Regular Expressions for Irregular Rhythms

Motivated by the desire to verify the correctness of algorithms for arrhythmia discrimination used in cardiac medical devices, we present a general wavelet-based characterization of peaks (local maxima and minima) that occur in cardiac electrograms, along with two peak-detection algorithms based on this characterization. Peak detection (PD) is a common signal-processing task, as peaks indicate events of interest, such as heartbeats (in the cardiac setting). The performance of PD thereby directly influences the correctness of the algorithms that depend on its output. We show that our wavelet-based PD algorithms (peakWPM and peakWPB) and a commercial PD algorithm from Medtronic Inc. (peakMDT) are easily expressible in Quantitative Regular Expressions (QREs), a formal language based on regular expressions for specifying complex numerical queries over data streams. We then study the accuracy and sensitivity of the resulting QRE-based PD algorithms on real patient data, and show that the wavelet-based peakWPM algorithm outperforms the other two PD algorithms, yielding results that are on par with those provided by a cardiologist.