Change in Blood Flow Velocity Pulse Waveform during Plateau Waves of Intracranial Pressure.

A reliable method for non-invasive detection of dangerous intracranial pressure (ICP) elevations is still unavailable. In this preliminary study, we investigate quantitatively our observation that superimposing waveforms of transcranial Doppler blood flow velocity (FV) and arterial blood pressure (ABP) may help in non-invasive identification of ICP plateau waves. Recordings of FV, ABP and ICP in 160 patients with severe head injury (treated in the Neurocritical Care Unit at Addenbrookes Hospital, Cambridge, UK) were reviewed retrospectively. From that cohort, we identified 18 plateau waves registered in eight patients. A "measure of dissimilarity" (Dissimilarity/Difference Index, DI) between ABP and FV waveforms was calculated in three following steps: 1. fragmentation of ABP and FV signal according to cardiac cycle; 2. obtaining the normalised representative ABP and FV cycles; and finally; 3. assessing their difference, represented by the area between both curves. DI appeared to discriminate ICP plateau waves from baseline episodes slightly better than conventional pulsatility index did: area under ROC curve 0.92 vs. 0.90, sensitivity 0.81 vs. 0.69, accuracy 0.88 vs. 0.84, respectively. The concept of DI, if further tested and improved, might be used for non-invasive detection of ICP plateau waves

Can interhemispheric desynchronization of cerebral blood flow anticipate upcoming vasospasm in aneurysmal subarachnoid haemorrhage patients?

BACKGROUND: Asymmetry of cerebral autoregulation (CA) was demonstrated in patients after aneurysmal subarachnoid haemorrhage (aSAH). A classical method for CA assessment requires simultaneous measurement of both arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). In this study, we have proposed a cerebral blood flow asymmetry index based only on CBFV and analysed its association with the occurrence of vasospasm after aSAH. NEW METHOD: The phase shifts (PS) between slow oscillations in left and right CBFV (side-to-side PS) and between ABP and CBFV (CBFV-ABP PS) were estimated using multichannel matching pursuit (MMP) and cross-spectral analysis. RESULTS: We retrospectively analysed data collected from 45 aSAH patients (26 with vasospasm). Data were analysed up to 7th day after aSAH unless the vasospasm was detected earlier. A progressive asymmetry, manifested by a gradual increase in side-to-side PS on consecutive days after aSAH, was observed in patients who developed vasospasm (Radj2 = 0.14, p = 0.009). In these patients, early side-to-side PS was more positive than in patients without vasospasm (2.8° ± 5.6° vs -1.7° ± 5.7°, p = 0.011). No such a difference was found in CBFV-ABP PS. Patients with positive side-to-side PS were more likely to develop vasospasm than patients with negative side-to-side PS (21/7 vs 5/12, p = 0.0047). COMPARISON WITH EXISTING METHOD: MMP, in contrast to the spectral approach, accounts for non-stationarity of analysed signals. MMP applied to the PS estimation reflects the cerebral blood flow asymmetry in aSAH better than the spectral analysis. CONCLUSIONS: Changes in side-to-side PS might be helpful to identify patients who are at risk of vasospasm

Using the entropy of the corneal pulse signal to distinguish healthy eyes from eyes affected by primary open-angle glaucoma.

OBJECTIVE: The purpose of this study was to evaluate whether the complexity of the corneal pulse (CP) signal can be used to differentiate patients with primary open-angle glaucoma (POAG) from healthy subjects. APPROACH: The study sample consisted of 28 patients with POAG and a control, age-matched group of 30 subjects. After standard ophthalmic examination, the CP signal from a randomly selected eye of each participant was measured using non-contact ultrasonic micro-displacement measurement technology. After pre-processing, the complexity of the CP signal was estimated using refined composite multiscale fuzzy entropy (RCMFE) up to scale factor 50. The average RCMFE values were computed from three repeated measurements of the CP signals for each participant and each scale factor. MAIN RESULTS: The complexity of the CP signal in glaucomatous eyes was higher than that observed in healthy ones. Also, RCMFE of the CP signal was found to differentiate (statistically significantly) between the two groups for scales in the range from 26 to 43. For these scales, the one for which the lowest p-value (t-test, p = 0.017) was obtained when comparing RCMFE between the two groups was selected as the optimal scale. Next, a receiver operating characteristic analysis for the optimal scale showed that the proposed approach of calculating the multiscale entropy of the CP signal has some potential to discriminate between patients with POAG and healthy controls (sensitivity, specificity and accuracy of 0.643, 0.700 and 0.672, respectively). SIGNIFICANCE: In conclusion, RCMFE, as a complexity measure, may be considered an auxiliary indicator to support glaucoma diagnostics

Intracranial pulse pressure waveform analysis using the higher harmonics centroid.

Funder: National Health Research Institutes; doi: http://dx.doi.org/10.13039/501100004737BACKGROUND: The pulse waveform of intracranial pressure (ICP) is its distinctive feature almost always present in the clinical recordings. In most cases, it changes proportionally to rising ICP, and observation of these changes may be clinically useful. We introduce the higher harmonics centroid (HHC) which can be defined as the center of mass of harmonics of the ICP pulse waveform from the 2nd to 10th, where mass corresponds to amplitudes of these harmonics. We investigate the changes in HHC during ICP monitoring, including isolated episodes of ICP plateau waves. MATERIAL AND METHODS: Recordings from 325 patients treated between 2002 and 2010 were reviewed. Twenty-six patients with ICP plateau waves were identified. In the first step, the correlation between HHC and ICP was examined for the entire monitoring period. In the second step, the above relation was calculated separately for periods of elevated ICP during plateau wave and the baseline. RESULTS: For the values averaged over the whole monitoring period, ICP (22.3 ± 6.9 mm Hg) correlates significantly (R = 0.45, p = 0.022) with HHC (3.64 ± 0.46). During the ICP plateau waves (ICP increased from 20.9 ± 6.0 to 53.7 ± 9.7 mm Hg, p < 10-16), we found a significant decrease in HHC (from 3.65 ± 0.48 to 3.21 ± 0.33, p = 10-5). CONCLUSIONS: The good correlation between HHC and ICP supports the clinical application of pressure waveform analysis in addition to the recording of ICP number only. Mean ICP may be distorted by a zero drift, but HHC remains immune to this error. Further research is required to test whether a decline in HHC with elevated ICP can be an early warning sign of intracranial hypertension, whether individual breakpoints of correlation between ICP and its centroid are of clinical importance

Compliance of the cerebrospinal space: comparison of three methods

Abstract: Background: Cerebrospinal compliance describes the ability of the cerebrospinal space to buffer changes in volume. Diminished compliance is associated with increased risk of potentially threatening increases in intracranial pressure (ICP) when changes in cerebrospinal volume occur. However, despite various methods of estimation proposed so far, compliance is seldom used in clinical practice. This study aimed to compare three measures of cerebrospinal compliance. Methods: ICP recordings from 36 normal-pressure hydrocephalus patients who underwent infusion tests with parallel recording of transcranial Doppler blood flow velocity were retrospectively analysed. Three methods were used to calculate compliance estimates during changes in the mean ICP induced by infusion of fluid into the cerebrospinal fluid space: (a) based on Marmarou’s model of cerebrospinal fluid dynamics (CCSF), (b) based on the evaluation of changes in cerebral arterial blood volume (CCaBV), and (c) based on the amplitudes of peaks P1 and P2 of ICP pulse waveform (CP1/P2). Results: Increase in ICP caused a significant decrease in all compliance estimates (p < 0.0001). Time courses of compliance estimators were strongly positively correlated with each other (group-averaged Spearman correlation coefficients: 0.94 [0.88–0.97] for CCSF vs. CCaBV, 0.77 [0.63–0.91] for CCSF vs. CP1/P2, and 0.68 [0.48–0.91] for CCaBV vs. CP1/P2). Conclusions: Indirect methods, CCaBV and CP1/P2, allow for the assessment of relative changes in cerebrospinal compliance and produce results exhibiting good correlation with the direct method of volumetric manipulation. This opens the possibility of monitoring relative changes in compliance continuously

Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia.

OBJECTIVE: Classic 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. METHODS: Continuous 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. RESULTS: The 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. CONCLUSION: The 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

Python-Embedded Plugin Implementation in ICM+: Novel Tools for Neuromonitoring Time Series Analysis with Examples Using CENTER-TBI Datasets.

With the appearance of publicly available, high-resolution, physiological datasets in neurocritical care, like Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), there is a growing need for tools that could be used by clinical researchers to interrogate this information-rich data. The ICM+ software is widely used for processing data acquired from bedside monitors. Considering the growing popularity of scripting simple-syntax programming languages like Python, particularly among clinical researchers, we have developed an interface in ICM+ that provides a streamlined way of adding Python scripting functionality to the ICM+ calculation engine. The new interface imposes certain requirements on the scripts and needs an accompanying descriptor file that tells ICM+ about the functions implemented, so that they become available to the end user in the same way as native ICM+ functions. ICM+ also now includes a tool that eases the creation of Python functions to be imported. The Python extension works very efficiently, and any user with some degree of experience in scripting can use it to enrich capabilities of ICM+. Depending on the data analysed and calculations performed, Python functions are 15-60% slower than built-in ICM+ functions, which is a more-than-acceptable trade-off for empowering ICM+ with the unlimited analytical freedom offered by extensive Python libraries

Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia

Baroreflex is a mechanism of short-term neural control responsible for maintaining stable levels of arterial blood pressure (ABP) in an ABP-heart rate negative feedback loop. Its function is assessed by baroreflex sensitivity (BRS)—a parameter which quantifies the relationship between changes in ABP and corresponding changes in heart rate (HR). The effect of postural change as well as the effect of changes in blood O2 and CO2 have been the focus of multiple previous studies on BRS. However, little is known about the influence of the combination of these two factors on dynamic baroreflex response. Furthermore, classical methods used for BRS assessment are based on the assumption of stationarity that may lead to unreliable results in the case of mostly nonstationary cardiovascular signals. Therefore, we aimed to investigate BRS during repeated transitions between squatting and standing in normal end-tidal CO2 (EtCO2) conditions (normocapnia) and conditions of progressively increasing EtCO2 with a decreasing level of O2 (hypercapnia with hypoxia) using joint time and frequency domain (TF) approach to BRS estimation that overcomes the limitation of classical methods. Noninvasive continuous measurements of ABP and EtCO2 were conducted in a group of 40 healthy young volunteers. The time course of BRS was estimated from TF representations of pulse interval variability and systolic pressure variability, their coherence, and phase spectra. The relationship between time-variant BRS and indices of ABP and HR was analyzed during postural change in normocapnia and hypercapnia with hypoxia. In normocapnia, observed trends in all measures were in accordance with previous studies, supporting the validity of presented TF method. Similar but slightly attenuated response to postural change was observed in hypercapnia with hypoxia. Our results show the merits of the nonstationary methods as a tool to study the cardiovascular system during short-term hemodynamic changes

Comparison of changes normocapnia vs. hypercapnia in the values of measured physiological signals.

<p>Comparison of changes normocapnia vs. hypercapnia in the values of measured physiological signals.</p

Comparison of the values of selected metrics in normocapnia and hypercapnia in particular frequency ranges.

<p>VLF– 0.02–0.07 Hz, LF– 0.07–0.20 Hz, HF– 0.20–0.35 Hz. Outliers defined as values lower than Q1–1.5∙IQR or greater than Q3 + 1.5∙IQR are marked by ‘+’. On each box, the central line is the median, the edges are Q1 and Q3. The whiskers indicate the most extreme data points excluding outliers. Horizontal lines indicate comparison by means of Wilcoxon signed-rank test and corresponding <i>p</i>-values are denoted (^~–<i>p</i> < 0.05, ^–<i>p</i> < 0.01, *–<i>p</i> < 0.001). (a) Average time-frequency coherence, (b) average time-frequency phase shift, (c) average spectral coherence, (d) spectral phase shift estimated at maximal value of spectral coherence within corresponding frequency range.</p