Abbas/Rodionova/Bartocci/Smolka/Grosu.

Quantitative Regular Expressions for Arrhythmia Detection Algorithms.*

H. Abbas, A. Rodionova, E. Bartocci, S.A. Smolka, and R. Grosu.

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.

In Proc. of CMSB'17, the 15th Conference on Computational Methods in Systems Biology, Darmstadt, Germany, September, 2017. Springer, LNCS/LNBI.

* This work was partially supported by the AFOSR FA9550-14-1-0261, NSF-Frontiers Cyber-Physical Heart Award, FWF-NFN RiSE Award, FWF-DC LMCS Award, FFG Harmonia Award, FFG Em2Apps Award, and the TUW CPPS-DK Award.