Award Abstract # 1446664
CPS: Frontier: Collaborative Research: Compositional, Approximate, and Quantitative Reasoning for Medical Cyber-Physical Systems

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA, THE
Initial Amendment Date: April 29, 2015
Latest Amendment Date: September 13, 2018
Award Number: 1446664
Award Instrument: Continuing Grant
Program Manager: Ralph Wachter
rwachter@nsf.gov
 (703)292-8950
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: May 1, 2015
End Date: April 30, 2021 (Estimated)
Total Intended Award Amount: $940,000.00
Total Awarded Amount to Date: $940,000.00
Funds Obligated to Date: FY 2015 = $178,505.00
FY 2016 = $183,700.00

FY 2017 = $188,405.00

FY 2018 = $389,390.00
History of Investigator:
  • Rahul Mangharam (Principal Investigator)
    rahulm@seas.upenn.edu
  • Sanjay Dixit (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Pennsylvania
3451 WALNUT ST STE 440A
PHILADELPHIA
PA  US  19104-6205
(215)898-7293
Sponsor Congressional District: 03
Primary Place of Performance: University of Pennsylvania
PA  US  19104-6391
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): GM1XX56LEP58
Parent UEI: GM1XX56LEP58
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01001516DB NSF RESEARCH & RELATED ACTIVIT
01001617DB NSF RESEARCH & RELATED ACTIVIT

01001718DB NSF RESEARCH & RELATED ACTIVIT

01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7918, 8236
Program Element Code(s): 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project represents a cross-disciplinary collaborative research effort on developing rigorous, closed-loop approaches for designing, simulating, and verifying medical devices. The work will open fundamental new approaches for radically accelerating the pace of medical device innovation, especially in the sphere of cardiac-device design. Specific attention will be devoted to developing advanced formal methods-based approaches for analyzing controller designs for safety and effectiveness; and devising methods for expediting regulatory and other third-party reviews of device designs. The project team includes members with research backgrounds in computer science, electrical engineering, biophysics, and cardiology; the PIs will use a coordinated approach that balances theoretical, experimental and practical concerns to yield results that are intended to transform the practice of device design while also facilitating the translation of new cardiac therapies into practice.

The proposed effort will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behaviors match designers' intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The scientific work being proposed will serve this vision by providing mathematically robust techniques for analyzing and verifying the behavior of medical devices, for modeling and simulating heart dynamics, and for conducting closed-loop verification of proposed therapeutic approaches.

The acceleration in medical device innovation achievable as a result of the proposed research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly. It will also yield a collection of tools and techniques that will be applicable in the design of other types of devices. Finally, it will contribute to the development of human resources and the further inclusion of under-represented groups via its extensive education and outreach programs, including intensive workshop experiences for undergraduates.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 40)
Abbas, H., Jang K.J., Liang J., Dixit S., and Mangharam R "A novel ICD morphology discriminator to improve discrimination between Ventricular and Supraventricular tachycardias" Heart Rhythm Society Scientific Sessions , 2017
Abbas, Houssam and O'Kelly, Matthew and Mangharam, Rahul "Relaxed Decidability and the Robust Semantics of Metric Temporal Logic" Proceedings of the 20th International Conference on Hybrid Systems: Computation and Control , 2017 , p.217 10.1145/3049797.3049813
H. Abbas and R. Mangharam "Generalized Robust MTL Semantics for Problems in Cardiac Electrophysiology" American Control Conference , 2018
H. Abbas, A. Rodionova, E. Bartocci, S. Smolka and R. Grosu "Quantitative Regular Expressions for Arrhythmia Detection Algorithms" Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science , v.10545 , 2017 , p.23 10.1007/978-3-319-67471-1_2
H. Abbas, K. Mamouras, A. Rodionova, R. Alur, J. Liang, S. Dixit, and R. Mangharam "A novel programming language to reduce energy consumption by arrhythmia monitoring algorithms in implantable cardioverter-defibrillators" Heart Rhythm Society, Scientific Sessions , 2018
H. Abbas, Z. Jiang, K. J. Jang, M. Beccani, J. Liang and R. Mangharam "High-level modeling for computer-aided clinical trials of medical devices" IEEE International High Level Design Validation and Test Workshop (HLDVT) , 2016 , p.85 10.1109/HLDVT.2016.7748260
Houssam Abbas, Konstantinos Mamouras, Alena Rodionova, Alur Rajeev, Jackson Liang, Sanjay Dixit, Rahul Mangharam "A novel programming language to reduce energy consumption by arrhythmia monitoring algorithms in implantable cardioverter-defibrillators" American Heart Rhythm Symposium , 2018
Houssam Abbas, Kuk Jin Jang and Rahul Mangharam "Nonlinear Hybrid Automata Model of Excitable Cardiac Tissue" ARCH 3rd International Workshop on Applied Verification for Continuous and Hybrid Systems , 2017 https://doi.org/10.29007/5zfk
Houssam Abbas, Kuk Jin Jang, and Rahul Mangharam "Benchmark: Nonlinear Hybrid Automata Model of Excitable Cardiac Tissue" Applied Verification for Continuous and Hybrid Systems, Cyber-Physical Systems Week , v.1 , 2016
Houssam Abbas, Kuk Jin Jang, and Rahul Mangharam "Nonlinear Hybrid Automata Model of Excitable Cardiac Tissue" Applied Verification for Continuous and Hybrid Systems , v.3 , 2016 , p.1 http://repository.upenn.edu/mlab_papers/90/
Houssam Abbas, Kuk Jin Jang, Zhihao Jiang, and Rahul Mangharam "Towards Model Checking of Implantable Cardioverter Defibrillators" 19th ACM International Conference on Hybrid Systems: Computation and Control , v.19 , 2016 , p.87-92 10.1145/2883817.2883841
(Showing: 1 - 10 of 40)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Derangements in the heart rhythm are called cardiac arrhythmias which affect millions of people worldwide - these can be fatal and result in chronic health conditions. The most effective treatment for fatal arrhythmias such as ventricular tachycardia are with implantable cardiac devices that prevent the onset and save patients’ lives. For chronic heart arrhythmias such as atrial fibrillation, the most effective treatment is surgical catheter ablation which restores correct rhythm. In both cases, there is considerable risk of incorrect diagnosis by the closed-loop device that result in over-shocking the patient or by the physician in incorrectly ablading the heart’s tissue resulting in permanent adverse damage to the patient. This project focused on developing integrated functional and formal models of the patient’s heart, the implantable medical devices and the tissue ablation therapy which alters the heart rhythm to ensure High-confidence Medical Cyber-Physical Software and Systems.
 
The Intellectual Merit of this project was realized through the development and deployment of:
1. Computer-aided Clinical Trials: Clinical trials are expensive, take 6-10 years, often fail and are put patients at risk. To expedite this process this project developed a statistical framework to complement traditional clinical trials with computer models and simulations of the patient and devices which could be used as regulatory-grade evidence. By generating 10,000s virtual patients demonstrated “In-silico Pre-Clinical Trials” are effective and robust in improving the success, reducing the cost and scope of the actual trial.
2. Patient-specific Heart Models for Surgical Guidance Systems: We developed a patient-specific computational heart models that can accurately reproduce the activation patterns to help in localizing atrial fibrillation triggers and sources. Our models have high spatial resolution, with whole-atrium temporal synchronous activity, and has the potential to help the physician with more reliable surgical devision making. 
3. Platforms for Closed-loop Medical CPS: We developed interactive tools for training cardiac electro physiologists to evaluate how different device algorithm settings influence the heart operation. This allows for tighter interaction between the physician and the anti-arrhythmia algorithm developer. We also developed tools for test and automation of implantable cardiac devices to verify operation across multiple devices for virtual clinical trials. 
  
Broader Impact
The project resulted in 33 peer-reviewed publication, 16 invited talks, 1 patent. The project involved 26 students (3 Postdocs, 5 PhD students, 4 MS students, 12 undergrads, 2 high school students). The students graduated to take positions as Tenure-track Assistant Professors in Oregon State University, University of Waterloo, University of Virginia, Rice University. Students continued to pursue PhDs at UIUC, Johns Hopkins University, NC State University, and Stanford University. Other students joined industry with jobs at Siemens Healthcare, GE Healthcare, Abbott, Qualcomm, Nvidia, Google X, Google/Waymo, and Intel Labs. 

 


Last Modified: 03/19/2022
Modified by: Rahul Mangharam

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