Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals

Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Author(s): Jonathan Martinez, Kaan Sel, Bobak J. Mortazavi and Roozbeh Jafari

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% on average for fiducial point identification and mean percent difference by 16% on average for IBI estimation.

Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients’ daily life.

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