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Quantitative Regular Expressions for Arrhythmia Detection Algorithms

  • Houssam AbbasEmail author
  • Alena Rodionova
  • Ezio Bartocci
  • Scott A. Smolka
  • Radu Grosu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10545)

Abstract

Motivated by the problem of verifying the correctness of arrhythmia-detection algorithms, we present a formalization of these algorithms in the language of Quantitative Regular Expressions. QREs are a flexible formal language for specifying complex numerical queries over data streams, with provable runtime and memory consumption guarantees. The medical-device algorithms of interest include peak detection (where a peak in a cardiac signal indicates a heartbeat) and various discriminators, each of which uses a feature of the cardiac signal to distinguish fatal from non-fatal arrhythmias. Expressing these algorithms’ desired output in current temporal logics, and implementing them via monitor synthesis, is cumbersome, error-prone, computationally expensive, and sometimes infeasible.

In contrast, we show that a range of peak detectors (in both the time and wavelet domains) and various discriminators at the heart of today’s arrhythmia-detection devices are easily expressible in QREs. The fact that one formalism (QREs) is used to describe the desired end-to-end operation of an arrhythmia detector opens the way to formal analysis and rigorous testing of these detectors’ correctness and performance. Such analysis could alleviate the regulatory burden on device developers when modifying their algorithms. The performance of the peak-detection QREs is demonstrated by running them on real patient data, on which they yield results on par with those provided by a cardiologist.

Keywords

Peak Detection Electrocardiograms Arrythmia discrimination ICDs Quantitative Regular Expressions 

Notes

Acknowledgments

The authors would like to thank Konstantinos Mamouras for insightful discussions about QREs and for providing the QRE Java library we used in this paper. This work is supported in part by AFOSR Grant FA9550-14-1-0261 and NSF Grants IIS-1447549, CNS-1446832, CNS-1446664, CNS-1445770, and CNS-1445770, and Austrian National Research Network grants S 11405-N23 and S 11412-N23 (RiSE/SHiNE of the FWF) and the ICT COST Action IC1402 Runtime Verification beyond Monitoring.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Houssam Abbas
    • 1
    Email author
  • Alena Rodionova
    • 2
  • Ezio Bartocci
    • 2
  • Scott A. Smolka
    • 3
  • Radu Grosu
    • 2
  1. 1.Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Cyber-Physical Systems GroupTechnische Universität WienViennaAustria
  3. 3.Department of Computer ScienceStony Brook UniversityStony BrookUSA

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