Assessing the blood volume and heart rate responses during haemodialysis in fluid overloaded patients using support vector regression
| Autor: | Paul M. Middleton, Philip Malouf, Nigel H. Lovell, Andrey V. Savkin, James D. Mackie, Faizan Javed, Gregory S. H. Chan, Elizabeth Steel |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Mean squared error
Physiology Vectorcardiography Water-Electrolyte Imbalance Biomedical Engineering Biophysics Blood Pressure Blood volume Goodness of fit Heart Rate Renal Dialysis Physiology (medical) Statistics Humans Aged Mathematics Aged 80 and over Blood Volume Nonparametric statistics Middle Aged Body Fluids Support vector machine Kernel (statistics) Radial basis function kernel Fluid Therapy Kidney Failure Chronic Nonlinear regression |
| Zdroj: | Physiological Measurement. 30:1251-1266 |
| ISSN: | 1361-6579 0967-3334 |
| DOI: | 10.1088/0967-3334/30/11/009 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|