@conference {, title = {Cybercardia project: Modeling, verification and validation of implantable cardiac devices}, booktitle = {2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, author = {Islam, Md Ariful and Lim, Hyunkyung and Paoletti, Nicola and Abbas, Houssam and Jiang, Zhihao and Cyranka, Jacek and Cleaveland, Rance and Gao, Sicun and Clarke, Edmund and Grosu, Radu and others} } @conference {, title = {High-level modeling for computer-aided clinical trials of medical devices}, booktitle = {High Level Design Validation and Test Workshop (HLDVT), 2016 IEEE International}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Medical devices like the Implantable Cardioverter Defibrillator (ICD) are life-critical systems. Malfunctions of the device can cause serious injury or death of the patient. In addition to rigorous testing and verification during the development process, new medical devices often go through clinical trials to evaluate their safety and performance on sample populations. Clinical trials are costly and prone to failure if not planned and executed properly. Evaluating devices on computer models of the relevant physiological systems can provide helpful insights into the safety and efficacy of the device, thus helping to plan and execute a clinical trial. In this paper, we demonstrate how to develop high-level physiological models of cardiac electrophysiology and how to apply them to the Rhythm ID Head to Head Trial (RIGHT), a 5-year long clinical trial for comparing two ICDs. We refer to this as a Computer-Aided Clinical Trial (CACT). We explored two modeling options, a white-box model capturing the mechanisms of the physiological behaviors, and a blackbox model which uses machine learning methods to synthesize physiological input signals. Both models were able to generate physiological inputs to the ICDs and we discuss the challenges and appropriateness of the two modeling options.

}, author = {Abbas, Houssam and Jiang, Zhihao and Jang, Kuk Jin and Beccani, Marco and Liangy, Jackson and Mangharam, Rahul} } @conference {, title = {In-silico pre-clinical trials for implantable cardioverter defibrillators}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, abstract = {

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.

}, author = {Jiang, Zhihao and Abbas, Houssam and Jang, Kuk Jin and Beccani, Marco and Liang, Jackson and Dixit, Sanjay and Mangharam, Rahul} } @conference {, title = {Towards Model Checking of Implantable Cardioverter Defibrillators}, booktitle = {Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control}, year = {2016}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Ventricular Fibrillation is a disorganized electrical excitation of the heart that results in inadequate blood flow to the body. It usually ends in death within a minute. A common way to treat the symptoms of fibrillation is to implant a medical device, known as an Implantable Cardioverter Defibrillator (ICD), in the patient{\textquoteright}s body. Model-based verification can supply rigorous proofs of safety and efficacy. In this paper, we build a hybrid system model of the human heart+ICD closed loop, and show it to be a STORMED system, a class of o-minimal hybrid systems that admit finite bisimulations. In general, it may not be possible to compute the bisimulation. We show that approximate reachability can yield a finite simulation for STORMED systems, and that certain compositions respect the STORMED property. The results of this paper are theoretical and motivate the creation of concrete model checking procedures for STORMED systems.

}, keywords = {heart modeling, icds, life-critical cps, medical devices, stormed systems}, isbn = {978-1-4503-3955-1}, doi = {10.1145/2883817.2883841}, url = {http://doi.acm.org/10.1145/2883817.2883841}, author = {Abbas, Houssam and Jiang, Kuk Jin and Jiang, Zhihao and Mangharam, Rahul} }