12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Nonlinear statistical modeling and model discov...
View graph of relations

« Back

Nonlinear statistical modeling and model discovery for cardiorespiratory data.

Research output: Contribution to journalJournal article

Published

Journal publication date08/2005
JournalPhysical Review E
Journal number2
Volume72
Pages021905
Original languageEnglish

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.