A robust Fourier-based method to measure pulse pressure variability. (July 2020) - Acosta, Sebastian; Ahmed, Mubbasheer; Yin, Suellen M.; Brady, Ken M.; Penny, Daniel J.; Rusin, Craig G.
Highlights: A new method, based on nonlinear Fourier analysis, is designed to estimate pulse pressure variability (PPV). Mathematical analysis reveals a spectral constraint that signals with amplitude modulation must satisfy. The applicability of the method is tested using synthetic data and 100 hours of physiologic data. The proposed method accurately recovers PPV at signal-to-noise ratios six times smaller than the traditional method. Potential use of PPV to recognize fluid responsiveness during low tidal volume ventilation or spontaneous breathing. Abstract: Objective: To propose a new method to estimate pulse pressure variability (PPV) in the arterial blood pressure waveform. Methods: Traditional techniques of calculating PPV using peak finding have a fundamental flaw that prevents them from accurately resolving PPV for small tidal volumes, limiting the use of PPV to only mechanical ventilated patients. The improved method described here addresses this limitation using Fourier analysis of an oscillatory signal that exhibits a time-varying modulation of its amplitude. The analysis reveals a constraint on the spectral representation that must be satisfied for any oscillatory signal that exhibits a time-varying modulation of its amplitude. This intrinsic mathematical structure is taken advantage of in order to improve the robustness of the algorithm. Results: The applicability of the method is tested using synthetic data and 100 h of physiologic data collected from patients admitted to Texas Children's Hospital. Significance and conclusion: The proposed method accurately recovers values of PPV at signal-to-noise ratios six times smaller than the traditional method. This is a significant advance for the potential use of PPV to recognize fluid responsiveness during low tidal volume ventilation or spontaneous breathing for which the signal-to-noise ratio is expected to be small.