Towards real-time QRS feature extraction for wearable monitors.

The ability to generate computationally compact ECG analysis algorithms is of interest in the field of wearable physiologic monitors. Such remote monitors necessarily have limited on-board energy storage and therefore lack the computational power and physical memory often required for academic study of physiologic waveforms. Herein we evaluate a set of algorithms with markedly different computation and memory footprints useful in extracting QRS complexes from synthetically generated noisy and measured ECG signals. A small memory and computational footprint Short Time Fourier Transform ECG analysis algorithm is demonstrated to have similar sensitivity and specificity to a more complex but highly accurate Stockwell Transform.