Document Detail

Assessing the blood volume and heart rate responses during haemodialysis in fluid overloaded patients using support vector regression.
MedLine Citation:
PMID:  19812455     Owner:  NLM     Status:  In-Process    
This study aims to assess the blood volume and heart rate (HR) responses during haemodialysis in fluid overloaded patients by a nonparametric nonlinear regression approach based on a support vector machine (SVM). Relative blood volume (RBV) and electrocardiogram (ECG) was recorded from 23 haemodynamically stable renal failure patients during regular haemodialysis. Modelling was performed on 18 fluid overloaded patients (fluid removal of >2 L). SVM-based regression was used to obtain the models of RBV change with time as well as the percentage change in HR with respect to RBV. Mean squared error (MSE) and goodness of fit (R(2)) were used for comparison among different kernel functions. The design parameters were estimated using a grid search approach and the selected models were validated by a k-fold cross-validation technique. For the model of HR versus RBV change, a radial basis function (RBF) kernel (MSE = 17.37 and R(2) = 0.932) gave the least MSE compared to linear (MSE = 25.97 and R(2) = 0.898) and polynomial (MSE = 18.18 and R(2)= 0.929). The MSE was significantly lower for training data set when using RBF kernel compared to other kernels (p < 0.01). The RBF kernel also provided a slightly better fit of RBV change with time (MSE = 1.12 and R(2) = 0.91) compared to a linear kernel (MSE = 1.46 and R(2) = 0.88). The modelled HR response was characterized by an initial drop and a subsequent rise during progressive reduction in RBV, which may be interpreted as the reflex response to a transition from central hypervolaemia to hypovolaemia. These modelled curves can be used as references to a controller that can be designed to regulate the haemodynamic variables to ensure the stability of patients undergoing haemodialysis.
Faizan Javed; Andrey V Savkin; Gregory S H Chan; Paul M Middleton; Philip Malouf; Elizabeth Steel; James Mackie; Nigel H Lovell
Related Documents :
24646145 - The effects of prior learned strategies on updating an opponent's strategy in the rock,...
24308445 - A metapopulation model for highly pathogenic avian influenza: implications for compartm...
25036815 - On the lower predictive bound approach for non-inferiority clinical trials with binary ...
23034765 - Elastic network models: theoretical and empirical foundations.
14530455 - Standardized determination of real-time pcr efficiency from a single reaction set-up.
17464425 - Dimensionality of the premenstrual syndrome: confirmatory factor analysis of premenstru...
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-10-08
Journal Detail:
Title:  Physiological measurement     Volume:  30     ISSN:  1361-6579     ISO Abbreviation:  Physiol Meas     Publication Date:  2009 Nov 
Date Detail:
Created Date:  2009-10-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  England    
Other Details:
Languages:  eng     Pagination:  1251-66     Citation Subset:  IM    
School of Electrical Engineering & Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

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

Previous Document:  Automated identification of peristaltic pressure waves in oesophageal manometry investigations using...
Next Document:  Application and comparison of dynamic models to assess impact of loading variations on performance o...