Key result
The global pulse signature derived from the synchrosqueezing transform of radial pulse signals accurately distinguished patients with congestive heart failure from normal controls, achieving an area under the curve of 1 at the right hand chi position.
Cross-Sectional (n=36)
No
Effect estimate: AUC 1 (95% CI 0.97-1)
p-value: p=<0.0001
A novel signal processing approach using wave-shape functions and synchrosqueezing transform shows potential for extracting hemodynamic features from standard pulse wave signals.
Requires prospective validation before clinical use; leaves open whether this approach extends to broader HF populations or real-world settings.
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.
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Wu et al. (2016) conducted a cross-sectional in Congestive heart failure (n=36). Global pulse signature (GPS) analysis via synchrosqueezing transform vs. Normal controls was evaluated on Detection of congestive heart failure using GPS from the right hand chi position (AUC 1, 95% CI 0.97-1, p=<0.0001). The global pulse signature derived from the synchrosqueezing transform of radial pulse signals accurately distinguished patients with congestive heart failure from normal controls, achieving an area under the curve of 1 at the right hand chi position.
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