Abstract
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.
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