Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform

TL;DR

This paper introduces a novel approach combining wave-shape functions and synchrosqueezing transform to model and analyze pulse signals, enabling extraction of health-related hemodynamics features from physiological data.

Contribution

It presents a new adaptive non-harmonic model and analysis method for oscillatory signals, specifically applied to pulse wave analysis for health assessment.

Findings

Effective extraction of spectral pulse signatures

Potential to characterize hemodynamics from pulse signals

Demonstrated application to physiological data

Abstract

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, {and} based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features.