Optimal time-frequency distribution of arterial blood pressure for assessing cerebral autoregulation
关键词: System identification 、 Cerebral autoregulation 、 Wavelet 、 Control system 、 Mathematics 、 Blood flow 、 Remote patient monitoring 、 Time–frequency analysis 、 Signal 、 Control theory
摘要: Cerebral autoregulation is a mechanism that blood flow keeps constantly steady in spite of pressure variability the brain. This has been modeled as control system with ABP input and CBFV output. Linear methods assessing CA suffer from non-linearity non-stationarity, while newly developed methods, wavelet MMPF, have their inherent drawbacks. Wavelet limited by uncertainty principle, whilst Hilbert-Huang transform (HHT) MMPF restricted to mono-component signal. We therefore used time-frequency distribution which can track instantaneous dynamics CA. In this paper, we will be focus on analysis ABP, important terms identification. Three different TFD smoothed pseudo Wigner-Ville (SMWVD), Zhao-Atlas-Marks (ZAMD), Choi-Williams (CWD), are compared show embedded signal signals properly. collected Nexfin monitor eight health volunteers multi-component produced deep breath supervision. Experiment results shows better performance over other qualified optimal distribution.
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