Generalized transfer function estimation using evolutionary spectral deblurring

1999 ◽  
Vol 47 (8) ◽  
pp. 2335-2339 ◽  
Author(s):  
S.I. Shah ◽  
L.F. Chaparro ◽  
A. El-Jaroudi
Keyword(s):  
Transfer Function ◽  
Function Estimation ◽  
Generalized Transfer Function

Effect of non-invasive calibration of radial waveforms on error in transfer-function-derived central aortic waveform characteristics

2004 ◽  
Vol 107 (2) ◽  
pp. 205-211 ◽  
Author(s):  
Sarah A. HOPE ◽  
Ian T. MEREDITH ◽  
James D. CAMERON
Keyword(s):  
Transfer Function ◽  
Augmentation Index ◽  
Systolic Pressure ◽  
Function Estimation ◽  
Non Invasive ◽  
Clinical Interest ◽  
Time Parameters ◽  
Generalized Transfer Function ◽  
The Impact ◽  
Significant Underestimation

Transfer function techniques are increasingly used for non-invasive estimation of central aortic waveform characteristics. Non-invasive radial waveforms must be calibrated for this purpose. Most validation studies have used invasive pressures for calibration, with little data on the impact of non-invasive calibration on transfer-function-derived aortic waveform characteristics. In the present study, simultaneous invasive central aortic (Millar Mikro-tip® catheter transducer) and non-invasive radial (Millar® Mikro-tip® tonometer) pressure waveforms and non-invasive brachial pressures (Dinamap®) were measured in 42 subjects. In this cohort, radial waveforms were calibrated to both invasive and non-invasive mean and diastolic pressures. From each of these, central waveforms were reconstructed using a generalized transfer function obtained by us from a previous cohort [Hope, Tay, Meredith and Cameron (2002) Am. J. Physiol. Heart Circ. Physiol. 283, H1150–H1156]. Waveforms were analysed for parameters of potential clinical interest. For calibrated radial and reconstructed central waveforms, different methods of calibration were associated with differences in pressure (P<0.001), but not time parameters or augmentation index. Whereas invasive calibration resulted in little error in transfer function estimation of central systolic pressure (difference −1±8 mmHg; P=not significant), non-invasive calibration resulted in significant underestimation (7±12 mmHg; P<0.001). Errors in estimated aortic parameters differed with non-invasively calibrated untransformed radial and transfer-function-derived aortic waveforms (all P<0.01), with smaller absolute errors with untransformed radial waveforms for most pressure parameters [systolic pressure, 5±16 and 7±12 mmHg; pulse pressure, 0±16 and 4±12 mmHg (radial and derived aortic respectively)]. When only non-invasive pressures are accessible, analysis of untransformed radial waveforms apparently produces smaller errors in the estimation of central aortic systolic pressure, and other waveform parameters, than using a generalized transfer function.

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Multiscale mathematical modeling vs. the generalized transfer function approach for aortic pressure estimation: a comparison with invasive data

2018 ◽  
Vol 42 (5) ◽  
pp. 690-698 ◽  
Author(s):  
Andrea Guala ◽  
Francesco Tosello ◽  
Dario Leone ◽  
Luca Sabia ◽  
Fabrizio D’Ascenzo ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Transfer Function ◽  
Aortic Pressure ◽  
Function Approach ◽  
Pressure Estimation ◽  
Generalized Transfer Function
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