Related Experiment Video
Updated: Aug 15, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
Published on: June 27, 2025
Nonlinear statistical modeling and model discovery for cardiorespiratory data
D G Luchinsky1, M M Millonas, V N Smelyanskiy
1Newstead Mission Critical Technologies, Inc., 9100 Wilshire Boulevard, Suite 540, East Beverly Hills, California 90212-3437, USA.
Abstract:
We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.
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