Nonlinear statistical modeling and model discovery for cardiorespiratory data.
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.
Luchinsky
D G
DG
Newstead Mission Critical Technologies, Inc., 9100 Wilshire Boulevard, Suite 540, East Beverly Hills, California 90212-3437, USA.
Millonas
M M
MM
Smelyanskiy
V N
VN
Pershakova
A
A
Stefanovska
A
A
McClintock
P V E
PV
D000465:Algorithms; D001683:Biological Clocks / Q000502:physiology; D001794:Blood Pressure / Q000502:physiology*; D001795:Blood Pressure Determination / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D009991:Oscillometry / Q000379:methods*; D011673:Pulsatile Flow / Q000502:physiology; D015656:Respiratory Mechanics / Q000502:physiology*