WO2014055798A1 - Cerebral vaso-reactivity assessment using pulse morphological template matching - Google Patents

Abstract

A method for real-time detection of cerebral vasodilatation and vasoconstriction using pulse morphological template matching. Templates comprising morphological metrics (e.g. of cerebral blood flow velocity (CBFV) pulse, which may be measured at middle cerebral artery using Transcranial Doppler), are first obtained by applying a morphological clustering and analysis of intracranial pulse algorithm to the data collected during induced vasodilatation and vasoconstriction in a controlled setting. These templates are then employed to define a vasodilatation index (VDI) and a vasoconstriction index (VCI) for any inquiry data segment as the percentage of the metrics demonstrating a trend consistent with those obtained from the training dataset.

Description

CEREBRAL VASO-REACTIVITY ASSESSMENT USING PULSE

MORPHOLOGICAL TEMPLATE MATCHING

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a nonprovisional of U.S. provisional patent

application serial number 61/709,256 filed on October 3, 2012, incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED

RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant Nos. NS059797 and NS066008 awarded by the National Institutes of Health (NIH). The Government has certain rights in this invention.

INCORPORATION-BY-REFERENCE OF

COMPUTER PROGRAM APPENDIX

Not Applicable

NOTICE OF MATERIAL SUBJECT TO COPYRIGHT PROTECTION

[0004] A portion of the material in this patent document is subject to

copyright protection under the copyright laws of the United States and of other countries. The owner of the copyright rights has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the United States Patent and Trademark Office publicly available file or records, but otherwise reserves all copyright rights whatsoever. The copyright owner does not hereby waive any of its rights to have this patent document maintained in secrecy, including without limitation its rights pursuant to 37 C.F.R. § 1 .14. BACKGROUND OF THE INVENTION

[0005] 1 . Field of the Invention

[0006] The present invention pertains generally to cardiovascular

monitoring, and more particularly to the monitoring of cerebral vasculature.

[0007] 2. Description of Related Art

[0008] Although accurate and continuous assessment of cerebral

vasculature status is highly desirable for managing cerebral vascular diseases, no such method exists for current clinical practice. Establishing a modality for direct monitoring of cerebral vasculature is considerably challenging due to the rigid skull. Methodologies to assess the cerebral vasculature like Transcranial Doppler (TCD) are limited due to the skull density that only allows insonation of large vessels of the circle of Willis in individuals with favorable windows by trained technicians.

[0009] While digital subtraction, CT, or MRI angiographic methods may provide accurate images of the cerebral vasculature and in some cases functional information of the cerebral blood flow, they can only be performed intermittently and carry risks associated with the use of contrast media, radiation or the endovascular intervention.

[0010] A few indirect metrics have been used to assess the cerebral

vasculature using hemodynamic concepts such as resistance and vascular tone exist, e.g. Gosling pulsatility index (PI), Pourcelot resistance index (Rl), and critical closing pressure (CCP). However, these metrics are often not accurate as they are derived from simplified models of cerebral blood flow circulation whose underlying assumptions may not be applicable to the related clinical scenario. In addition to a potential model-mismatch, hemodynamics metrics such as CCP and RAP rely on approximating the cerebral arterial blood pressure using peripherally measured systemic pressures, which may further compromise the accuracy of these metrics due to confounding influence from extracranial systemic circulatory systems.

[0011] Furthermore, the methods currently available to evaluate the

pathophysiological changes of the cerebral circulation have significant time resolution limitations and do not allow continuous evaluations of the fluctuations in cerebral perfusion.

BRIEF SUMMARY OF THE INVENTION

[0012] One aspect is a system and methods for real-time detection of

cerebral vasodilatation and vasoconstriction using pulse morphological template matching.

[0013] In a preferred aspect, the systems and methods of the present

invention are configured to provide a real-time and continuous assessment of cerebral vaso-reactivity using a model-free approach to avoid

assumptions necessary for building the model.

[0014] In another aspect, the system and methods of the present invention use pulse morphological changes in addition to the mean of pulsatile signal to detect cerebral vasodilatation and vasoconstriction.

[0015] The systems and methods of the present invention may be practiced as an add-on module to current pulsatile physiological signal monitoring devices, or may comprise a plug-in for next-generation ICU monitoring systems. One example of the latter is the intelligent ICU (ilCU) system.

[0016] The model-free systems and methods for assessing cerebral

vasculature are built on the premise that intracranial pulses have a clear vascular origin and cerebrovascular changes, especially those of acute nature, can modulate the shape of individual pulses. In one embodiment, cerebral blood flow velocity (CBFV) pulse morphological templates are obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) algorithm to the cerebral blood flow velocity (CBFV) data collected during induced vasodilatation and vasoconstriction in a controlled setting. These templates may then be employed to define a vasodilatation index (VDI) and a vasoconstriction index (VCI) for any inquiry data segment as the percentage of the metrics demonstrating a trend consistent with those obtained from the training dataset.

[0017] Another aspect is a method for real-time detection of cerebral

vasodilatation and vasoconstriction using pulse morphological template matching. Templates comprising morphological metrics of cerebral blood flow velocity (CBFV) pulse, which may be measured at middle cerebral artery using Transcranial Doppler, are first obtained by applying a morphological clustering and analysis of intracranial pulse algorithm to the data collected during induced vasodilatation and vasoconstriction in a controlled setting. These templates are then employed to define a vasodilatation index (VDI) and a vasoconstriction index (VCI) for any inquiry data segment as the percentage of the metrics demonstrating a trend consistent with those obtained from the training dataset.

[0018] Another aspect is system and method for providing real time

information on the changes occurring in the cerebral vasculature, which may increase the time effectiveness of therapeutic interventions and prevent secondary cerebral damage due to ischemia or hyperperfusion. Such a system and method could be particularly advantageous in the management of conditions such as cerebral vasospasm after subarachnoid hemorrhage, evaluation of the collateral flow in patients with acute and chronic unstable ischemic stroke and monitoring of cerebrovascular changes associated with traumatic brain injury.

[0019] Another aspect is a system and method for assessing the cerebral vasculature as a function of changing intracranial pressure pulse

morphology as patients inhale a CO2-enriched gas mixture. Such a system and method are based on the premise that: 1 ) intracranial pulses, including ICP and CBFV, originate from vascular pulsations propagating from the heart and hence acute cerebrovascular changes can modulate the shape of these pulses; 2) this modulation induces changes of pulse morphology in an expected fashion, i.e., vasodilatation or vasoconstriction causes pulses to change in a certain way so that a quantification of how well an

observation of pulse morphological changes matches this expectation can lead to metrics of cerebral vasodilatation and vasoconstriction.

[0020] The systems and methods of the present invention were validated on a dataset of 27 healthy subjects during hyperventilation and CO2

rebreathing tests show a sensitivity of 92% and 81 % for detection of vasodilatation and vasoconstriction, respectively and the specificity of 90% for both. Moreover, the method of detection of vasodilatation

(vasoconstriction) is capable of rejecting all the cases associated with vasoconstriction (vasodilatation).

[0021] Further aspects of the invention will be brought out in the following portions of the specification, wherein the detailed description is for the purpose of fully disclosing preferred embodiments of the invention without placing limitations thereon. BRIEF DESCRIPTION OF THE SEVERAL VIEWS

OF THE DRAWING(S)

[0022] The invention will be more fully understood by reference to the

following drawings which are for illustrative purposes only:

[0023] FIG. 1 shows an exemplary refined ICP pulse with metrics that can be extracted and monitored according to the present invention.

[0024] FIG. 2 shows an array of metrics (n = 128) associated with the ICP pulse of FIG. 1 to comprehensively characterize the amplitude, curvature, slope, and time-intervals among peaks and troughs of pulses.

[0025] FIG. 3 is a flow diagram of a training method used to identify a set of MOCAIP metrics that consistently increase/decrease during vasodilatation but decrease/increase during vasoconstriction.

[0026] FIG. 4 is a flow diagram of the template matching method to define a vasodilatation index (VDI) and vasoconstriction index (VCI) for an inquiry data segment.

[0027] FIG. 5 illustrates a schematic flow diagram of an exemplary

morphological clustering and analysis of an intracranial pulse (MOCAIP) algorithm in accordance with the present invention.

[0028] FIG. 6 illustrates an exemplary system for detection of cerebral vasodilatation and/or vasoconstriction via pulse morphological template matching.

[0029] FIG. 7A shows a plot of the calculated VDI for testing data segments of CO₂ rebreathing and baseline measurements over their corresponding level of change in P_ETCO2.

[0030] FIG. 7B shows a plot of the ROC curves of vasodilatation detection, excluding this invalid data segment, and the related calculated parameters are presented in Table 1 .

[0031] FIG. 8A shows a plot of the calculated VCI for testing data segments of CO2 rebreathing and baseline measurements over their corresponding level of change in PETCO_2.

[0032] FIG. 8B shows a plot of the ROC curves of vasoconstriction

detection, and the related calculated parameters are presented in Table 2.

[0033] FIG. 9A is a plot of the hyperventilation data segments with validated vasoconstriction episodes.

[0034] FIG. 9B is a plot of the hyperventilation data segments with validated vasodilatation episodes.

[0035] FIG. 10A through FIG. 10D show examples of invalid data segments during CO₂ rebreathing and hyperventilation; (FIG. 10A) non-increasing trend of PETCO2 for subject #4 during CO2 rebreathing; (FIG. 10B) non- decreasing trend of PETCO2 for subject #3 during hyperventilation; (FIG.

10C) non-decreasing trend of cerebrovascular resistance index (CVRi) for subject #4 during CO2 rebreathing; (FIG. 10D) non-increasing trend of CVRi for subject #3 during hyperventilation.

[0036] FIG. 1 1 A is a plot of the calculated vasodilatation index (VDI) over

ACVRi during CO₂ rebreathing.

[0037] FIG. 1 1 B is a plot of the calculated vasoconstriction index (VCI) over

ACVRi during hyperventilation.

[0038] FIG. 12A shows a comparison plot of vaso-reactivity detection

performance using the VDI index, resistance area product (RAP) and critical closing pressure (CCP).

[0039] FIG. 12B shows a comparison plot of vaso-reactivity detection

performance using the VDI index, resistance area product (RAP) and critical closing pressure (CCP).

[0040] FIG. 13A is a plot illustrating accuracy of the detection of

vasodilatation and vasoconstriction for different numbers of subjects in the training dataset.

[0041] FIG. 13B is a plot illustrating the effect of the size (number of

consistent MOCAIP metrics) of the largest template obtained from a training dataset of n-subjects where n=1 ...21 .

DETAILED DESCRIPTION OF THE INVENTION

[0042] The systems and methods of the present invention detect cerebral vasodilatation and/or vasoconstriction by quantitatively characterizing the shape of intracranial pulses. In a preferred embodiment, a Morphological Clustering and Analysis of an Intracranial Pulse (MOCAIP) algorithm is used for the automatic extraction of morphological features of intracranial pulsatile signals (FIG. 1 ) in real time. The MOCAIP algorithm (shown as method 100 in greater detail in FIG. 5), provides a framework capable of enhancing signal quality via recognition of legitimate (not contaminated with noise and artifacts) pulses and detection of the three subpeaks and three sub-nadirs of an intracranial pulse. It is appreciated that CBFV pulses also follow the same triphasic shape as ICP pulses. Collectively, we term pulsatile signals from intracranial compartment "Intracranial Pulse".

[0043] Referring to FIG. 2, via identification of these six landmarks on an individual pulse, the MOCAIP algorithm generates an array of metrics (n =

128) to comprehensively characterize the amplitude, curvature, slope, and time-intervals among peaks and troughs of pulses.

[0044] Therefore, the MOCAIP algorithm can be reliably applied to process continuous ICP and CBFV signal recordings from real clinical environment to extract useful morphological features of the corresponding pulses.

Additional information concerning MOCAIP can be found in U.S. patent application serial no. 12/985,603 filed on January 6, 201 1 and published as US Patent Application Publication No. US-201 1 -0201961 -A1 , herein incorporated by reference in its entirety.

[0045] Referring now FIG. 3 and FIG. 4, a training algorithm 10 and

template matching algorithm 30 are shown for training and testing of detection of cerebral vaso-reactivity using pulse morphological template matching (PMTM).

[0046] To detect cerebral vasodilatation and/or vasoconstriction using

MOCAIP algorithm 100, the MOCAIP metrics which are the potential indicator of a vasodilatation or vasoconstriction episode are first

determined. A primary objective of the PMTM method is to obtain the template that defines the expected pulse morphological change pattern associated with cerebral vasodilatation and vasoconstriction.

[0047] Referring to training method 10 of FIG. 3, a training data set is used to identify a set of MOCAIP metrics (FIG. 2) that consistently

increase/decrease during vasodilatation but decrease/increase during vasoconstriction. The qualification of this template can be established by the fact that vasodilatation and vasoconstriction are opposite physiological processes and hence the direction of change of a valid indicator of vasodilatation or vasoconstriction should be opposite in these two conditions.

[0048] An experiment is first conducted to induce paired cerebral

vasodilatation-vasoconstriction in multiple subjects. Intracranial pulses from episodes of vasodilatation and vasoconstriction are then analyzed using the MOCAIP algorithm 100. A monotonic trending detection algorithm is used next to determine the direction of changes for each MOCAIP metric during vasodilatation and vasoconstriction. Let us assume that our training dataset 290 (FIG. 6) comprises vasodilatation data segments 12 and

vasoconstriction data segments 14 from identified episodes of

vasodilatation or vasoconstriction of R subjects. Then, by robustly fitting a regression line (using weighted least square method) to the extracted metric values (e.g. CBFV) over the segment of interest, one can determine the trend of change of 128 MOCAIP metrics (increasing vs. decreasing) over each individual training data segment. Here we define the "consistent vasodilatation metrics" as those metrics which have the same trend

(direction) over the vasodilatation data segments of all subjects in the training dataset. [0049] Following this definition, a vector of metrics trend during

vasodilatation (V_dii_atation = [vi v₂. ..Vj...vi₂8]^T) is constructed from the training dataset 12 at block 16, by assigning a ternary number of {+1 , -1 , 0} to each metric as:

1 if j^th metric consistently increases across all subjects

V_D (j) = -1 ifj^th metric consistently decreases across all subjects ^

0 otherwise to represent for each MOCAIP metric its consistency and the direction of change.

[0050] A vector of metrics trend during vasoconstriction (V_constriction) is

similarly constructed from training dataset 14 at block 18. Since

vasodilatation and vasoconstriction are regarded as opposite physiological phenomena, one can expect for a MOCAIP metric to be a potential indicator of vasodilatation and/or vasoconstriction, only if its trend of change during one phenomenon is in opposite direction of the other.

[0051] At block 20, the subset of indices for the MOCAIP metrics that are a potential indicator of a vasodilatation or vasoconstriction event, subset ( I ), may be determined as I = {i|ei = -1}, where matrix E = diag (V_dii_atation) * diag (V_constriction) and [d e₂ ...e^s]¹ are the entries along the main diagonal of matrix E.

[0052] In summary, Vdiiatation, V_COnstriction and I are the three elements of a PMTM template. The subsequent discussion is directed to application of a PMTM template to detect either cerebral vasodilatation or vasoconstriction.

[0053] Referring to FIG. 4, the obtained template or subset of metrics

indices ( I ) from method 10 may be employed with the template matching method 30 to define a vasodilatation index (VDI) and vasoconstriction index (VCI) for an inquiry data segment 32 (i.e. determination/association of a given segment of an intracranial pulsatile signal as a vasodilatation event, vasoconstriction event, or neither). VDI and VCI are the approximate likelihood of vasodilatation and vasoconstriction, respectively, and the percentage of I metrics whose trends of change over the inquiry data segment are similar to those of the consistent vasodilatation/ vasoconstriction metrics learned from the training dataset.

[0054] In a preferred embodiment, VDI and VCI are calculated by

application of MOCAIP to analyze this pulsatile intracranial signal and obtain a time series of MOCAIP metrics. The same monotonic trending detection algorithm as adopted in the training method 10 is used to determine the existence and the direction of the change of each MOCAIP metric. More specifically, to calculate VDI for an inquiry data segment 32, a line is robustly fit to each of extracted metrics (m_j, j=l, 128) over the data segment of interest and calculate P_j, the probability of t-statistic of regression (estimated slope divided by its standard error). Hence, a vector of metrics trend for the inquiry data segment x dilatation ⁼ [^χι ^x2 — ^xj - · ^χΐ28 ]^{T is} constructed at block 38 as:

1 if t ej^th

X ; -1 if t ej^th Eq. 2

0 if t ej^th where a positive 5_j is a preset threshold for accepting/rejecting the null hypothesis that the slope of the fitted line is equal to zero, so j < 5_j means that the estimated slope of the fitted line is statistically significant.

[0055] The best value choice for 5_j is dependent on the nature of data. One option would be to set it equal to a percentile of the probabilities of t- statistics calculated for the corresponding metric over the subjects in the training dataset. So if potation G,^r) 34 represents probability of t-statistics of the fitted regression line to the j^th metric j = 1,..., 128) for the vasodilatation data segment of the r^th subject in the training dataset (r = 1 , ...,R), then

Pdilatation = [Pdilatation (],^r)]l28xR and 5j = ρ^βΓΰ^βΠίίΙ^β Of j^th TOW Of Pdilatation-

[0056] At block 40, if diagonal matrix F is denoted as F = diag (V_dii_atation) * diag (Xdiiatation), then D would be a subset of metric indices whose trend of change for the inquiry data segment is similar to those learned from vasodilatation of the training dataset. In other words, D = {d|f_d = 1 } where [fi f₂ ...f_j . ..fi2s]^T are the entries along the main diagonal of matrix F. Using the notation of N_s as the number of elements in set S and Π as the intersection set operation, VDI may be obtained at block 42 as:

Ν^ηητ

VDI =— Eq. 3

i

[0057] Similarly, to verify whether the inquiry data segment is associated with a vasoconstriction event, a vector

X constriction = [xi *2 — ^xj · - ^xi28 ]^{T is} constructed at block 44 by determining the slope of the fitted regression line to each MOCAIP metric and comparing the probability p_j to the preset threshold for the null hypothesis that the slope of the fitted line is equal to zero. Similarly,

Pconstriction (j,^r) 36, which represents the probability of t-statistics of the fitted regression line to the j^th metric j = 1,...,128) for the constriction data segment of the r^th subject in the training dataset (r = 1 , ...,R), is obtained from the training data. So a vasoconstriction index (VCI) can be calculated

N_rni

at block 48 as VCI = where G = diag (V_constriction) * diag (X_Constriction)

Ni

and C = {c|g_c = l}, both of which are calculated at block 46.

[0058] Following calculation of VDI (or VCI) for an inquiry segment of data, an assessment is made at blocks 52 and 54 whether the corresponding segment could be associated with a vasodilatation event 60 or

vasoconstriction event 62 by comparing the calculated VDI (or VCI) with a preset threshold Ψ₀ or Ψ₀. If thresholds Ψ₀ or Ψ₀ are not met, a

determination is returned of no vasodilatation event 56 or vasoconstriction event 58. Since Ψ₀ or Ψ₀ (0< Ψο,Ψο < 1 ) are the thresholds on the percentage of the indicator metrics of subset I 50 which have

demonstrated an expected trend of change during inquired vasodilatation (or vasoconstriction) event, it is expected that decreasing the value of threshold would increase both the true positive rate (TPR) and false positive rate (FPR) of the detection of vasodilatation (or vasoconstriction). [0059] In sum, the trend detection algorithm above uses a robust line fitting algorithm to conduct a weighted least square fit of each MOCAIP metric time series with a line. In constructing the PMTM template, the sign of the resultant slope is used to indicate whether the trend is positive or negative because the consistency or existence of the trend for i^th metric will be verified by criterion V_D(i)x V_c(i) = -1 . In applying the template to a new signal segment, we first determine whether a trend exists. This is done by using the p value of the estimated slope, i.e., a trend exists if the

corresponding p value is less than a pre-set threshold. Once established whether a trend exists, the sign of the slope is taken to judge the direction of the change as needed in Eq. 2.

[0060] FIG. 5 illustrates a schematic flow diagram of an exemplary

morphological clustering and analysis of an intracranial pulse (MOCAIP) algorithm 100 in accordance with the present invention. An observed pulse morphological change can be first quantified in terms of MOCAIP metrics and then compared with a template of expected changes during either cerebral vasodilatation or vasoconstriction to establish the likelihood of conformance of this observation to the template.

[0061] MOCAIP algorithm 100 comprises an integrated and modular pulse analysis framework for analyzing pulsatile signals, e.g. ICP and CBFV. A pulse extraction technique is used to segment the continuous signal into a sequence of individual pulses. Then, in order to handle the practical problems of noise and artifacts commonly found in clinical signals, a hierarchical clustering approach, different filtration and the correlation calculation of the extracted pulse with all the pulses of a reference library of already validated pulses, are employed. A set of the peak candidates or curve inflections are detected for each validated pulse and then the three sub-peaks are identified from these peak candidates by maximizing the probability of observing the current sub-peaks given the prior Gaussian distributions of the peaks (learned from the library of valid pulses). Finally, as shown in FIG. 2, 128 pulse morphological metrics are extracted using the identified peaks and troughs.

[0062] Further refinement of each of MOCAIP algorithm 100 individual processing blocks may be performed, e.g. enhancement of the

performance of valid pulse recognition applying a singular value

decomposition based algorithm or increase of the accuracy of peak designation using a nonlinear regression-based method, an integrated peak recognition technique, and a non-parametric Bayesian tracking algorithm. Therefore, the MOCAIP algorithm 100 can be reliably applied to process continuous signal recordings from real clinical environments to extract useful morphological features of the corresponding pulses.

[0063] Used in conjunction with MOCAIP algorithm 100, the resulting Pulse Morphological Template Matching (PMTM) algorithm 30 of the present invention is non-parametric, does not depend on any models of CBF circulation, and is not confounded by influences from systemic circulation.

[0064] As seen in FIG. 5, intracranial pulse signals are acquired at block 120 from a sensor (e.g. sensor 210 of system 200 shown in FIG. 6) using conventional methods of installation. Signals produced from the sensors are preferably recorded and analyzed or may be stored in random access memory of a computer and analyzed in real time without recording in the alternative. In the preferred embodiment, the continuous input signals optionally include ECG signals to produce a stream of pulse data. It is appreciated that pulse signals may be obtained from a number of locations within the brain of the patient, e.g. a cerebral artery, brain ventricle, intraparenchymal tissue, subarachnoid space, or epidural space, etc.

Furthermore, the system may also import, in a dynamic fashion depending on the testing subject, an existing template that may be further

parameterized by gender, sex, age, mean blood pressure, mean heart rate, etc. to match the condition of a test subject.

[0065] The acquired pulse signal data at block 120 is processed with a

number of process steps to eliminate noise and to refine the peaks for analysis at block 190. In the embodiment shown in FIG. 5, a system with five major components is provided including a beat-by-beat pulse detection component 130, a pulse clustering component 140, a non-artifactual pulse recognition component 150, a peak detection component 160, and an optimal peak designation component 180. In addition, the algorithm makes use of a library of reference intracranial pressure (ICP) pulses that contains a collection of pulses and locations of their designated three peaks. The beat-by-beat detection of the ICP pulse at block 130 is preferably conducted using an algorithm for extracting intracranial pressure latency relative to electrocardiogram r wave, as understood in the art.

[0066] Pulse clustering may be used in two stages of processing. Clustering is initially applied to consecutive subsequences of the raw ICP pulses obtained from the ICP pulse detection process to generate a dominant pulse for each pulse sequence at block 140. This process results in a sequence of dominant ICP pulses that is further analyzed by the pulse recognition component 150. Pulse clustering may be applied again to the sequence of dominant pulses in this process. The recognized non- artifactual pulses may be further processed to detect all peak candidates in each of them. Finally, the peak designation process 180 is executed to optimally designate the three well-established ICP peaks in each non- artifactual dominant pulse using the detected peak candidates in the embodiment shown.

[0067] Referring now to block 130, the ICP pulse is detected from the ICP signals from the sensors. This step segments the continuous ICP into a sequence of individual ICP pulses. Instead of solely using ICP for pulse detection, the mature technique of ECG QRS detection to first find each ECG beat is preferred to achieve reliable ICP pulse detection. Optionally, interval constraints for ICP peak locations can be incorporated to prevent false ICP pulse detections that would be caused by spurious ECG QRS detections. The interval constraints can also be adapted on a beat-by-beat basis.

[0068] ICP recordings collected from bedside monitors can often be

contaminated by several types of noise and artifacts. For example ICP pulses can be contaminated by high-frequency noise that originated from measurement or amplifier devices. Transient artifacts from coughing or patient movement or ICP recordings with the sensor detached from the patient monitor for a period of time can also occur. These artifacts and noise are common for typical ICP recordings and can interfere with the analysis of ICP pulse morphology.

[0069] Instead of applying the ICP morphology analysis to each individual pulse separately, a representative cleaner pulse is preferably extracted from a sequence of consecutive ICP pulses at block 140. Therefore, a continuous ICP recording can be segmented into consecutive pulse sequences and morphological characteristics of the pulses can be calculated based on the representative pulse of each sequence, in this embodiment.

[0070] In one embodiment at block 140, a sequence of raw ICP pulses is first clustered into distinct groups based on their morphological distance. The largest cluster is then identified. An averaging process is conducted to obtain an averaged pulse for this largest cluster. These averaged pulses of the largest cluster are called dominant ICP pulses. Subsequent analysis of ICP morphology will be only conducted for this dominant pulse. This dominant pulse is preferred for performing morphological analysis because the clustering procedure will effectively isolate transient disturbances from the normal ICP pulses. Therefore, the dominant ICP pulse would most likely represent the signal group. In addition, the averaging process effectively reduces influences from random noise and quantization noise on the morphological analysis of the ICP pulse by enhancing the signal-to-noise ratio.

[0071] In one embodiment, a hierarchical clustering approach is used to cluster ICP pulses at block 140 because it does not require a prior specification of the number of clusters. After the clustering procedure, the largest cluster is retained to extract the dominant pulse.

[0072] It can be seen that a dominant pulse is immune to noises of a

transient nature. However, dominant pulse clusters extracted from signal segments could still be artifactual because the complete segment it represents could be noise. For example, sensor detachment can cause several minutes or even hours of ICP recording to be invalid. In such cases, the dominant pulses should not be analyzed any further.

[0073] To identify legitimate dominant ICP pulses in an automated fashion, a reference library of validated ICP pulses is preferably used to aid the recognition of non-artifactual peaks at block 150. This library of reference ICP pulses is preferably constructed with legitimate pulses of divergent shapes. The library preferably uses data sets from many different patients. In one embodiment, a self-identification component is incorporated so that a non-artifactual ICP pulse that does not match a template found in the library is not falsely rejected. For example, a self-authentication may be created by further clustering the dominant pulses found in the first pass of the clustering analysis since a cluster formed by an artifactual dominant pulses will be less coherent that a cluster formed by non-artifactual pulses.

[0074] The input at block 150 is the sequence of dominant pulses identified for each consecutive sub-sequence of the signal segment being processed. This sequence may be further clustered. The average dominant pulse of each cluster is then subject to a matching test with each reference pulse found in the library with a correlation analysis. A dominant pulse is considered to be a non-artificial pulse if it belongs to a cluster that has an average pulse that correlates with any of the reference ICP pulses with a correlation coefficient greater than a selected value, for example, a correlation coefficient greater than n. To avoid the false rejection of a valid cluster because of the incompleteness of the reference library or

inappropriate n, those clusters that fail the first test will be further checked by comparing its self-coherence against r₂. Accordingly, the dominant pulses of the cluster that fails both checks will be excluded from further analysis in this embodiment.

[0075] Once a valid ICP pulse has been extracted and verified at block 150 a set of peak candidates (or curve inflections) are detected at block 160 of FIG. 5. Each candidate is potentially one of the three peaks. The extraction of these candidates relies on the segmentation of the ICP pulse form into concave and convex regions. This is preferably accomplished using the second derivative of the pulse.

[0076] Generally, peak locations may be found at block 160 using the

concave portions of the pulse curve according to four possible definitions in the embodiment shown. The first definition treats the intersection of a concave to a convex region as a peak if the first derivative of the concave portion is greater than zero, otherwise the intersection of a convex region to a concave region is the peak. The second definition is based on the curvature of the signal such that the peak is the location with maximal absolute curvature within each concave region, the third and the fourth definitions both involve a straight line linking the two end points of a concave region. According to the third and the fourth definitions, a peak can be found at the position where the perpendicular distance or the vertical distance from the ICP to this line is maximal, respectively.

[0077] Typically, a peak corresponds to the intersection of a convex to a concave region on a rising edge of ICP pulse or to the intersection of a concave to a convex region on the descending edge of the pulse. This detection process at block 160 produces a pool of N peak candidates

, 32 a_N ).

[0078] At block 180 of FIG. 5, the detected peaks are assigned. The

objective of block 180 is to obtain the best designation of the three well- recognized ICP peaks, denoted as Pi , P2 and P3, respectively, from an array of detected candidate peaks at block 60. Given Pi(aj), i = 1 , 2, 3 to denote the probability density functions (PDF) of assigning aj to the i-th peak (each PDF is a Gaussian distribution estimated from peak locations previously detected on a set of reference ICP pulses). In order to deal with missing peaks, an empty designation aO is added to the pool of candidates. In addition, to avoid false designation, MOCAIP uses a threshold q such that Pi(ak) = 0, i [ {1 , 2, 3}, k [ {1 , 2,..., N} if the probability of assigning ak to pi is less than q.

[0079] In an alternative embodiment, the detection and assignment of

peaks is accomplished with a regression model at block 170 instead of using unimodal priors during peak designation to improve the accuracy of the peak designation process.

[0080] Referring now to block 170 and block 180, a regression model y = f(x) is able to predict the most likely position of the three peaks, y = (p1 , p2, p3), given a segmented ICP pulse discretized as a vector x. Regression analysis is a statistical technique used for the numerical analysis between an input variable and an output variable. Different regression analysis methods may be used such as Multi-Linear Regression, Support vector machine (SVM) algorithm, spectral regression (SR) analysis, and extremely randomized decision trees.

[0081] During the peak assignment at block 180, the method exploits

Gaussian priors to infer the position of the three peaks from a set of peak candidates. Because large variations in the pulse morphology of the ICP signals exist the actual position of each of the three peaks is extremely variable. The complexity of data may lead to wrong or missed assignments in some instances.

[0082] In the alternative embodiment at block 170, the position (p1 , p2, p3) of the peaks is considered as a function f of the pulse signal. To this end, a regression model is exploited instead of the Gaussian priors during the peak designation to improve the accuracy of the process. One strength of using this model is that it exploits the values of the pulse itself during the peak assignment at block 180. Another advantage is the ability of the framework to exploit powerful machine learning algorithms.

[0083] Finally, the designated peaks at block 180 can be analyzed and

morphological features can be extracted at block 190 of FIG. 5. The various features can be used by treating physicians to evaluate the condition of the patient and make timely treatments to avoid potential future events.

[0084] FIG. 6 illustrates an exemplary system 200 for detection of cerebral vasodilatation and/or vasoconstriction via pulse morphological template matching. The system 200 includes one or more sensors 210 configured for acquiring signals representative of physiological characteristics of the subject patient. For measuring CBFV, sensor 210 may be configured to measure at middle cerebral artery using Transcranial Doppler. Other sensor configurations are contemplated according to the desired imaging modality (e.g. sensor 210 may be configured for acquiring intracranial pressure data). In one example, system 200 may comprise an add-on module to a current pulsatile physiological signal monitoring device, or may comprise a plug-in for a next-generation ICU monitoring system. One example of the latter is the intelligent ICU (ilCU) system.

[0085] The sensors 210 are coupled to a computer, server, or other

processing apparatus 220 comprising a processor 230 and memory 240 for storing application software 250. Application software 250 preferably comprises a training application software component 270 that identifies MOCAIP metrics (e.g. training method 10 of FIG. 2) indicative of

vasodilatation/ vasoconstriction via training data 290 (which may comprise a database or like storage medium). Template matching application software 280 is also included for matching the acquired sensor data to conditions characteristic of vasodilatation/ vasoconstriction. A module 260 comprising a MOCAIP algorithm 100, or the like, may be used quantitatively characterizing the shape of intracranial pulses for use in the training module 270 or template matching module 280.

[0086] Experimental Results

[0087] Studies were performed to validate the method of the present

invention using data recorded during physiological challenges known to cause cerebral vasodilatation and vasoconstriction.

[0088] The training dataset comprised of the CBFV and electrocardiograph (ECG) recordings of five female patients (21 , 24, 26, 32 and 54 years old), who were admitted at UCLA medical center for the evaluation of their chronic headaches.

[0089] During their hospitalization, the patients underwent a CO2 challenge test by inhaling a 5% CO2 mixture for less than 3 minutes. Simultaneous cardiovascular monitoring was also performed using bedside GE monitors and CBFV was measured using TCD machine (Multi-Dop X, Compumedics DWL, Singen, Germany). CBFV and ECG signals were recorded during CO2 test with few minutes preceding (baseline) and proceeding (post-test) at a sampling rate of 400 Hz using a mobile cart at the bedside that was equipped with the PowerLab TM SP-16 data acquisition system

(ADInstruments, Colorado Springs, CO).

[0090] Twenty nine healthy, consenting individuals (25 males and 4

females; 18 to 44 years) were instrumented to monitor partial pressure of end-tidal carbon dioxide (PETCO2) (Ametek CD-3A, AEI Technologies, Pitsburgh, PA USA), ECG (Bioamp, ADInstruments, Colorado Springs, CO USA) and middle cerebral artery (MCA) flow velocity (Multi-Dop T2, DWL Electronic Systems, Singen, Germany). Using Powerlab instrument (16SP, ADInstruments, Colorado Springs, CO, USA) signals were synchronized and recorded simultaneously at a sampling rate of 1 KHz, but later resampled at 400 Hz. After 6 min of rest, subjects underwent a series of tests in which fractional concentrations of inspired gases and breathing rate were manipulated to evaluate baseline and normoxic cerebral hemodyamic parameters, including vasoreactivity. Between tests, partial pressure of end-tidal oxygen (PETO2) returned to baseline values established at rest. The data collected during baseline (rest), hyperventilation and CO2 rebreathing tests were used. For hyperventilation, individuals were asked to breathe to a metronome at 18 breaths per minute for 2 minutes in order to reduce PETCO2 to approximately 20 mmHg. During the CO2 rebreathing test, subjects took 2 to 3 deep breaths of 7.5% CO2, 60% O2, balance N₂, then rebreathed through -15-L circuit until PETCO₂ rose to above 50 mmHg. Complete datasets were recorded in 27 subjects and used for further analysis.

[0091 ] The MOCAIP algorithm was applied to individual pulses in the

training and testing data segments and 128 CBFV pulse morphological metrics were extracted. The data segments corresponding to the rising edge of CBFV during the CO2 challenge test of 5 headache patients were treated as vasodilating episodes while data segments corresponding to the falling edge of CBFV during post-test measurements were analyzed as vasoconstricting episodes. The slope of the lines fitted to each of the extracted metrics over the rising and falling edges of CBFV signal were calculated, consistent vasodilatation and vasoconstriction metrics were derived and metrics trend vectors of V_diiatation and V_Constriction were constructed. Finally, the subset of indices for the metrics that are potential indicator of a vasodilatation or vasoconstriction event ( i ) was determined. These edges can be readily marked because the change of mean CBFV was significant from the baseline to the maximal value during CO₂ challenge and back from the maximal value to the baseline once patients started to breathe from the room air.

[0092] According to the training method 10 described above for FIG. 3 to build a PMTM template, a pre-set threshold is needed for establishing whether a MOCAIP metric trend exists. This is achieved by picking the threshold to correspond to the q-th percentile of all p values from line-fitting of all training segments. To explore the effect of q on the performance of the method of the present invention, we changed it as 10^th, 30^th, 50^th, 70^th and 90^th percentiles. At each q value, we sweep a threshold from 1 to 0 at a step size of 0.01 to obtain a binary outcome from VDI/VCI values. This results in conventional Receiver Operator Characteristics (ROC) curves for VDI and VCI, respectively. The following parameters were then calculated from a ROC curve: 1 ) the area under the ROC curve (AUC) , its standard deviation (SD) and 95% confidence interval (CI); 2) the partial area under the ROC curve for True Positive Rate (TPR) greater than 0.8 ( AUC_{0 8}); 3) the value of False Positive Rate (FPR) when TPR =0.80 (n_FPR ) .

Moreover, the operational point is determined from a ROC curve as the point on the curve closest to the point [0, 1]. The threshold value at this point will be used to provide a binary outcome for a given VDI/VCI value.

[0093] It is known that during a cerebral vasomotor reactivity test such as CO₂ rebreathing (hyperventilation), the dilatation (constriction) of cerebral vessels would result in a decrease (increase) of cerebrovascular resistance. To further evaluate the efficacy of the proposed indices (VDI and VCI) as a measure of vasoreactivity, we also studied the correlation of the calculated indices and cerebrovascular resistance index (CVRi); a conventional measure of cerebrovascular resistance. For this purpose, arterial blood pressure (ABP) pulses of the testing dataset were delineated similar to those of CBFV and then the ratios of beat-by-beat mean ABP and mean CBFV were obtained. ACVRi was computed as the change in the value of CVRi at the beginning and end of the test:

(ACVRi = CVRi|_end - CVRi|_begining). Eq. 4

[0094] Finally, the vector of calculated VDI (VCI) for all the subjects were correlated with that of ACVRi during CO₂ rebreathing (hyperventilation) and the Pearson correlation coefficients were obtained.

[0095] Since establishing the ground truth of the vasodilatation and

vasoconstriction cases via direct observation of arterial caliber changes is almost impossible, for the present work, the ground truth is determined based on the expected trend of change in P_ETCO₂ and CVRi during

ΔΡ CO

vasodilatation and vasoconstriction. For this purpose, ET^ 2 j_s computed as the change in the value of observed P_ETCO₂ at the beginning and end of the test (AP_EXCO₂ = P_ETCO₂ |_end - P_ETCO₂ |_begining) . In addition, ACVRi is calculated as described in further detail below. Then a data segment during CO₂ rebreathing is considered as a true vasodilatation case unless both P_ETCO₂ and CVRi for that data segment do not follow the expected trend of increasing and decreasing, respectively (^¾τ^0₂≤ 0 and ACVRi > 0 ). Similarly, a data segment during hyperventilation is considered as a true vasoconstriction case unless AP_ETCO2 > 0 and ACVRi < 0.

[0096] We compare the performance of the method of detection of vasoreactivity using VDIA/CI with that of two other conventional hemodynamic metrics, i.e. resistance area product (RAP) and critical closing pressure (CCP). For this purpose, beat-by-beat RAP and CCP values are calculated by applying first harmonic fitting technique to ABP and CBFV pulses of testing dataset. Then, ARAP and ACCP are computed by subtracting the RAP and CCP values at the beginning of the data segment from that at the end of the data segment. Finally, the ROC curves of vaso-reactivity are obtained by applying 100 equally spaced threshold points (swept from the minimum to maximum values of ARAP and ACCP) to compute binary outcomes for detection of vasodilatation and vasoconstriction. These ROC curves are compared with those of the method of the present invention.

[0097] The performance of using the template constructed from the

headache patients was compared to that from the normal subjects. This is done by fixing the number of training subjects to be 5 and then randomly sampling a sets of 5-subject training data from all qualified normal subjects, i.e., those with both validated episodes of vasodilatation and vasoconstriction. The performance of using the template from normal subjects was the average of the performance metrics from these a sets.

[0098] The influence of varying the number of subjects in the training

dataset on the size of the obtained template (number of consistent MOCAIP metrics in the template) and accuracy of vaso-reactivity detection was also tested. For this purpose, the data from headache patients and normal subjects was merged, and then an increasing number of training subjects was used to build the template and test it on the remaining subjects. Again, we randomly pick at most a sets of n-subject training data where n ranges from 1 to total number of qualified subjects. If the possible number of permutations is less than a , all permutations are used.

[0099] The duration of the collected CBFV data for the headache patients was (4.2 + 1.1 minute). This data included (0.4 + 0.2 minute) of resting baseline, (2.6 ± 0.6 minute) of CO2 challenge test and (1.2 ± 0.9 minute) of post-test measurements. The percentage of change in the mean CBFV value from resting to CO2 challenge test was 43.8% ± 5%, indicating a significant cerebral vasodilatation during the challenge test. The collected data for the healthy subjects included ( 5.7 + 1.3 min) of resting baseline, ( 4.1 + 0.7 min) of CO2 rebreathing and (2.4 + 0.7 min) of hyperventilation.

[00100] FIG. 7A shows a plot of the calculated VDI for testing data

segments of CO₂ rebreathing and baseline measurements over their corresponding level of change in PETCO2, and demonstrates that an increase in the level of PETCO2 is accompanied by an increase in the calculated VDI. (correlation coefficient of 0.82 and p < 10^"5). As the figure depicts, during CO₂ rebreathing test, PETCO₂ increases (for at least 5 mmHg) for all the subjects in the study, except for subject #4, whose data point is marked. As CO2 is a potent cerebral vasodilator, we conclude that the data segment of this subject during CO2 rebreathing is not a valid representation of a vasodilatation event. Excluding this invalid data segment.

[00101] FIG. 7B shows a plot of the ROC curves of vasodilatation detection, excluding this invalid data segment, and the related calculated parameters are presented in Table 1 . We observe that although the estimated area under the ROC curves for different values of q (as explained in subsection 2.5.1 ) are all above 0.97 and have small standard deviations, the method of he present invention seems to perform the best when q = 90^th percentile. The calculation of the cut-off point for the ROC curve of q = 90 results in FPR=0.107, TPR=0.923 and Ψ₀= 0.535.

[00102] FIG. 8A shows a plot of the calculated VCI for testing data segments of CO2 rebreathing and baseline measurements over their corresponding level of change in PETCO_2. AS FIG. 8A demonstrates, a decrease in the value of PETCO2 during hyperventilation results in an increase in the value of calculated VCI (correlation coefficient of -0.75 and p < 10^"5). However, 1 1 subjects had an unexpected increase in the level of PETCO2 during hyperventilation. It is believed that these subjects may not have given as much effort (breathing to a metronome at the target rate) which was needed to keep PETCO2 from drifting back up. Therefore, the data segments of these 1 1 subjects during hyperventilation could not be a valid

representative of a vasoconstriction episode. [00103] FIG. 8B shows a plot of the ROC curves of vasoconstriction detection, and the related calculated parameters are presented in Table 2. Excluding the invalid 1 1 data segments, the ROC curves of

vasoconstriction detection were obtained for the ROC curve of q = 90, the area under the curve is 0.897 ± 0.054 and the calculation of the cut-off point results FPR=0.105, TPR= 0.812 and Ψ₀= 0.535.

[00104] FIG. 9A and FIG. 9B show the results of evaluation of "counter- performance" of the method of the present invention using q = 90 and Ψ₀ = Ψ₀= 0.535. FIG. 9A is a plot of the hyperventilation data segments with validated vasoconstriction episodes. FIG. 9B is a plot of the hyperventilation data segments with validated vasodilatation episodes. The method of the present invention of vasodilatation is able to reject all 16 data segments with validated episodes of vasoconstriction, as all the calculated VDIs are at least 54% below the threshold Ψ_η. Similarly, since all the calculated VCIs are at least 66% below the threshold Ψο, the method of the present invention of vasoconstriction is able to reject all 26 data segments with validated episodes of vasodilatation.

[00105] It should be noted that both indices (VDI and VCI) are defined as a percentage of the same subset of metrics I (metrics which are a potential indicator of a vasodilatation or vasoconstriction event and are obtained from training dataset), so VDI +VCI < 1 . Then if the inquiry data segment is associated with a vasodilatation (or vasoconstriction) event, the VDI (or VCI) should be at least equal to preset threshold Ψ₀ (or Ψ₀), a number greater than 0.5 (ROC curves of FIG. 7B and FIG. 8B). Therefore, the calculated VCI (or VDI) for that inquiry data segment would be smaller than 0.5 and as a result, that inquiry data segment would be rejected by the vasoconstriction (or vasodilatation) test.

[00106] Following the method 30 of FIG. 4, the ground truth for detection of vasodilatation and vasoconstriction were determined by studying the trend of change of PETCO2 and cerebrovascular resistance index during CO2 rebreathing and hyperventilation episodes. This investigation revealed that during CO2 rebreathing test, P_ETCO2 increases (for at least 5 mmHg) for all the subjects in the study (FIG. 7A), except for subject #4 whose data point is highlighted with a bolder triangle on that figure.

[00107] Moreover, the calculated CVRi for this subject does not follow the expected decreasing trend during CO2 rebreathing (FIG. 10A, FIG. 10C). As CO2 is a potent cerebral vasodilator, we conclude that the data segment of this subject during CO2 rebreathing is not a valid representation of a vasodilatation event. Similarly, we observe that 1 1 subjects had an unexpected increase in the level of PETCO2 during hyperventilation

(highlighted with bolder triangle points on FIG. 8A). We believe that these subjects had difficulty with long periods of hyperventilation. They started the hyperventilation with a correct breathing rate, but after a while, they got fatigued and lightheaded. So they began to breathe more slowly to accommodate and as a result, their P_ETCO2 drifted back up. Investigating the trend of CVRi during hyperventilation reveals that in fact for 10 of these subjects, the calculated cerebrovascular resistance index also did not demonstrate an increasing trend expected during a vasoconstriction event (FIG. 10B and FIG. 10D show the P_ETCO₂ and CVRi for one of the 10 subjects). Therefore, the data segments of these 10 subjects during hyperventilation could not be a valid representative of a vasoconstriction episode. In summary, based on our determined ground truth, only 16 healthy subjects had both validated data segments of vasoconstriction and vasodilatation.

[00108] FIG. 1 1 A is a plot of the calculated vasodilatation index (VDI) over ACVRi during CO2 rebreathing. FIG. 1 1 B is a plot of the calculated vasoconstriction index (VCI) over ACVRi during hyperventilation. The plot of the calculated VDI (VCI) for testing data segments of CO2 rebreathing (hyperventilation) and baseline measurements over their corresponding level of change in CVRi (figure 4) shows that a higher change in the CVRi during CO₂ rebreathing (hyperventilation) would correspond to higher values of VDI (VCI). The correlation coefficient between VDI (VCI) and ACVRi during CO2 rebreathing (hyperventilation) is -0.74 (0.62) with p < 10^"5. [00109] FIG. 12A shows a comparison plot of vaso-reactivity detection performance using the VDI index, resistance area product (RAP) and critical closing pressure (CCP). FIG. 12B shows a comparison plot of vaso- reactivity detection performance using the VDI index, resistance area product (RAP) and critical closing pressure (CCP). As illustrated in FIG. 12A and FIG. 12B, the result of detection of vasodilatation and

vasoconstriction employing the proposed vaso-reactivity indices with q = 90 and two other conventional hemodynamic metrics (RAP and CCP). We observe that the ROC curve of the method of the present invention is above the other two. In fact, the areas under the ROC curves for detection of vasodilatation using the method of the present invention, RAP and CCP are 0.98, 0.91 and 0.65, respectively. For vasoconstriction, these areas are 0.90, 0.63 and 0.71 , respectively. Therefore, the method of the present invention outperforms the other two by at least 7% for vasodilatation and 19% for vasoconstriction.

[001 10] The investigation of the effect of the selection of the training dataset on the detection of cerebral vasodilatation with a = 300 resulted in

AUCvasodiiation = 0.97 ± 0.01 and AUCvasodiiation = 0.98 ± 0.01 , for the training dataset of 5 headache subjects (template size of 49 metrics) and 5 healthy subjects (average template size of 19 metrics), respectively. The same analysis for detection of vasoconstriction resulted in AUCconstriction = 0.90 ± 0.02 and AUCconstriction = 0.85 ± 0.02 for the training dataset of 5 headache subjects versus that of 5 healthy subjects. We observe that the selection of the healthy subjects for the training dataset may slightly improve the detection of vasodilatation, but it worsens the detection of vasoconstriction.

[001 1 1 ] FIG. 13A is a plot illustrating the effect of the size of the training dataset on the performance of the method of the present invention. As the number of subjects in the training dataset increases the accuracy of the detection of vasodilatation increases from 0.84 (when R = 1 ) to 0.92 (for R = 6), but then further enlargement of the training dataset does not affect the performance of detection. Similarly, the initial enlargement of the training dataset (up to R = 6) improves the accuracy of detection of vasoconstriction, and then the performance slightly degrades (with a rate less than 1 % per added subject) till R =13. For 13 < R < 16, the

performance enhances (by rate of 2% per added subject) and again starts to decrease with a slow rate of 1 % per added subject.

[001 12] FIG. 13B is a plot showing the size of the largest template obtained from a training dataset of n-subject (n=1 ,...,21 ). We observe that when all 21 subjects are included in the training dataset, our PMTM template would consist of only 2 MOCAIP metrics (mean ICP and diastolic pressure point). But by excluding one subject from the training dataset (in this case normal subject #15), the PMTM template size increases to 9 at its largest (for n = 20) and includes 6 metrics with an increasing/decreasing trend during vasodilatation/vasoconstriction (dP₂, dV₂, mICP, diasP, K1/RC1 , RC3/RC1 ) and 3 metrics with decreasing/increasing trend during

vasodilatation/vasoconstriction (dVi/dV₂, dPi/dV₂ and RC1/RC2). Further exclusion of another subject (normal subject #27) from the training dataset results in a larger PMTM template of 12 metrics (for n = 19) which includes three latency ratio metrics (L_VI PI/L_VI P3, Lvi pi Lpi_P3 and L_VI P3 LPI P3), in addition to the aforementioned 9 metrics. We conclude that these 12 metrics are good indicators of vasoreactivity. In fact, detection of vasodilatation and vasoconstriction employing the method of the present invention with this template (of 12 metrics) and the comparison threshold of (VDI or VCI > 0.57) results in accuracy of 94% and 88%, respectively. Please note that excluding more numbers of subjects from the training dataset would produce a larger template. However, aiming at obtaining a template made from a large training dataset with good accuracy for detection of vasoreactivity, like the aforementioned template of 12 metrics, seems to be a reasonable solution.

[001 13] Two CBFV-based novel indices (VDI and VCI) have been proposed to for real-time detection of cerebral arterial vasodilatation and

vasoconstriction, respectively. These two indices in essence quantify the matching degree between a template, which is a set of common CBFV pulse morphological metrics that consistently increase/decrease during a vasodilatation or vasoconstriction process for a set of training subjects, and the set of morphological metrics that increase/decrease for a testing CBFV segment. The results showed that these two indices can detect

vasodilatation and vasoconstriction with an accuracy of 0.92 and 0.82, respectively.

[00114] The PMTM method of the present invention differs fundamentally compared to existing approaches of characterizing cerebrovascular changes in terms of avoiding both simplified assumptions of the cerebral hemodynamics that are needed in model based approaches and

approximating cerebral arterial blood pressure using extracranial systemic blood pressure. Instead, the PMTM heavily depends on characterizing morphological changes of intracranial pulse waveforms to detect cerebrovascular changes. Intracranial pulses such as ICP and CBFV originate from the transmitted systemic pulses and are under the influence of the cerebrovascular changes. Acute changes in the diameter, tone, and compliance of the cerebrovascular are reflected in the changes of the pulse shape. However, an analytic description of how the shape changes are related to cerebrovascular changes does not exist. Hence, the PMTM uses a data-driven approach to establish a template that characterizes the expected changes of pulse morphology in terms of 128 metrics derived from MOCAIP analysis. In this way, the accuracy of detecting

cerebrovascular changes will only depend on the quality of the training data that implicitly define the changing patterns of pulse morphology in response to cerebrovascular changes, which are acute cerebral vasodilatation and vasoconstriction as studied in the present work.

[00115] It is appreciated that intracranial pulse morphological metrics are able to reflect cerebral vascular and hemodynamic changes. Using ICP pulse waveforms to classify cerebral perfusion status, it was found that elevation of the third peak of an ICP pulse may be associated with low global CBF. The existence of consistent changes of ICP pulse waveforms have been found to be induced by hypercapnic cerebral vasodilatation. It is appreciated that pulse waveform changes may be used to infer cerebral vascular changes, and in particular with association between ICP pulse waveform changes and cerebral Lactate Pyruvate ratio (LPR) increase. The systems and methods of the present invention use an algorithm to conduct the detection of vasodilatation and vasoconstriction using only intracranial pulse morphological features.

[00116] In conclusion, performance of the PMTM was rigorously studied using two independent data sets. Using the template built from five patients with severe headache undergoing CO2 challenge test, the performance of detecting vasodilatation for 27 normal subjects achieved a TPR of 0.92 and a FPR of 0.10. Detecting vasoconstriction has an inferior TPR of 0.82. It is inherently challenging to establish the ground truth of the vasodilatation and vasoconstriction cases because a direct observation of arterial caliber changes is almost impossible.

[00117] The system and methods of the present invention are premised on the physiological influence of CO₂ on dilating the cerebral arteries to identify true vasodilatation cases. However, it is more ambiguous to identify true vasoconstriction cases from the hyperventilation dataset. Therefore a conventional metric CVRi is used as a cross-reference to filter out some obviously false vasoconstriction cases. This practice may result in a large amount of uncertain vasoconstriction cases and eventually affect the performance of VCI.

[00118] It is also appreciated that the proposed approach will never

misclassify a vasodilatation case for a vasoconstriction case and vice versa. This can be guaranteed by using a threshold greater than 0.5 on VDI and VCI because the summation of VDI and VCI is strictly less or equal to one. This means that an inquiry data segment can only be judged to be either a vasodilatation case or a vasoconstriction case. Indeed, ROC curve analysis shows that the operating threshold for both VDI and VCI is slightly greater than 0.5.

[00119] Employing the training dataset of headache subjects (versus healthy subjects) resulted in a higher performance of detection of vasoconstriction, and did not significantly affect that of the vasodilatation. While the template obtained from the training dataset of five headache subjects included 49 MOCAIP metrics, the average number of such metrics (over 300 selected copies of trainings) for 5 healthy subjects was 19.13 ± 7.1 1 . This result indicates that the changes of CBFV pulse morphology in response to vasodilatation/vasoconstriction in the headache subjects were more consistent than those of the healthy subjects. Moreover, it is possible that even some of the 16 normal subjects with the expected decreasing trend in the level of PETCO2 were unable to maintain the target rate and/or depth of breathing during the hyperventilation phase to cause a significant vasoconstriction (e.g. subject #15 and 27 from FIG. 13B). Therefore, training datasets of healthy subjects (specially the vasoconstriction segments) may not result in an accurate representation of the pulse morphology templates and this can cause a degraded performance for detection of vasoconstriction.

[00120] Enlargement of the training dataset may have a two-fold effect on the performance of the detection method of the present invention; adding more subjects to the training dataset may increase the accuracy of detection of vasodilatation/vasoconstriction by diversifying the training set, but it may also impair the performance by limiting the number of metrics in the PMTM template (as the training dataset become larger, the number of consistent metrics over all subjects decreases). Results show that size of the training dataset does not affect the good performance of the detection of vasodilatation as long as it includes the data of at least 5 subjects.

Moreover, although the performance of detection of vasoconstriction may slightly change depending on the size of the training dataset, the level of change is small (less than 1 % decrease in accuracy per added subject).

[00121 ] Finally, the PMTM template of 12 metrics (dP₂, dV₂, mICP, diasP, K-|/RCi, RCs/RC-i, I_VI PI/LVI P3, LVI PI LPI P3, l_vi P3/Lpi P3, dV-|/dV2, dPi dV2 and RC1/RC2) was obtained from a training dataset of 19 subjects and demonstrated a high accuracy in detection of vasoreactivity. Therefore, this template is a recommended template to be prospectively evaluated for future studies. [00122] Embodiments of the present invention may be described with reference to flowchart illustrations of methods and systems according to embodiments of the invention, and/or algorithms, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of a flowchart, and combinations of blocks (and/or steps) in a flowchart, algorithm, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and/or software including one or more computer program instructions embodied in computer-readable program code logic. As will be appreciated, any such computer program instructions may be loaded onto a computer, including without limitation a general purpose computer or special purpose computer, or other programmable processing apparatus to produce a machine, such that the computer program instructions which execute on the computer or other programmable processing apparatus create means for implementing the functions specified in the block(s) of the flowchart(s).

[00123] Accordingly, blocks of the flowcharts, algorithms, formulae, or

computational depictions support combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and computer program instructions, such as embodied in computer-readable program code logic means, for performing the specified functions. It will also be understood that each block of the flowchart illustrations, algorithms, formulae, or computational depictions and combinations thereof described herein, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer-readable program code logic means.

[00124] Furthermore, these computer program instructions, such as

embodied in computer-readable program code logic, may also be stored in a computer-readable memory that can direct a computer or other programmable processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s). The computer program instructions may also be loaded onto a computer or other programmable processing apparatus to cause a series of operational steps to be

performed on the computer or other programmable processing apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable processing apparatus provide steps for implementing the functions specified in the block(s) of the flowchart(s), algorithm(s), formula(e), or computational depiction(s).

[00125] From the discussion above it will be appreciated that the invention can be embodied in various ways, including the following:

[00126] 1 . A method for real-time assessment of vaso-reactivity of a cerebral vasculature of a patient, comprising: acquiring an intracranial pulse signal from the patient; generating a pulse morphological template associated with the intracranial pulse signal; generating a vaso-reactivity index for an inquiry data segment as a function of the generated pulse morphological template; and comparing the vaso-reactivity index against a threshold value to detect a vaso-reactivity event associated with the inquiry data segment.

[00127] 2. The method of any previous embodiment: wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of an intracranial pulse; and wherein a plurality of MOCAIP metrics are generated from the intracranial pulse signal.

[00128] 3. The method of any previous embodiment, further comprising: acquiring a training data set to identify a set of MOCAIP metrics that are indicative of a vaso-reactivity event; wherein the pulse morphological template is a function of trends of change over the inquiry data segment similar to that of the identified set of MOCAIP metrics.

[00129] 4. The method of any previous embodiment: wherein the vaso- reactivity event comprises a vasodilatation event; wherein the vaso- reactivity index comprises a vasodilatation index; and wherein the

vasodilatation index represents a likelihood of a vasodilatation event associated with the inquiry data segment.

[00130] 5. The method of any previous embodiment: wherein the vaso- reactivity event comprises a vasoconstriction event; wherein the vaso- reactivity index comprises a vasoconstriction index; and wherein the vasoconstriction index represents a likelihood of a vasoconstriction event associated with the inquiry data segment.

[00131] 6. The method of any previous embodiment, wherein the pulse

morphological template comprises a set of MOCAIP metrics that consistently increase or decrease during vasodilatation events but have an opposite decrease or increase during vasoconstriction events.

[00132] 7. The method of any previous embodiment, wherein the pulse

morphological template comprises a cerebral blood flow velocity (CBFV) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to cerebral blood flow velocity (CBFV) data collected during the inquiry data segment.

[00133] 8. The method of any previous embodiment, wherein the pulse

morphological template comprises an intracranial pressure (ICP) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to intracranial pressure (ICP) data collected during the inquiry data segment.

[00134] 9. The method of any previous embodiment, wherein a monotonic trending function is used to determine a direction of changes associated with each of the MOCAIP metrics.

[00135] 10. A system for real-time assessment of vaso-reactivity of a

cerebral vasculature of a patient, comprising: (a) a processor; and (b) programming executable on the processor for: (i) acquiring an intracranial pulse signal from the cerebral vasculature; (ii) generating a pulse morphological template associated with the intracranial pulse signal; (iii) generating a vaso-reactivity index for an inquiry data segment as a function of the generated pulse morphological template; and (iv) comparing the vaso-reactivity index against a threshold value to detect a vaso-reactivity event. [00136] 1 1 . The system of any previous embodiment: wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of an intracranial pulse associated with the cerebral vasculature; and wherein a plurality of MOCAIP metrics are generated from the intracranial pulse signal.

[00137] 12. The system of any previous embodiment, further said

programming is further configured for: acquiring a training data set to identify set of MOCAIP metrics that are indicative of a vaso-reactivity event; wherein the pulse morphological template is a function of trends of change over the inquiry data segment similar to that of the identified set of MOCAIP metrics.

[00138] 13. The system of any previous embodiment: wherein the vaso- reactivity event comprises a vasodilatation event; wherein the vaso- reactivity index comprises a vasodilatation index; and wherein the vasodilatation index represents a likelihood of a vasodilatation event associated with the inquiry data segment.

[00139] 14. The system of any previous embodiment: wherein the vaso- reactivity event comprises a vasoconstriction event; wherein the vaso- reactivity index comprises a vasoconstriction index; and wherein the vasoconstriction index represents a likelihood of a vasoconstriction event associated with the inquiry data segment.

[00140] 15. The system of any previous embodiment, wherein the pulse

morphological template comprises a set of MOCAIP metrics that

consistently increase or decrease during vasodilatation events but have an opposite decrease or increase during vasoconstriction events.

[00141] 16. The system of any previous embodiment, wherein the pulse

morphological template comprises a cerebral blood flow velocity (CBFV) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to cerebral blood flow velocity (CBFV) data collected during the inquiry data segment.

[00142] 17. The system of any previous embodiment, wherein the pulse

morphological template comprises an intracranial pressure (ICP) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to intracranial pressure (ICP) data collected during the inquiry data segment.

[00143] 18. The system of any previous embodiment, wherein a monotonic trending function is used to determine a direction of changes associated with each of the MOCAIP metrics.

[00144] 19. The system of any previous embodiment, further comprising: a sensor configured for acquiring the inquiry data segment.

[00145] 20. A monitor for real-time assessment of vaso-reactivity of a

cerebral vasculature of a patient, comprising: (a) a processor; (b) a sensor configured for acquiring an inquiry data segment from the patient; and (c) programming executable on the processor for: (i) acquiring an intracranial pulse signal relating to the inquiry data segment; (ii) generating a pulse morphological template associated with the intracranial pulse signal; (iii) generating a vaso-reactivity index for the inquiry data segment as a function of the generated pulse morphological template; and (iv) comparing the vaso-reactivity index against a threshold value to detect a vaso- reactivity event associated with the cerebral vasculature.

[00146] 21 . The monitor of any previous embodiment: wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of an intracranial pulse; and wherein a plurality of MOCAIP metrics are generated from the intracranial pulse signal.

[00147] 22. The monitor of any previous embodiment, further said

programming is further configured for: acquiring a training data set to identify a set of MOCAIP metrics that are indicative of a vaso-reactivity event; and wherein the pulse morphological template is a function of trends of change over the inquiry data segment similar to that of the identified set of MOCAIP metrics.

[00148] 23. The monitor of any previous embodiment: wherein the vaso- reactivity event comprises a vasodilatation event; wherein the vaso- reactivity index comprises a vasodilatation index; and wherein the vasodilatation index represents a likelihood of a vasodilatation event associated with the inquiry data segment.

[00149] 24. The monitor of any previous embodiment: wherein the vaso- reactivity event comprises a vasoconstriction event; wherein the vaso- reactivity index comprises a vasoconstriction index; and wherein the vasoconstriction index represents a likelihood of a vasoconstriction event associated with the inquiry data segment.

[00150] 25. The monitor of any previous embodiment, wherein the pulse

morphological template comprises a set of MOCAIP metrics that

consistently increase or decrease during vasodilatation events but have an opposite decrease or increase during vasoconstriction events.

[00151] 26. The monitor of any previous embodiment, wherein the pulse

morphological template comprises a cerebral blood flow velocity (CBFV) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to cerebral blood flow velocity (CBFV) data collected during the inquiry data segment.

[00152] 27. The monitor of any previous embodiment, wherein the pulse

morphological template comprises an intracranial pressure (ICP) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to intracranial pressure (ICP) data collected during the inquiry data segment.

[00153] 28. The monitor of any previous embodiment, wherein a monotonic trending function is used to determine a direction of changes associated with each of the MOCAIP metrics.

[00154] Although the description above contains many details, these should not be construed as limiting the scope of the invention but as merely providing illustrations of some of the presently preferred embodiments of this invention. Therefore, it will be appreciated that the scope of the present invention fully encompasses other embodiments which may become obvious to those skilled in the art, and that the scope of the present invention is accordingly to be limited by nothing other than the appended claims, in which reference to an element in the singular is not intended to mean "one and only one" unless explicitly so stated, but rather "one or more." All structural, chemical, and functional equivalents to the elements of the above-described preferred embodiment that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the present claims. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the present invention, for it to be encompassed by the present claims. Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. No claim element herein is to be construed under the provisions of 35 U.S.C. 1 12, sixth paragraph, unless the element is expressly recited using the phrase "means for."

Table 1

The calculated parameters for the ROC curves of detection of vasodilatation (FIG. 7B) applying the proposed method on the testing dataset. ^*Area under the ROC curve. ^**95% confidence interval. ^xPartial area under the ROC curve for 0.8 <TPR<1. ^xx Value of FPR when TPR = 0.8.

Table 2

The calculated parameters for the ROC curves of detection of vasoconstriction (FIG. 8B) applying the proposed method on the testing dataset. ^*Area under the ROC curve. ^**95% confidence interval. ^xPartial area under the ROC curve for 0.8≤TPR<1. ^xxValue of FPR when TPR = 0.8.

Claims

CLAIMS What is claimed is:

1 . A method for real-time assessment of vaso-reactivity of a cerebral vasculature of a patient, comprising:

acquiring an intracranial pulse signal from the patient;

generating a pulse morphological template associated with the intracranial pulse signal;

generating a vaso-reactivity index for an inquiry data segment as a function of the generated pulse morphological template; and

comparing the vaso-reactivity index against a threshold value to detect a vaso-reactivity event associated with the inquiry data segment.

2. A method as recited in claim 1 :

wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of an intracranial pulse; and

wherein a plurality of MOCAIP metrics are generated from the intracranial pulse signal.

3. A method as recited in claim 2, further comprising:

acquiring a training data set to identify a set of MOCAIP metrics that are indicative of a vaso-reactivity event;

wherein the pulse morphological template is a function of trends of change over the inquiry data segment similar to that of the identified set of MOCAIP metrics.

4. A method as recited in claim 3:

wherein the vaso-reactivity event comprises a vasodilatation event;

wherein the vaso-reactivity index comprises a vasodilatation index; and wherein the vasodilatation index represents a likelihood of a vasodilatation event associated with the inquiry data segment.

5. A method as recited in claim 4:

wherein the vaso-reactivity event comprises a vasoconstriction event; wherein the vaso-reactivity index comprises a vasoconstriction index; and wherein the vasoconstriction index represents a likelihood of a

vasoconstriction event associated with the inquiry data segment.

6. A method as recited in claim 5, wherein the pulse morphological template comprises a set of MOCAIP metrics that consistently increase or decrease during vasodilatation events but have an opposite decrease or increase during vasoconstriction events.

7. A method as recited in claim 1 , wherein the pulse morphological template comprises a cerebral blood flow velocity (CBFV) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to cerebral blood flow velocity (CBFV) data collected during the inquiry data segment.

8. A method as recited in claim 1 , wherein the pulse morphological template comprises an intracranial pressure (ICP) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to intracranial pressure (ICP) data collected during the inquiry data segment.

9. A method as recited in claim 3, wherein a monotonic trending function is used to determine a direction of changes associated with each of the MOCAIP metrics.

10. A system for real-time assessment of vaso-reactivity of a cerebral vasculature of a patient, comprising:

(a) a processor; and

(b) programming executable on the processor for:

(i) acquiring an intracranial pulse signal from the cerebral vasculature;

(ii) generating a pulse morphological template associated with the intracranial pulse signal;

(iii) generating a vaso-reactivity index for an inquiry data segment as a function of the generated pulse morphological template; and

(iv) comparing the vaso-reactivity index against a threshold value to detect a vaso-reactivity event.

1 1 . A system as recited in claim 10:

wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of an intracranial pulse associated with the cerebral vasculature; and

wherein a plurality of MOCAIP metrics are generated from the intracranial pulse signal.

12. A system as recited in claim 1 1 , further said programming is further configured for:

acquiring a training data set to identify set of MOCAIP metrics that are indicative of a vaso-reactivity event;

wherein the pulse morphological template is a function of trends of change over the inquiry data segment similar to that of the identified set of MOCAIP metrics.

13. A system as recited in claim 12:

wherein the vaso-reactivity event comprises a vasodilatation event;

wherein the vaso-reactivity index comprises a vasodilatation index; and wherein the vasodilatation index represents a likelihood of a vasodilatation event associated with the inquiry data segment.

14. A system as recited in claim 13:

wherein the vaso-reactivity event comprises a vasoconstriction event;

wherein the vaso-reactivity index comprises a vasoconstriction index; and wherein the vasoconstriction index represents a likelihood of a

vasoconstriction event associated with the inquiry data segment.

15. A system as recited in claim 14, wherein the pulse morphological template comprises a set of MOCAIP metrics that consistently increase or decrease during vasodilatation events but have an opposite decrease or increase during vasoconstriction events.

16. A system as recited in claim 10, wherein the pulse morphological template comprises a cerebral blood flow velocity (CBFV) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to cerebral blood flow velocity (CBFV) data collected during the inquiry data segment.

17. A system as recited in claim 10, wherein the pulse morphological template comprises an intracranial pressure (ICP) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to intracranial pressure (ICP) data collected during the inquiry data segment.

18. A system as recited in claim 12, wherein a monotonic trending function is used to determine a direction of changes associated with each of the MOCAIP metrics.

19. A system as recited in claim 10, further comprising:

a sensor configured for acquiring the inquiry data segment.

20. A monitor for real-time assessment of vaso-reactivity of a cerebral vasculature of a patient, comprising:

(a) a processor;

(b) a sensor configured for acquiring an inquiry data segment from the patient; and (c) programming executable on the processor for:

(i) acquiring an intracranial pulse signal relating to the inquiry data segment;

(ii) generating a pulse morphological template associated with the intracranial pulse signal;

(iii) generating a vaso-reactivity index for the inquiry data segment as a function of the generated pulse morphological template; and

(iv) comparing the vaso-reactivity index against a threshold value to detect a vaso-reactivity event associated with the cerebral vasculature.

21 . A monitor as recited in claim 20:

wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of an intracranial pulse; and

wherein a plurality of MOCAIP metrics are generated from the intracranial pulse signal.

22. A monitor as recited in claim 21 , further said programming is further configured for:

acquiring a training data set to identify a set of MOCAIP metrics that are indicative of a vaso-reactivity event; and

wherein the pulse morphological template is a function of trends of change over the inquiry data segment similar to that of the identified set of MOCAIP metrics.

23. A monitor as recited in claim 22:

wherein the vaso-reactivity event comprises a vasodilatation event;

wherein the vaso-reactivity index comprises a vasodilatation index; and wherein the vasodilatation index represents a likelihood of a vasodilatation event associated with the inquiry data segment.

24. A monitor as recited in claim 23:

wherein the vaso-reactivity event comprises a vasoconstriction event; wherein the vaso-reactivity index comprises a vasoconstriction index; and wherein the vasoconstriction index represents a likelihood of a

vasoconstriction event associated with the inquiry data segment.

25. A monitor as recited in claim 24, wherein the pulse morphological template comprises a set of MOCAIP metrics that consistently increase or decrease during vasodilatation events but have an opposite decrease or increase during vasoconstriction events.

26. A monitor as recited in claim 20, wherein the pulse morphological template comprises a cerebral blood flow velocity (CBFV) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to cerebral blood flow velocity (CBFV) data collected during the inquiry data segment.

27. A monitor as recited in claim 20, wherein the pulse morphological template comprises an intracranial pressure (ICP) pulse morphological template obtained by applying a morphological clustering and analysis of an intracranial pulse (MOCAIP) to intracranial pressure (ICP) data collected during the inquiry data segment.

28. A monitor as recited in claim 22, wherein a monotonic trending function is used to determine a direction of changes associated with each of the MOCAIP metrics.