Multi-lead wavelet-based ECG delineation on a wearable embedded sensor platform

This work is dedicated to the sensible optimization and porting of a multi-lead (ML) wavelet-transform (WT)-based electrocardiogram (ECG) wave delineator to a state-of-the-art commercial wearable embedded sensor platform with limited processing and storage resources. The original offline algorithm was recently proposed and validated in the literature, as an extension of an earlier well-established single-lead (SL) WT-based ECG delineator. Several ML ECG delineation approaches, including SL selection according to various criteria and lead combination into a single root-mean-squared (RMS) curve, are carefully optimized for real-time operation on a state-of-the-art commercial wearable embedded sensor platform. Furthermore, these ML ECG delineation approaches are contrasted in terms of their delineation accuracy, complexity and memory usage, as well as suitability for ambulatory real-time operation. Finally, the robustness and stability of the ML ECG delineation approaches are benchmarked with respect to a validated SL implementation.