title: Rapid pressure-to-flow dynamics of cerebral autoregulation induced by instantaneous changes of arterial CO2.
abstract: Continuous assessment of CA is desirable in a number of clinical conditions, where cerebral hemodynamics may change within relatively short periods. In this work, we propose a novel method that can improve temporal resolution when assessing the pressure-to-flow dynamics in the presence of rapid changes in arterial CO2. A time-varying multivariate model is proposed to adaptively suppress the instantaneous effect of CO2 on CBFV by the recursive least square (RLS) method. Autoregulation is then quantified from the phase difference (PD) between arterial blood pressure (ABP) and CBFV by calculating the instantaneous PD between the signals using the Hilbert transform (HT). A Gaussian filter is used prior to HT in order to optimize the temporal and frequency resolution and show the rapid dynamics of cerebral autoregulation. In 13 healthy adult volunteers, rapid changes of arterial CO2 were induced by rebreathing expired air, while simultaneously and continuously recording ABP, CBFV and end-tidal CO2 (ETCO2). Both simulation and physiological studies show that the proposed method can reduce the transient distortion of the instantaneous phase dynamics caused by the effect of CO2 and is faster than our previous method in tracking time-varying autoregulation. The normalized mean square error (NMSE) of the predicted CBFV can be reduced significantly by 38.7% and 37.7% (p<0.001) without and with the Gaussian filter applied, respectively, when compared with the previous univariate model. These findings suggest that the proposed method is suitable to track rapid dynamics of cerebral autoregulation despite the influence of confounding covariates.
authors.affiliation: Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, PR China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China. Electronic address: jia.liu@siat.ac.cn.
authors.forename: Jia
authors.initials: J
authors.lastname: Liu
authors.affiliation: ISVR, University of Southampton, University Rd., Southampton SO17 1BJ, UK.
authors.forename: David M
authors.initials: DM
authors.lastname: Simpson
authors.affiliation: ISVR, University of Southampton, University Rd., Southampton SO17 1BJ, UK.
authors.forename: Hesam
authors.initials: H
authors.lastname: Kouchakpour
authors.affiliation: Department of Cardiovascular Sciences and National Institutes for Health Research (NIHR), Biomedical Research Unit, University of Leicester, Leicester Royal Infirmary, Level 1, Sandringham Building, Leicester LE1 5WW, UK.
authors.forename: Ronney B
authors.initials: RB
authors.lastname: Panerai
authors.affiliation: Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, PR China.
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, PR China.
authors.forename: Shan
authors.initials: S
authors.lastname: Gao
authors.affiliation: Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, PR China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China.
authors.forename: Pandeng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, PR China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Wu
mesh_terms: D000328:Adult; D001794:Blood Pressure / Q000502:physiology*; D002245:Carbon Dioxide / Q000378:metabolism*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D006935:Hypercapnia / Q000503:physiopathology; D016018:Least-Squares Analysis; D008875:Middle Aged; D020768:Middle Cerebral Artery / Q000000981:diagnostic imaging / Q000502:physiology; D008955:Models, Cardiovascular; D008959:Models, Neurological; D015999:Multivariate Analysis; D016011:Normal Distribution; D012680:Sensitivity and Specificity; D017585:Ultrasonography, Doppler, Transcranial; D055815:Young Adult
chemical_list: D002245:Carbon Dioxide
keywords: Adaptive filters; Cerebral hemodynamics; Hilbert transform; Multivariate model