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.

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Objective. Mean intracranial pressure (ICP) is commonly used in the management of patients with intracranial pathologies. However, the shape of the ICP signal over a single cardiac cycle, called ICP pulse waveform, also contains information on the state of the craniospinal space. In this study we aimed to propose an end-to-end approach to classification of ICP waveforms and assess its potential clinical applicability. Methods. ICP pulse waveforms obtained from long-term ICP recordings of 50 neurointensive care unit (NICU) patients were manually classified into four classes ranging from normal to pathological. An additional class was introduced to simultaneously identify artifacts. Several deep learning models and data representations were evaluated. An independent testing dataset was used to assess the performance of final models. Occurrence of different waveform types was compared with the patients' clinical outcome. Results. Residual Neural Network using 1-D ICP signal as input was identified as the best performing model with accuracy of 93% in the validation and 82% in the testing dataset. Patients with unfavorable outcome exhibited significantly lower incidence of normal waveforms compared to the favorable outcome group even at ICP levels below 20 mm Hg (median [first-third quartile]: 9 [1-36] % vs. 63 [52-88] %, p=0.002).Conclusions. Results of this study confirm the possibility of analyzing ICP pulse waveform morphology in long-term recordings of NICU patients. Proposed approach could potentially be used to provide additional information on the state of patients with intracranial pathologies beyond mean ICP.

Our results indicated that lower values of HRV indices and BRS correlate with mortality and that there is a link between cerebral dysautoregulation and the analysed estimates of the ANS in aSAH patients.

Our results suggest that the pulsatile flow forward model better reflects changes in CrCP and in τ induced by controlled alterations in EtCO.

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The shape of the pulse waveforms of intracranial pressure (ICP) and cerebral blood flow velocity (CBFV) typically contains three characteristic peaks. It was reported that alterations in cerebral hemodynamics may influence the shape of the pulse waveforms by changing peaks’ configuration. However, the changes in peak appearance time (PAT) in ICP and CBFV pulses are only described superficially. We analyzed retrospectively ICP and CBFV signals recorded in traumatic brain injury patients during decrease in ICP induced by hypocapnia (n = 11) and rise in ICP during episodes of ICP plateau waves (n = 8). All three peaks were manually annotated in over 48 thousand individual pulses. The changes in PAT were compared between periods of vasoconstriction (expected during hypocapnia) and vasodilation (expected during ICP plateau waves) and their corresponding baselines. Correlation coefficient (rS) analysis between mean ICP and mean PATs was performed in each individual recording. Vasodilation prolonged PAT of the first peaks of ICP and CBFV pulses and the third peak of CBFV pulse. It also accelerated PAT of the third peak of ICP pulse. In contrast, vasoconstriction shortened appearance time of the first peaks of ICP and CBFV pulses and the second peak of ICP pulses. Analysis of individual recordings demonstrated positive association between changes in PAT of all three peaks in the CBFV pulse and mean ICP (rS range: 0.32–0.79 for significant correlations). Further study is needed to test whether PAT of the CBFV pulse may serve as an indicator of changes in ICP–this may open a perspective for non-invasive monitoring of alterations in mean ICP.