Mangharam, Rahul
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Introduction
Rahul's focus is on the development of life-critical and safety-critical real-time embedded systems in medical devices, automotive electronics, wireless control networks and energy-efficient buildings. He directs mLAB::Real-Time and Embedded Systems Lab, which develops software verification and testing techniques for high-confidence medical devices such as implantable cardiac pacemakers; new designs for automotive electronic controllers to develop programmable vehicles of the future; distributed wireless control networks for closed-loop industrial automation, and new scheduling/control algorithms for energy-efficient buildings.
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Publication Event-based Green Scheduling of Radiant Systems in Buildings(2013-03-01) Nghiem, Truong X; Pappas, George; Mangharam, RahulThis paper looks at the problem of peak power demand reduction for intermittent operation of radiant systems in buildings. Uncoordinated operation of the circulation pumps of a multi-zone hydronic radiant system can cause temporally correlated electricity demand surges when multiple pumps are activated simultaneously. Under a demand-based electricity pricing policy, this uncoordinated behavior can result in high electricity costs and expensive system operation. We have previously presented Green Scheduling with the periodic scheduling approach for reducing the peak power demand of electric radiant heating systems while maintaining indoor thermal comfort. This paper develops an event-based state feedback scheduling strategy that, unlike periodic scheduling, directly takes into account the disturbances and is thus more suitable for building systems. The effectiveness of the new strategy is demonstrated through simulation in MATLAB.Publication Model-Based Conformance Testing for Implantable Pacemakers(2013-07-31) Chen, George; Jiang, Zhihao; Mangharam, RahulPublication Heart-on-a-Chip: A Closed-loop Testing Platform for Implantable Pacemakers(2014-07-02) Jiang, Zhihao; Radhakrishnan, Sriram; Sampath, Varun; Sarode, Shilpa; Mangharam, RahulImplantable cardiac pacemakers restore normal heart rhythm by delivering external electrical pacing to the heart. The pacemaker software is life-critical as the timing of the pulses determine its ability to control the heart rate. Recalls due to software issues have been on the rise with the increasing complexity of pacing algorithms. Open-loop testing remains the primary approach to evaluate the safety of pacemaker software. While this tests how the pacemaker responds to stimulus, it cannot reveal pacemaker malfunctions which drive the heart into an unsafe state over multiple cycles. To evaluate the safety and efficacy of pacemaker software we have developed a heart model to generate different heart conditions and interact with real pacemakers. In this paper, we introduce the closed-loop testing platform which consists of a programmable hardware implementation of the heart that can interact with a commercial pacemaker in closed-loop. The heart-on-a-chip implementation is automatically generated from the Virtual Heart Model in Simulink which models different heart conditions. We describe a case study of Endless Loop Tachycardia to demonstrate potential closed-loop pacemaker malfunctions which inappropriately increase the heart rate. The test platform is part of our model-based design framework for verification and testing of medical devices with the patient--in-the-loop.Publication Electroanatomic Mapping to Determine Scar Regions in Patients with Atrial Fibrillation(2019-04-24) He, Jiyue; Jang, Kuk Jin; Walsh, Katie; Mangharam, Rahul; Liang, Jackson; Dixit, SanjayLeft 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.Publication Temporal Logic Robustness for General Signal Classes(2019-04-15) Abbas, Houssam; Pant, Yash Vardhan; Mangharam, RahulIn multi-agent systems, robots transmit their planned trajectories to each other or to a central controller, and each receiver plans its own actions by maximizing a measure of mission satisfaction. For missions expressed in temporal logic, the robustness function plays the role of satisfaction measure. Currently, a Piece-Wise Linear (PWL) or piece-wise constant reconstruction is used at the receiver. This allows an efficient robustness computation algorithm - a.k.a. monitoring - but is not adaptive to the signal class of interest, and does not leverage the compression properties of more general representations. When communication capacity is at a premium, this is a serious bottleneck. In this paper we first show that the robustness computation is significantly affected by how the continuous-time signal is reconstructed from the received samples, which can mean the difference between a successful control and a crash. We show that monitoring general spline-based reconstructions yields a smaller robustness error, and that it can be done with the same time complexity as monitoring the simpler PWL reconstructions. Thus robustness computation can now be adapted to the signal class of interest. We further show that the monitoring error is tightly upper-bounded by the L ∞ signal reconstruction error. We present a (non-linear) L ∞ -based scheme which yields even lower monitoring error than the spline-based schemes (which have the advantage of being faster to compute), and illustrate all results on two case studies. As an application of these results, we show how time-frequency specifications can be efficiently monitored online.Publication Real-Time Traffic Congestion Prediction(2008-11-01) Mangharam, Rahul; Lee, Insup; Sokolsky, OlegPublication Towards Synthesis of Platform-Aware Attack-Resilient Control Systems: Extended Abstract(2013-04-09) Pajic, Miroslav; Bezzo, Nicola; Weimer, James; Alur, Rajeev; Mangharam, Rahul; Michael, Nathan; Pappas, George J; Sokolsky, Oleg; Tabuada, Paulo; Weirich, Stephanie; Lee, InsupPublication Real-time Heart Model for Implantable Cardiac Device Validation and Verification(2010-01-20) Jiang, Zhihao; Pajic, Miroslav; Connolly, Allison T; Dixit, Sanjay; Mangharam, RahulDesigning bug-free medical device software is dif- ficult, especially in complex implantable devices that may be used in unanticipated contexts. Safety recalls of pacemakers and implantable cardioverter defibrillators due to firmware problems between 1990 and 2000 affected over 200,000 devices, comprising 41% of the devices recalled and are increasing in frequency. There is currently no formal methodology or open experimental platform to validate and verify the correct operation of medical device software. To this effect, a real-time Virtual Heart Model (VHM) has been developed to model the electrophysiological operation of the functioning (i.e. during normal sinus rhythm) and malfunctioning (i.e. during arrhythmia) heart. We present a methodology to extract timing properties of the heart to construct a timed-automata model. The platform exposes functional and formal interfaces for validation and verification of implantable cardiac devices. We demonstrate the VHM is capable of generating clinically-relevant response to intrinsic (i.e. premature stimuli) and external (i.e. artificial pacemaker) signals for a variety of common arrhythmias. By connecting the VHM with a pacemaker model, we are able to pace and synchronize the heart during the onset of irregular heart rhythms. The VHM has also been implemented on a hardware platform for closed-loop experimentation with existing and virtual medical devices. The VHM allows for exploratory electrophysiology studies for physicians to evaluate their diagnosis and determine the appropriate device therapy. This integrated functional and formal device design approach will potentially help expedite medical device certification for safer operation.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 Evaluation of DR-Advisor on the ASHRAE Great Energy Predictor Shootout Challenge(2014-07-24) Behl, Madhur; Mangharam, RahulThis paper describes the evaluation of DR-Advisor algorithms on ''The Great Energy Predictor Shootout - The First Building Data Analysis and Prediction Competition'' held in 1993-94 by ASHRAE.