Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia

Abstract

ObjectiveClassic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment.MethodsContinuous recording of CBFV, ABP, ECG, and end-tidal CO2 were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time—frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02–0.07 Hz (VLF), 0.07–0.20 Hz (LF), and 0.20–0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed.ResultsThe hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF.ConclusionThe time—frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.

Wavelet pressure reactivity index: a validation study

“…; Panerai, ), there is motivation to perform more sophisticated analyses to quantify the coupling mechanism of ABP and CBF (Placek et al . ). The wavelet transform method is a powerful mathematical tool for analysing intermittent, noisy and non‐stationary signals, such as measured in studies of the autonomic nervous system (Pichot et al .…”

“…With the clarification of the non-stationarity and non-linearity of CA (Panerai et al 1999;Panerai, 2014), there is motivation to perform more sophisticated analyses to quantify the coupling mechanism of ABP and CBF (Placek et al 2017). The wavelet transform method is a powerful mathematical tool for analysing intermittent, noisy and non-stationary signals, such as measured in studies of the autonomic nervous system (Pichot et al 1999;Addison, 2002;Davrath et al 2003;Keissar et al 2009), making it also a natural candidate for assessing cerebral pressure reactivity (Bishop et al 2012;Garg et al 2014;Tian et al 2016;Wszedybyl-Winklewska et al 2017).…”

Cerebrovascular Dynamics During Continuous Motor Task

“…With TF estimates we used strict linear modeling to assess the relationship between BP and CBFV. This kept nonlinear and nonstationary aspects of the system unconsidered (Castro et al 2017, Giller and Mueller 2003, Placek et al 2017. All nonlinear approaches showed that the coefficient of variance as a sign of the models quality or analog measures become smaller compared to the strict linear models indicating that these models were likely more precise.…”

“…However, in terms phase, gain or impulse response as measure of comparison such models did not exhibit large differences from linear differential equations models (Kouchakpour et al 2014, Marmarelis et al 2014, Meel-van den Abeelen et al 2014, Panerai 1999a, Smirl et al 2015, Panerai et al 2001. Regarding non-stationarity present in the data, recordings over time periods of several minutes can average out non-stationary effects with the result that time-variant models and time-invariant models produce close results (Kouchakpour et al 2010, Marmarelis et al 2014, Nikolic et al 2015, Placek et al 2017.…”

Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus

“…Parameters derived from the wavelet transform of ICP signals, like wavelet entropy (WE) and wavelet turbulence (WT), have been also used to study signal irregularity and variability in ICP recordings obtained during ITs [24]. Alternative time-frequency representations, such as Zhao-Atlas-Marks distribution, have been used to analyse cerebral autoregulation in healthy subjects during hypercapnia [25].…”