Continuous detection of cerebral vasodilatation and vasoconstriction using intracranial pulse morphological template matching

A Scoping Review of Cerebral Doppler Arterial Waveforms in Infants

Cerebral Doppler ultrasound has been an important tool in pediatric diagnostics and prognostics for decades. Although the Doppler spectrum can provide detailed information on cerebral perfusion, the measured spectrum is often reduced to simple numerical parameters. To help pediatric clinicians recognize the visual characteristics of disease-associated Doppler spectra and identify possible areas for future research, a scoping review of primary studies on cerebral Doppler arterial waveforms in infants was performed. A systematic search in three online bibliographic databases yielded 4898 unique records. Among these, 179 studies included cerebral Doppler spectra for at least five infants below 1 y of age. The studies describe variations in the cerebral waveforms related to physiological changes (43%), pathology (62%) and medical interventions (40%). Characteristics were typically reported as resistance index (64%), peak systolic velocity (43%) or end-diastolic velocity (39%). Most studies focused on the anterior (59%) and middle (42%) cerebral arteries. Our review highlights the need for a more standardized terminology to describe cerebral velocity waveforms and for precise definitions of Doppler parameters. We provide a list of reporting variables that may facilitate unambiguous reports. Future studies may gain from combining multiple Doppler parameters to use more of the information encoded in the Doppler spectrum, investigating the full spectrum itself and using the possibilities for long-term monitoring with Doppler ultrasound.

Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach

OBJECTIVE

We present a framework for analyzing the morphology of intracranial pressure (ICP). The analysis of ICP signals is challenging due to the non-linear and non-Gaussian characteristics of the signal dynamics, inevitable corruption by noise and artifacts, and variations in ICP pulse morphology among individuals with different neurological conditions. Existing frameworks make unrealistic assumptions regarding ICP dynamics and are not tuned for individual patients.

APPROACH

We propose a dynamic Bayesian network for automated detection of three major ICP pulsatile components. The proposed model captures the non-linear and non-Gaussian dynamics of ICP morphology and further adapts to a patient as the individual's ICP measurements are received. To make the approach more robust, we leverage evidence reversal and present an inference algorithm to obtain the posterior distribution over the locations of pulsatile components.

MAIN RESULTS

We evaluate our approach on a dataset with over 700 h of recordings from 66 neurological patients, where the pulsatile components were annotated by prior studies. The algorithm obtains accuracies of 96.56%, 92.39%, and 94.04% for the detection of each pulsatile component in the test set, showing significant improvement over existing approaches.

SIGNIFICANCE

Continuous ICP monitoring is essential in guiding the treatment of neurological conditions such as traumatic brain injuries. An automated approach for ICP morphology analysis is a step towards enhancing patient care with minimal supervision. Compared to previous methods, our framework offers several advantages. It learns the parameters that model each patient's ICP in an unsupervised manner, resulting in an accurate morphology analysis. The Bayesian model-based framework provides uncertainty estimates and reveals interesting facts about the ICP dynamics. The framework can readily be applied to replace existing morphological analysis methods and support the use of ICP pulse morphological features to aid the monitoring of pathophysiological changes of relevance to the care of patients with acute brain injuries.

Morphological changes of intracranial pressure quantifies vasodilatory effect of verapamil to treat cerebral vasospasm

INTRODUCTION

After aneurysmal subarachnoid hemorrhage (SAH), both proximal and distal cerebral vasospasm can contribute to the development of delayed cerebral ischemia. Intra-arterial (IA) vasodilators are a mainstay of treatment for distal arterial vasospasm, but no methods of assessing the efficacy of interventions in real time have been established.

OBJECTIVE

To introduce a new method for continuous intraprocedural assessment of endovascular treatment for cerebral vasospasm.

METHODS

The premise of our approach was that distal cerebral arterial changes induce a consistent pattern in the morphological changes of intracranial pressure (ICP) pulse. This premise was demonstrated using a published algorithm in previous papers. In this study, we applied the algorithm to calculate the likelihood of cerebral vasodilation (VDI) and cerebral vasoconstriction (VCI) from intraprocedural ICP signals that are synchronized with injection of the IA vasodilator, verapamil. Cerebral blood flow velocities (CBFVs) on bilateral cerebral arteries were studied before and after IA therapy.

RESULTS

192 recordings of patients with SAH were reviewed, and 27 recordings had high-quality ICP waveforms. The VCI was significantly lower after the first verapamil injection (0.47±0.017) than VCI at baseline (0.49±0.020, p<0.001). A larger dose of injected verapamil resulted in a larger and longer VDI increase. CBFV of the middle cerebral artery increases across the days before the injection of verapamil and decreases after IA therapy.

CONCLUSION

This study provides preliminary validation of an algorithm for continuous assessment of distal cerebral arterial changes in response to IA vasodilator infusion in patients with vasospasm and aneurysmal SAH.

Evaluation of a New Catheter for Simultaneous Intracranial Pressure Monitoring and Cerebral Spinal Fluid Drainage: A Pilot Study

OBJECTIVES

Intracranial pressure (ICP) monitoring is a common practice when treating intracranial pathology with risk of elevated ICP. External ventricular drain (EVD) insertion is a standard approach for both monitoring ICP and draining cerebrospinal fluid (CSF). However, the conventional EVD cannot serve these two purposes simultaneously because it cannot accurately measure ICP and its pulsatile waveform while the EVD is open to CSF drainage. A new Integra^® Camino^® FLEX Ventricular Catheter (Integra Lifesciences, County Offaly, Ireland) with a double-lumen construction has been recently introduced into the market, and it can monitor ICP waveforms even during CSF drainage. The aim of this study was to evaluate and validate this new FLEX catheter for ICP monitoring in a neurological intensive care unit.

METHODS

Six patients with 34 EVD open/close episodes were retrospectively analyzed. Continuous ICP was detected in two ways: through the FLEX sensor at the tip (ICP_f) and through a fluid-coupled manometer within the FLEX catheter, functioning as a conventional EVD (ICP_e). The morphologies of ICP_f and ICP_e pulses were extracted using Morphological Clustering and Analysis of ICP algorithm, an algorithm that has been validated in previous publications. The mean ICP and waveform shapes of ICP pulses detected through the two systems were compared. Bland-Altman plots were used to assess the agreement of the two systems.

RESULTS

A significant linear relationship existed between mean ICP_f and mean ICP_e, which can be described as: mICP_f = 0.81 × mICP_e + 1.67 (r = 0.79). The Bland-Altman plot revealed that no significant difference existed between the two ICPs (average of [ICP_e-ICP_f] was - 1.69 mmHg, 95% limits of agreement: - 7.94 to 4.56 mmHg). The amplitudes of the landmarks of ICP pulse waveforms from the two systems showed strong, linear relationship (r ranging from 0.89 to 0.94).

CONCLUSIONS

This study compared a new FLEX ventricular catheter with conventional fluid-coupled manometer for ICP waveform monitoring. Strong concordance in ICP value and waveform morphology between the two systems indicates that this catheter can be used for reliability for both clinical and research applications.

Cerebral Vascular Changes During Acute Intracranial Pressure Drop

OBJECTIVE

This study applied a new external ventricular catheter, which allows intracranial pressure (ICP) monitoring and cerebral spinal fluid (CSF) drainage simultaneously, to study cerebral vascular responses during acute CSF drainage.

METHODS

Six patients with 34 external ventricular drain (EVD) opening sessions were retrospectively analyzed. A published algorithm was used to extract morphological features of ICP recordings, and a template-matching algorithm was applied to calculate the likelihood of cerebral vasodilation index (VDI) and cerebral vasoconstriction index (VCI) based on the changes of ICP waveforms during CSF drainage. Power change (∆P) of ICP B-waves after EVD opening was also calculated. Cerebral autoregulation (CA) was assessed through phase difference between arterial blood pressure (ABP) and ICP using a previously published wavelet-based algorithm.

RESULTS

The result showed that acute CSF drainage reduced mean ICP (P = 0.016) increased VCI (P = 0.02) and reduced ICP B-wave power (P = 0.016) significantly. VCI reacted to ICP changes negatively when ICP was between 10 and 25 mmHg, and VCI remained unchanged when ICP was outside the 10-25 mmHg range. VCI negatively (r = - 0.44) and VDI positively (r = 0.82) correlated with ∆P of ICP B-waves, indicating that stronger vasoconstriction resulted in bigger power drop in ICP B-waves. Better CA prior to EVD opening triggered bigger drop in the power of ICP B-waves (r = - 0.612).

CONCLUSIONS

This study demonstrates that acute CSF drainage reduces mean ICP, and results in vasoconstriction which can be detected through an index, VCI. Cerebral vessels actively respond to ICP changes or cerebral perfusion pressure (CPP) changes in a certain range; beyond which, the vessels are insensitive to the changes in ICP and CPP.

Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

Identification of Pulse Onset on Cerebral Blood Flow Velocity Waveforms: A Comparative Study

The low cost, simple, noninvasive, and continuous measurement of cerebral blood flow velocity (CBFV) by transcranial Doppler is becoming a common clinical tool for the assessment of cerebral hemodynamics. CBFV monitoring can also help with noninvasive estimation of intracranial pressure and evaluation of mild traumatic brain injury. Reliable CBFV waveform analysis depends heavily on its accurate beat-to-beat delineation. However, CBFV is inherently contaminated with various types of noise/artifacts and has a wide range of possible pathological waveform morphologies. Thus, pulse onset detection is in general a challenging task for CBFV signal. In this paper, we conducted a comprehensive comparative analysis of three popular pulse onset detection methods using a large annotated dataset of 92,794 CBFV pulses—collected from 108 subarachnoid hemorrhage patients admitted to UCLA Medical Center. We compared these methods not only in terms of their accuracy and computational complexity, but also for their sensitivity to the selection of their parameters' values. The results of this comprehensive study revealed that using optimal values of the parameters obtained from sensitivity analysis, one method can achieve the highest accuracy for CBFV pulse onset detection with true positive rate (TPR) of 97.06% and positive predictivity value (PPV) of 96.48%, when error threshold is set to just less than 10 ms. We conclude that the high accuracy and low computational complexity of this method (average running time of 4ms/pulse) makes it a reliable algorithm for CBFV pulse onset detection.

A Coupled Lumped-Parameter and Distributed Network Model for Cerebral Pulse-Wave Hemodynamics

The cerebral circulation is unique in its ability to maintain blood flow to the brain under widely varying physiologic conditions. Incorporating this autoregulatory response is necessary for cerebral blood flow (CBF) modeling, as well as investigations into pathological conditions. We discuss a one-dimensional (1D) nonlinear model of blood flow in the cerebral arteries coupled to autoregulatory lumped-parameter (LP) networks. The LP networks incorporate intracranial pressure (ICP), cerebrospinal fluid (CSF), and cortical collateral blood flow models. The overall model is used to evaluate changes in CBF due to occlusions in the middle cerebral artery (MCA) and common carotid artery (CCA). Velocity waveforms at the CCA and internal carotid artery (ICA) were examined prior and post MCA occlusion. Evident waveform changes due to the occlusion were observed, providing insight into cerebral vasospasm monitoring by morphological changes of the velocity or pressure waveforms. The role of modeling of collateral blood flows through cortical pathways and communicating arteries was also studied. When the MCA was occluded, the cortical collateral flow had an important compensatory role, whereas the communicating arteries in the circle of Willis (CoW) became more important when the CCA was occluded. To validate the model, simulations were conducted to reproduce a clinical test to assess dynamic autoregulatory function, and results demonstrated agreement with published measurements.

Characterization of Cerebral Vascular Response to EEG Bursts Using ICP Pulse Waveform Template Matching

Neurovascular coupling is the relationship between the activity of the brain and the subsequent change in blood flow to the active region. The most common methods of detecting neurovascular coupling are cumbersome and noncontinuous. However, the integration of intracranial pressure (ICP) and electroencephalography (EEG) may serve as an indirect measure of neurovascular coupling.This study used data collected from burst-suppressed patients who received both ICP and depth EEG monitoring. An adaptive thresholding algorithm was used to detect the start and end of each EEG burst. The morphological clustering and analysis of ICP and pulse morphological template-matching algorithms were then applied to derive several metrics describing the shape of the ICP pulse waveform and track how it changed following an EEG burst. These changes were compared using a template obtained from patients undergoing CO2-induced vasodilation.All segments exhibited a significant period of vasodilation within 1-2 s after burst, and 4 of 5 had a significant period of vasoconstriction within 4-11 s of the EEG burst, suggesting that there might be a characteristic response of vasodilation and subsequent vasoconstriction after a spontaneous EEG burst. Furthermore, these findings demonstrate the potential of integrated EEG and ICP as an indirect measure of neurovascular coupling.

Characterization of the relationship between intracranial pressure and electroencephalographic monitoring in burst-suppressed patients

BACKGROUND

The objective of this study is to characterize the relationship between ICP and EEG METHODS: Simultaneous ICP and EEG data were obtained from burst-suppressed patients and segmented by EEG bursts. Segments were categorized as increasing/decreasing and peak/valley to investigate relationship between ICP changes and EEG burst duration. A generalized ICP response was obtained by averaging all segments time-aligned at burst onsets. A vasodilatation index (VDI) was derived from the ICP pulse waveform and calculated on a sliding interval to investigate cerebrovascular changes post-burst.

RESULTS

Data from two patients contained 309 bursts. 246 ICP segments initially increased, of which 154 peaked. 63 ICP segments decreased, and zero reached a valley. The change in ICP (0.54 ± 0.85 mmHg) was significantly correlated with the burst duration (p < 0.001). Characterization of the ICP segments showed a peak at 8.1 s and a return to baseline at 14.7 s. The VDI for increasing segments was significantly elevated (median 0.56, IQR 0.31, p < 0.001) and correlated with burst duration (p < 0.001).

CONCLUSIONS

Changes in the ICP and pulse waveform shape after EEG burst suggest that these signals can be related within the context of neurovascular coupling.

SIGNIFICANCE

Existence of a physiological relationship between ICP and EEG may allow the study of neurovascular coupling in acute brain injury patients.

Taking the pulse of aging: mapping pulse pressure and elasticity in cerebral arteries with optical methods

Cerebrovascular support is crucial for healthy cognitive and brain aging. Arterial stiffening is a cause of reduced brain blood flow, a predictor of cognitive decline, and a risk factor for cerebrovascular accidents and Alzheimer's disease. Arterial health is influenced by lifestyle factors, such as cardiorespiratory fitness (CRF). We investigated new noninvasive optical measures of cerebrovascular health, which provide estimates of arterial pulse parameters (pulse pressure, transit time, and compliance/elasticity) within specific cerebral arteries and cortical regions, and low-resolution maps of large superficial cerebral arteries. We studied naturally occurring variability in these parameters in adults (aged 55-87), and found that these indices of cerebrovascular health are negatively correlated with age and positively with CRF and gray and white matter volumes. Further, regional pulse transit time predicts specific neuropsychological performance.

Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer

Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively.

Is there any association between cerebral vasoconstriction/vasodilatation and microdialysis Lactate to Pyruvate ratio increase?

BACKGROUND

Although abnormally high Lactate/Pyruvate ratio (LPR) could indicate cerebral ischemia for brain injury patients, there is a debate on what is primary factor responsible for LPR increase.

METHODS

A data analysis experiment is taken to test whether any association between cerebral vasodilatation/vasoconstriction and LPR increase exists. We studied 4,316 microdialysis data samples collected in an average interval of 1.3 h from 30 severe traumatic brain injury (TBI) patients. The LPR increase episodes were automatically identified using a moving time-window of 5 samples. A novel pulse morphological template matching (PMTM) algorithm was applied to the intracranial pressure (ICP) data of the corresponding patients to assess the occurrence of cerebral vasodilatation and vasoconstriction during the identified LPR increase episodes. Several analyses were performed to evaluate the association between cerebral vasoconstriction/vasodilatation and LPR increase.

RESULTS

Results revealed that although more than half of the LPR increase episodes are not associated with any detected cerebral vasoconstriction/vasodilatation, when a vaso-change happens in association of LPR increase, it is more likely that this vaso-change is in the form of vasoconstriction rather than vasodilatation. Also for few subjects with dominant number of vasoconstriction episodes, a causality relationship between vasoconstriction and LPR increase were observed (vasoconstriction precedes LPR increase).

CONCLUSIONS

Using continuous intracranial pressure monitoring and our pulse morphological template matching (PMTM) algorithm could be potentially helpful in teasing out whether culprit cerebral vascular changes precede metabolic crisis for traumatic brain injury patients and hence guiding the management of this condition.

Reproduction of consistent pulse-waveform changes using a computational model of the cerebral circulatory system

Due to the inaccessibility of the cranial vault, it is difficult to study cerebral blood flow dynamics directly. A mathematical model can be useful to study these dynamics. The model presented here is a novel combination of a one-dimensional fluid flow model representing the major vessels of the circle of Willis (CoW), with six individually parameterized auto-regulatory models of the distal vascular beds. This model has the unique ability to simulate high temporal resolution flow and velocity waveforms, amenable to pulse-waveform analysis, as well as sophisticated phenomena such as auto-regulation. Previous work with human patients has shown that vasodilation induced by CO2 inhalation causes 12 consistent pulse-waveform changes as measured by the morphological clustering and analysis of intracranial pressure algorithm. To validate this model, we simulated vasodilation and successfully reproduced 9 out of the 12 pulse-waveform changes. A subsequent sensitivity analysis found that these 12 pulse-waveform changes were most affected by the parameters associated with the shape of the smooth muscle tension response and vessel elasticity, providing insight into the physiological mechanisms responsible for observed changes in the pulse-waveform shape.