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Abstract-The assessment of dynamic cerebral autoregulation response using changes in arterial blood pressure (ABP) as a stimulus is increasingly used. Transcranial Doppler ultrasonography measurements of middle cerebral artery velocity (MCAv) are often used in conjunction with ABP measurements using photoplethysmography (e.g. Finapres) to assess the response of the autoregulation mechanism. Two linear models of dynamic cerebral autoregulation have been developed independently. The first is an ARX model using the least-squares algorithm to fit the ABP and MCAv signals. The second is a flow dependent feedback mechanism controlling the pressure gradient across the MCA. Both models have been found to reproduce qualitatively similar results to those recorded in both thigh cuff and lower body negative pressure experiments, whereas the first model has also been used to analyse MCAv simulated using Ursino's physiological model. This paper assesses the ability of the two models to reproduce MCAv measurements from recordings of ABP from the same experiments.Keywords-Cerebral autoregulation, System identification, Physiological simulation, Modeling I. INTRODUCTION Cerebral autoregulation, the active changes in arterial and arteriolar diameters, allows cerebral blood flow (CBF) to be maintained despite changes in cerebral perfusion pressure. Both arterial blood pressure (ABP) and middle cerebral arterial flow velocity (MCAv) can be measured noninvasively using photoplethysmography (e.g. Finapres) and transcranial Doppler ultrasonography (TCD) respectively. Recent studies show that the mean MCAv variability is largely due to the spontaneous changes in ABP, providing the other physiological conditions are in a steady state, e.g. arterial pCO 2 is constantMany types of experiment have been developed to manipulate the ABP in order to assess dynamic autoregulation, such as the thigh cuff technique and carotid artery compression to create step changes in ABP together with controlled breathing, squat-standing and lower body negative pressure (LBNP) experiments that induce slow oscillatory variations in ABP [3]. Data from both the thigh cuff and LBNP techniques are used in this paper to investigate the variability in ABP and MCAv.Many different approaches have been adopted to establish a quantitative relationship between ABP and MCAv [4], [5]. In this paper, two different linear mathematical models of dynamic autoregulation are presented.The first approach is ARX modeling. The ARX model is constructed using the least-squares algorithm to fit MCAv data by the simultaneous ABP data. Besides collecting MCAv from TCD, MCAv is also simulated by a physiological model. The multi-compartmental physiological model developed by Ursino and his colleagues [6] is used to simulate a controllable cerebral circulation system, i.e. we can change the cerebral autoregulation by adjusting some parameters of the physiological model. This physiological model simulation showed a good correspondency with real MCAv [7]. The step response of ARX models c...

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