Dynamic cerebral autoregulation assessment using an ARX model: comparative study using step response and phase shift analysis

Liu, Y.; Birch, Alexander A.; Allen, Robert

doi:10.1016/s1350-4533(03)00015-8

Cited by 36 publications

(38 citation statements)

References 14 publications

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“…The physiologically-based models can be contrasted with statistical black-box approaches. 7,29 This group of methods includes the autoregulation index (ARI), 36 autoregressive-moving average (ARMA), autoregressive exogenous (ARX), 18,19 and transfer function analysis. 11,30,46 Although most of these methods can be used to analyze clinical data, they are rather limited in explaining physiological mechanisms underlying CA.…”

Section: Introductionmentioning

confidence: 99%

Modeling Cerebral Blood Flow Velocity During Orthostatic Stress

2014

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Cerebral autoregulation refers to the physiological process that maintains stable cerebral blood flow (CBF) during changes in arterial blood pressure (ABP). In this study, we propose a simple, nonlinear quantitative model with only four parameters that can predict CBF velocity as a function of ABP. The model was motivated by the viscoelastic-like behavior observed in the data collected during postural change from sitting to standing. Qualitative testing of the model involved analysis of dynamic responses to step-changes in pressure both within and outside the autoregulatory range, while quantitative testing was used to show that the model can fit dynamics observed in data measured from a healthy young and a healthy elderly subject. The latter involved analysis of structural and practical identifiability, sensitivity analysis, and parameter estimation. Results showed that the model is able to reproduce observed overshoot and adaptation and predict the different responses in the healthy young and the healthy elderly subject. For the healthy young subject, the overshoot was significantly more pronounced than for the elderly subject, but the recovery time was longer for the young subject. These differences resulted in different parameter values estimated using the two datasets.

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Section: Introductionmentioning

confidence: 99%

Modeling Cerebral Blood Flow Velocity During Orthostatic Stress

2014

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“…The response to this input will be denominated as the pressure-pulseresponse (PPR). Figure 2B and 3A show the expected left-shift (phase lead) in the PPR [7] resulting from the high-pass characteristics of the autoregulatory response [5,14]. Results from preliminary work [13] based on simulations using Tiecks model [15] and recorded data showed that the PPR at 1.5 seconds (A1.5) and the amplitude at 7 seconds (A7) (as shown in Fig.…”

Section: Selection Of Autoregulatory Parametersmentioning

confidence: 87%

“…Examples of such autoregulatory parameters include gain, phase and coherence in selected frequency ranges [5,7,8,14], or alternatively features of the step-response -e.g. slopes or amplitudes at selected points [9,14]. An alternative method is provided by the autoregulatory index ARI [15]: this varies from 0 (absent CA) to 9 (excellent CA), with each corresponding to a specific linear filter.…”

Section: Introductionmentioning

confidence: 99%

“…[19]). The majority of published studies have considered one or two measures of CA for the analysis of cerebral blood flow control from spontaneous variations [10,14], and no single parameter has become accepted as a gold standard. To date, it is not clear which combination of model and parameter for the assessment of CA is the most appropriate for future research and clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Optimising the assessment of cerebral autoregulation from black box models

Angarita-Jaimes

Kouchakpour

Liu

et al. 2014

Medical Engineering & Physics

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Cerebral autoregulation (CA) mechanisms maintain blood flow approximately stable despite changes in arterial blood pressure. Mathematical models that characterise this system have been used extensively in the quantitative assessment of function/impairment of CA. Using spontaneous fluctuations in arterial blood pressure (ABP) as input and cerebral blood flow velocity (CBFV) as output, the autoregulatory mechanism can be modelled using linear and nonlinear approaches, from which indexes can be extracted to provide an overall assessment of CA. Previous studies have considered a single -or at most a couple of measures, making it difficult to compare the performance of different CA parameters. We compare the performance of established autoregulatory parameters and propose novel measures. The key objective is to identify which model and index can best distinguish between normal and impaired CA. To this end twenty-six recordings of ABP and CBFV from normocapnia and hypercapnia (which temporarily impairs CA) in 13 healthy adults were analysed. In the absence of a 'gold' standard for the study of dynamic CA, lower inter-and intra-subject variability of the parameters in relation to the difference between normo-and hypercapnia were considered as criteria for identifying improved measures of CA. Significantly improved performance compared to some conventional approaches was achieved, with the simplest method emerging as probably the most promising for future studies.

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“…ARX modeling was the subject of studies in several fields such as chemical engineering, [26,27] agriculture and biological science, [28,29] medicine, [30] energy and the power and [31] energy economics. [32] The ARX needs the determination of the model orders, the time delay.…”

Section: Arx Modelingmentioning

confidence: 99%

Using ARX and NARX approaches for modeling and prediction of the process behavior: application to a reactor‐exchanger

Chetouani

2008

Asia-Pacific J Chem Eng

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Chemical industries are characterized often by nonlinear processes. Therefore, it is often difficult to obtain nonlinear models that accurately describe a plant in all regimes. The main contribution of this work is to establish a reliable model of a process behavior. The use of this model should reflect the normal behavior of the process and allow distinguishing it from an abnormal one. Consequently, the black-box identification based on the neural network (NN) approach by means of a nonlinear autoregressive with exogenous input (NARX) model has been chosen in this study. A comparison with an autoregressive with exogenous input (ARX) model based on the least squares criterion is carried out. This study also shows the choice and the performance of ARX and NARX models in the training and test phases. Statistical criteria are used for the validation of the experimental data of these approaches. The identified neural model is implemented by training a multilayer perceptron artificial neural network (MLP-ANN) with input-output experimental data. An analysis of the inputs number, hidden neurons and their influence on the behavior of the neural predictor is carried out. In order to illustrate the proposed ideas, a reactor-exchanger is used. Satisfactory agreement between identified and experimental data is found and results show that the neural model predicts the evolution of the process dynamics in a better way. 

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Dynamic cerebral autoregulation assessment using an ARX model: comparative study using step response and phase shift analysis

Cited by 36 publications

References 14 publications

Modeling Cerebral Blood Flow Velocity During Orthostatic Stress

Modeling Cerebral Blood Flow Velocity During Orthostatic Stress

Optimising the assessment of cerebral autoregulation from black box models

Using ARX and NARX approaches for modeling and prediction of the process behavior: application to a reactor‐exchanger

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