  Goal: To achieve-high quality comprehensive feature extraction from
physiological signals that enables precise physiological parameter estimation
despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW,
a probabilistic framework that leverages dynamic time warping (DTW) and minimal
domain-specific heuristics to simultaneously segment physiological signals and
identify fiducial points that represent cardiac events. An automated dynamic
template adapts to evolving waveform morphologies. We validate
Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies
include subject- and respiratory-induced variation. Results: Boosted-SpringDTW
achieves precision, recall, and F1-scores over 0.96 for identifying fiducial
points and mean absolute error values less than 11.41 milliseconds when
estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to
two baseline feature extraction algorithms by 35 percent on average for
fiducial point identification and mean percent difference by 16 percent on
average for IBI estimation. Significance: Precise hemodynamic parameter
estimation with wearable devices enables continuous health monitoring
throughout a patients' daily life.
