houssam; Mamouras, Konstantinos; Alur, Rajeev; Rodionova, Alena; Mangharam, RahulAs methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case performance, on the basis of which hardware can be designed and algorithms can be tuned.Publication Automated Closed-Loop Model Checking of Implantable Pacemakers using Abstraction Trees(2016-03-16) Jiang, Zhihao; Abbas, Houssam; Mosterman, Pieter; Mangharam, RahulAutonomous medical devices such as implantable cardiac pacemakers are capable of diagnosing the patient condition and delivering therapy without human intervention. Their ability to autonomously affect the physiological state of the patient makes them safety-critical. Sufficient evidence for the safety and efficacy of the device software, which makes these autonomous decisions, should be provided before these devices can be released on the market. Formal methods like model checking can provide safety evidence that the devices can safely operate under a large variety of physiological conditions. The challenge is to develop physiological models that are general enough to cover the large variability of human physiology, and also expressive enough to provide physiological contexts to counter-examples returned by the model checker. In this paper, the authors develop a set of physiological abstraction rules that introduce physiological constraints to heart models. By applying these abstraction rules to a initial set of heart models, an abstraction tree is created. The root model covers all possible inputs to a pacemaker and derived models cover inputs from different heart conditions. If a counter-example is returned by the model checker, the abstraction tree is traversed so that the most concrete counter-example(s) with physiological contexts can be returned to the domain experts for validity check. The abstraction tree framework replaces the manual abstraction and refinement framework, which reduced the amount of domain knowledge required to perform closed-loop model checking. It encourages the use of model checking during the development of autonomous medical devices, and identifies safety risks earlier in the design process.Publication Co-Design of Anytime Computation and Robust Control(2015-01-01) Pant, Yash Vardhan; Mohta, Kartik; Abbas, Houssam; Nghiem, Truong; Deveitti, Joesph; Mangharam, RahulPublication Computer-Aided Design for Safe Autonomous Vehicles(2017-05-01) O'Kelly, Matthew; Abbas, Houssam; Mangharam, RahulThis paper details the design of an autonomous vehicle CAD toolchain, which captures formal descriptions of driving scenarios in order to develop a safety case for an autonomous vehicle (AV). Rather than focus on a particular component of the AV, like adaptive cruise control, the toolchain models the end-to-end dynamics of the AV in a formal way suitable for testing and verification. First, a domain-specific language capable of describing the scenarios that occur in the day-to-day operation of an AV is defined. The language allows the description and composition of traffic participants, and the specification of formal correctness requirements. A scenario described in this language is an executable that can be processed by a specification-guided automated test generator (bug hunting), and by an exhaustive reachability tool. The toolchain allows the user to exploit and integrate the strengths of both testing and reachability, in a way not possible when each is run alone. Finally, given a particular execution of the scenario that violates the requirements, a visualization tool can display this counter-example and generate labeled sensor data. The effectiveness of the approach is demonstrated on five autonomous driving scenarios drawn from a collection of 36 scenarios that account for over 95% of accidents nationwide. These case studies demonstrate robustness-guided verification heuristics to reduce analysis time, counterexample visualization for identifying controller bugs in both the discrete decision logic and low-level analog (continuous) dynamics, and identification of modeling errors that lead to unrealistic environment behavior.Publication Technical Report: Control Using the Smooth Robustness of Temporal Logic(2017-03-01) Pant, Yash Vardhan; Abbas, Houssam; Mangharam, RahulCyber-Physical Systems must withstand a wide range of errors, from bugs in their software to attacks on their physical sensors. Given a formal specification of their desired behavior in Metric Temporal Logic (MTL), the robust semantics of the specification provides a notion of system robustness that can be calculated directly on the output behavior of the system, without explicit reference to the various sources or models of the errors. The robustness of the MTL specification has been used both to verify the system offline (via robustness minimization) and to control the system online (to maximize its robustness over some horizon). Unfortunately, the robustness objective function is difficult to work with: it is recursively defined, non-convex and non-differentiable. In this paper, we propose smooth approximations of the robustness. Such approximations are differentiable, thus enabling us to use powerful off-the- shelf gradient descent algorithms for optimizing it. By using them we can also offer guarantees on the performance of the optimization in terms of convergence to minima. We show that the approximation error is bounded to any desired level, and that the approximation can be tuned to the specification. We demonstrate the use of the smooth robustness to control two quad-rotors in an autonomous air traffic control scenario, and for temperature control of a building for comfort.Publication Co-design of Anytime Computation and Robust Control (Supplemental)(2015-05-15) Pant, Yash Vardhan; Mohta, Kartik; Abbas, Houssam; Nghiem, Truong X; Deveitti, Joesph; Mangharam, RahulPublication Benchmark: Nonlinear Hybrid Automata Model of Excitable Cardiac Tissue(2016-03-20) Abbas, Houssam; Jang, Kuk Jin; Mangharam, RahulImplantable cardiac devices like pacemakers and defibrillators are life-saving medical devices. To verify their functionality, there is a need for heart models that can simulate interesting phenomena and are relatively computationally tractable. In this benchmark we implement a model of the electrical activity in excitable cardiac tissue as a network of nonlinear hybrid automata. The model has previously been shown to simulate fast arrhythmias. The hybrid automata are arranged in a square n-by-n grid and communicate via their voltages. Our Matlab implementation allows the user to specify any size of model $n$, thus rendering it ideal for benchmarking purposes since we can study tool efficiency as a function of size. We expect the model to be used to analyze parameter ranges and network connectivity that lead to dangerous heart conditions. It can also be connected to device models for device verification.Publication Computer Aided Clinical Trials for Implantable Cardiac Devices(2018-07-12) Jang, Kuk Jin; Weimer, James; Abbas, Houssam; Jiang, Zhihao; Liang, Jackson; Dixit, Sanjay; Mangharam, RahulIn 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 considerable 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 allows for the systematic incorporation of information available at the design of the trial. With this formulation, the CACT estimates the inappropriate therapy rate of Vitality 2 compared to Medtronic as 33.22% vs 15.62% (p