Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals

TL;DR

This paper introduces Boosted-SpringDTW, a probabilistic framework that enhances physiological signal feature extraction, enabling accurate fiducial point detection and inter-beat interval estimation despite waveform variability.

Contribution

The paper presents a novel Boosted-SpringDTW method that adaptively segments signals and identifies fiducial points with high accuracy, outperforming existing algorithms.

Findings

F1-scores over 0.96 for fiducial point detection

Mean absolute error less than 11.41 ms for IBI estimation

35% average improvement over baseline algorithms

Abstract

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.