Universal coordinate on wave-shape manifold of cardiovascular waveform signal for dynamic quantification and cross-subject comparison

Objective Quantifying physiological dynamics from nonstationary time series for clinical decision-making is challenging, especially when comparing data across different subjects. We propose a solution and validate it using two real-world surgical databases, focusing on underutilized arterial blood pressure (ABP) signals. Method We apply a manifold learning algorithm, Dynamic Diffusion Maps (DDMap), combined with the novel Universal Coordinate (UC) algorithm to quantify dynamics from nonstationary time series. The method is demonstrated using ABP signal and validated with liver transplant and cardiovascular surgery databases, both containing clinical outcomes. Sensitivity analyses were conducted to assess robustness and identify optimal parameters. Results: : UC application is validated by significant correlations between the derived index and clinical outcomes. Sensitivity analyses confirm the algorithm’s stability and help optimize parameters. Conclusions DDMap combined with UC enables dynamic quantification of ABP signals and comparison across subjects. This technique repurposes typically discarded ABP signals in the operating room, with potential applications to other nonstationary biomedical signals in both hospital and homecare settings. Clinical and Impact The proposed manifold learning algorithm enables dynamic quantification of typically discarded ABP signals in the operation room that is comparable across subjects for clinical decision making.