Chen, Sanjian
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Publication Towards a Model-Based Meal Detector for Type I Diabetics(2015-04-13) Chen, Sanjian; Weimer, James; Rickels, Michael R.; Peleckis, Amy; Lee, InsupBlood glucose management systems are an important class of Medical Cyber-Physical Systems that provide vital everyday decision support service to diabetics. An artificial pancreas, which integrates a continuous glucose monitor, a wearable insulin pump, and control algorithms running on embedded computing devices, can significantly improve the quality of life for millions of Type 1 diabetics. A primary problem in the development of an artificial pancreas is the accurate detection and estimation of meal carbohydrates, which cause significant glucose system disturbances. Meal carbohydrate detection is challenging since post-meal glucose responses greatly depend on patient-specific physiology and meal composition. In this paper, we develop a novel meal-time detector that leverages a linearized physiological model to realize a (nearly) constant false alarm rate (CFAR) performance despite unknown model parameters and uncertain meal inputs. Insilico evaluations using 10, 000 virtual subjects on an FDA-accepted maximal physiological model illustrate that the proposed CFAR meal detector significantly outperforms a current state-of-the-art meal detector that utilizes a voting scheme based on rate-of-change (RoC) measures. The proposed detector achieves 99.6% correct detection rate while averaging one false alarm every 24 days (a 1.4% false alarm rate), which represents an 84% reduction in false alarms and a 95% reduction in missed alarms when compared to the RoC approach.Publication An Intraoperative Glucose Control Benchmark for Formal Verification(2015-10-01) Chen, Sanjian; O'Kelly, Matthew; Weimer, James; Sokolsky, Oleg; Lee, InsupDiabetes associated complications are affecting an increasingly large population of hospitalized patients. Since glucose physiology is significantly impacted by patient-specific parameters, it is critical to verify that a clinical glucose control protocol is safe across a wide patient population. A safe protocol should not drive the glucose level into dangerous low (hypoglycemia) or high (hyperglycemia) ranges. Verification of glucose controllers is challenging due to the high-dimensional, non-linear glucose physiological models which contain both unobservable states and unmeasurable patient-specific parameters. This paper presents a hybrid system model of a closed-loop physiological system that includes an existing FDA-accepted high-fidelity physiological model tailored to intraoperative settings and a validated improvement to a clinical glucose control protocol for diabetic cardiac surgery patients. We propose the closed-loop model as a physiological system benchmark for verification and present our initial results on verifying the system using the SMT-based hybrid system verification tool dReach.Publication Data Freshness Over-Engineering: Formulation and Results(2018-05-01) Golomb, Dagaen; Gangadharan, Deepak; Chen, Sanjian; Sokolsky, Oleg; Lee, InsupIn many application scenarios, data consumed by real-time tasks are required to meet a maximum age, or freshness, guarantee. In this paper, we consider the end-to-end freshness constraint of data that is passed along a chain of tasks in a uniprocessor setting. We do so with few assumptions regarding the scheduling algorithm used. We present a method for selecting the periods of tasks in chains of length two and three such that the end-to-end freshness requirement is satisfied, and then extend our method to arbitrary chains. We perform evaluations of both methods using parameters from an embedded benchmark suite (E3S) and several schedulers to support our result.Publication GSA: A Framework for Rapid Prototyping of Smart Alarm Systems(2010-11-11) King, Andrew; Roederer, Alex; Arney, David; Chen, Sanjian; Fortino-Mullen, Margaret; Giannareas, Ana; Hanson III, C. William; Kern, Vanessa; Stevens, Nicholas; Viesca Trevino, Adrian; Park, Soojin; Sokolsky, Oleg; Lee, Insup; Tannen, JonathanWe describe the Generic Smart Alarm, an architectural framework for the development of decision support modules for a variety of clinical applications. The need to quickly process patient vital signs and detect patient health events arises in many clinical scenarios, from clinical decision support to tele-health systems to home-care applications. The events detected during monitoring can be used as caregiver alarms, as triggers for further downstream processing or logging, or as discrete inputs to decision support systems or physiological closed-loop applications. We believe that all of these scenarios are similar, and share a common framework of design. In attempting to solve a particular instance of the problem, that of device alarm fatigue due to numerous false alarms, we devised a modular system based around this framework. This modular design allows us to easily customize the framework to address the specific needs of the various applications, and at the same time enables us to perform checking of consistency of the system. In the paper we discuss potential specific clinical applications of a generic smart alarm framework, present the proposed architecture of such a framework, and motivate the benefits of a generic framework for the development of new smart alarm or clinical decision support systems.Publication Parameter-Invariant Design of Medical Alarms(2015-10-01) Weimer, James; Ivanov, Radoslav; Roederer, Alexander; Chen, Sanjian; Lee, InsupThe recent explosion of low-power low-cost communication, sensing, and actuation technologies has ignited the automation of medical diagnostics and care in the form of medical cyber physical systems (MCPS). MCPS are poised to revolutionize patient care by providing smarter alarm systems, clinical decision support, advanced diagnostics, minimally invasive surgical care, improved patient drug delivery, and safety and performance guarantees. With the MCPS revolution emerges a new era in medical alarm systems, where measurements gathered via multiple devices are fused to provide early detection of critical conditions. The alarms generated by these next generation monitors can be exploited by MCPS to further improve performance, reliability, and safety. Currently, there exist several approaches to designing medical monitors ranging from simple sensor thresholding techniques to more complex machine learning approaches. While all the current design approaches have different strengths and weaknesses, their performance degrades when underlying models contain unknown parameters and training data is scarce. Under this scenario, an alternative approach that performs well is the parameter-invariant detector, which utilizes sufficient statistics that are invariant to unknown parameters to achieve a constant false alarm rate across different systems. Parameter-invariant detectors have been successfully applied in other cyber physical systems (CPS) applications with structured dynamics and unknown parameters such as networked systems, smart buildings, and smart grids; most recently, the parameter-invariant approach has been recently extended to medical alarms in the form of a critical shunt detector for infants undergoing a lung lobectomy. The clinical success of this case study application of the parameter-invariant approach is paving the way for a range of other medical monitors. In this tutorial, we present a design methodology for medical parameter-invariant monitors. We begin by providing a motivational review of currently employed medical alarm techniques, followed by the introduction of the parameter-invariant design approach. Finally, we present a case study example to demonstrate the design of a parameter-invariant alarm for critical shunt detection in infants during surgical procedures.