Automated beat onset and peak detection algorithm for field-collected photoplethysmograms

Recent reports suggest that photoplethysmo-graphy (PPG), which is a component of routine pulse oximetry, may be useful for detecting hypovolemia. An essential step in extracting and analyzing common PPG features is the robust identification of onset and peak locations of the vascular beats, despite varying beat morphologies and major oscillations in the baseline. Some prior reports used manual analysis of the PPG waveform; however, for systematic widespread use, an automated method is required. In this paper, we report an algorithm that automatically detects beat onsets and peaks from noisy field-collected PPG waveforms. We validated the algorithm by clinician evaluation of 100 randomly selected PPG waveform samples. For 99% of the beats, the algorithm was able to credibly identify the onsets and peaks of vascular beats, although the precise locations were ambiguous, given the very noisy data from actual clinical operations. The algorithm appears promising, and future consideration of its diagnostic capabilities and limitations is warranted.