Quantitative verification of implantable cardiac pacemakers over hybrid heart models

Chen, Taolue; Diciolla, Marco; Kwiatkowska, Marta; Mereacre, Alexandru

doi:10.1016/j.ic.2014.01.014

Cited by 39 publications

(75 citation statements)

References 27 publications

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“…Works in quantitative verification, such as [44] and [45], model the heart and pacemaker to verify statistically or through simulations whether some quantitative properties are satisfied. This contrasts with our approach which is model-free, and provides cost upper bounds based on the QRE code itself, not a model of it.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the implantable pacemaker has been used to illustrate the benefits of model-based design [11], [12], [13]. This involves specifying the algorithms for detecting slower-than-normal rhythms used by pacemakers using formal modeling languages, such as timed automata [14] and hybrid automata [15], and verifying correctness requirements using a model checker such as UPPAAL [16].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Decision Policies With Predictable Performance

et al. 2018

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As 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. Abstract-As 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 per...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Decision Policies With Predictable Performance

et al. 2018

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“…As the complexity of the pacemakers increase, there is a need for the development of better processes for validation of such medical devices. R heart model reported in [3], which was foundational in developing the benchmark 2 Indicate as appropriate: category (academic, industrial), difficulty (low, medium, high, challenge)…”

Section: Context and Originsmentioning

confidence: 99%

“…For analytical purposes, an abstract model based on Timed Automata (TA) consisting of a network of 33 nodes (or cells) has used for testing off-the-shelf pacemakers [6]. A more refined approach that models the continuous voltage using Hybrid Input Output Automata (HIOA) is presented in [3]. In both cases, the abstraction from two trillion cell to 33 nodes is not discussed in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Automata Model of the Heart for Formal Verification of Pacemakers

Andalam¹,

Malik²,

Roop³

et al.

EPiC Series in Computing

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A cardiac model of networked Hybrid Automatas (HAs) cells or nodes is potentially very useful for industry validation of pacemaker algorithms. Such a model replicates the electrical conduction system of the heart and the interaction with a pacemaker. The benchmark is a network of more than 100 HAs. It is based on a published model 1 . It exhibits several key phenomena that are typical for hybrid systems and reachability analysis. Category: academic Difficulty: high 2

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