Document Detail

Prediction of mean arterial blood pressure with linear stochastic models.
MedLine Citation:
PMID:  22254409     Owner:  NLM     Status:  MEDLINE    
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.
Sahika Genc
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2011     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2011  
Date Detail:
Created Date:  2012-01-18     Completed Date:  2012-06-07     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  712-5     Citation Subset:  IM    
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MeSH Terms
Blood Pressure*
Blood Pressure Determination / methods*
Computer Simulation
Heart / innervation,  physiopathology*
Linear Models*
Models, Cardiovascular*
Models, Statistical
Sympathetic Nervous System / physiopathology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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