title: Prediction of mean arterial blood pressure with linear stochastic models.
abstract: A model-based approach that integrates known portion of the cardiovascular system and unknown portion through a parameter estimation to predict evolution of the mean arterial pressure is considered. The unknown portion corresponds to the neural portion that acts like a controller that takes corrective actions to regulate the arterial blood pressure at a constant level. The input to the neural part is the arterial pressure and output is the sympathetic nerve activity. In this model, heart rate is considered a proxy for sympathetic nerve activity. The neural portion is modeled as a linear discrete-time system with random coefficients. The performance of the model is tested on a case study of acute hypotensive episodes (AHEs) on PhysioNet data. TPRs and FPRs improve as more data becomes available during estimation period.
authors.affiliation: Sensor Informatics and Technologies Laboratory, Software Sciences and Analytics, General Electric Global Research, Niskayuna, NY 12309, USA. gencs@research.ge.com
authors.forename: Sahika
authors.initials: S
authors.lastname: Genc
mesh_terms: D000818:Animals; D001794:Blood Pressure; D001795:Blood Pressure Determination / Q000379:methods*; D003198:Computer Simulation; D006321:Heart / Q000294:innervation / Q000503:physiopathology*; D006801:Humans; D016014:Linear Models; D008955:Models, Cardiovascular; D015233:Models, Statistical; D013564:Sympathetic Nervous System / Q000503:physiopathology*