WO2014055797A1

WO2014055797A1 - Steady-state indicator of intracranial pressure using geodesic distance of icp pulse waveforms

Info

Publication number: WO2014055797A1
Application number: PCT/US2013/063324
Authority: WO
Inventors: Xiao Hu
Original assignee: The Regents Of The University Of California
Priority date: 2012-10-03
Filing date: 2013-10-03
Publication date: 2014-04-10

Abstract

A steady-state indicator for the intracranial pressure (ICP) dynamic system that uses a Morphological Clustering and Analysis of Intracranial Pulse (MOCAIP) algorithm to extract artifact-free dominant pulses, each of which is representative of a short recording interval of ICP is presented. In a preferred embodiment, detecting morphological changes between waveforms is achieved with a unique distance metric, the geodesic distance, as a metric for measuring the inter-pulse distance that is sensitive to the low-dimensional geometric manifold where the time series of ICP pulses reside.

Description

STEADY-STATE INDICATOR OF INTRACRANIAL PRESSURE USING GEODESIC DISTANCE OF ICP PULSE WAVEFORMS

CROSS-REFERENCE TO RELATED APPLICATIONS

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

application serial number 61/709,254 filed on October 3, 2012, incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with Government support under Grant Nos.

NS059797, NS066008, and NS076738 awarded by the National Institutes of Health (NIH).

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 steady-state monitoring of cerebral vasculature.

[0007] 2. Description of Related Art

[0008] Disorders afflicting the brain can perturb homeostasis of cerebral perfusion, cerebral metabolism, and neuroelectrical activities. For a clinician caring for an afflicted patient in the acute period of traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), and hydrocephalus, proper management ideally entails early detection of developing alterations from equilibrium. This is particularly challenging in comatose patients, in which there may be a paucity of clinical indicators and restricted availability to obtain imaging diagnostic tests.

[0009] Using the terminology of systems engineering, in a normal healthy state various functional and anatomical sub-systems of the normal brain are in steady state. In particular, normal functioning of the brain depends on the homeostasis (~ steady state) of its various physiological sub-systems, one of which is the intracranial pressure (ICP) dynamic system.

[0010] However, the status quo of ICP monitoring solely targets mean ICP.

[0011] Accordingly, an object of the present invention is early detection of deviation from steady state of these systems, which may facilitate

recognition of pathological changes of the brain prior to crisis states. BRIEF SUMMARY OF THE INVENTION

[0012] One aspect is a system and method configured to analyze

continuous intracranial pressure (ICP) pulse waveforms to detect deviation of the ICP dynamic system from a steady-state due to acute intracranial changes such as hydrocephalus, brain injury, and mass effect.

[0013] Another aspect is a method of direct detection of the deviation of ICP dynamics from a steady state, and in particular, a steady-state indicator that can accommodate different metrics of inter-pulse distance and different statistics of the distance histograms.

[0014] Another aspect is a software module for ICP monitors or neurocritical care monitoring system configured to analyze continuous intracranial pressure (ICP) pulse waveforms to detect deviation of the ICP dynamic system from a steady-state. In one embodiment, the system is capable of detecting acute ventricular changes.

[0015] A further aspect is a steady-state indicator for the intracranial

pressure (ICP) dynamic system. In one embodiment, the steady-state indicator is derived by characterizing diversity of the morphologies of ICP pulses at similar mean ICP levels. In a further embodiment, the method of the present invention avoids assessing morphology of individual pulses on a beat-by-beat basis, and instead uses a Morphological Clustering and Analysis of Intracranial Pulse (MOCAIP) algorithm to extract artifact-free dominant pulses, each of which is representative of a short recording interval of ICP.

[0016] In a preferred embodiment, detecting morphological changes

between waveforms is achieved with a unique distance metric, the geodesic distance, as a metric for measuring the inter-pulse distance that is sensitive to the low-dimensional geometric manifold where the time series of ICP pulses reside.

[0017] 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)

[0018] The invention will be more fully understood by reference to the

following drawings which are for illustrative purposes only:

[0019] FIG. 1 A is a plot of a single pressure volume curve as a first-order approximation for an ICP dynamic system in a steady state.

[0020] FIG. 1 B is a plot of an ICP dynamic system undergoing acute changes.

[0021] FIG. 2 illustrates a schematic flow diagram of an exemplary

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

[0022] FIG. 3 shows an exemplary refined ICP pulse with metrics that can be extracted and monitored according to the present invention.

[0023] FIG. 4 shows an array of metrics (n = 128) associated with the ICP pulse of FIG. 3 to comprehensively characterize the amplitude, curvature, slope, and time-intervals among peaks and troughs of pulses.

[0024] FIG. 5 is a schematic flow diagram of a method to derive steady- state indicator for the intracranial pressure dynamic system of the present invention.

[0025] FIG. 6 shows a two dimensional projection of dominant ICP pulses using the local linear embedding (LLE) algorithm in accordance with the present invention.

[0026] FIG. 7 illustrates a schematic diagram of an exemplary system for direct detection of the deviation of ICP dynamics from a steady state.

[0027] FIG. 8A through FIG. 8D show twelve box-plots for one-way ANOVA group analysis of 12 different steady-state indicators in differentiating among NPH, SVS+, and SVS- groups displayed as Geodesic distance,

Euclidean distance, Pearson correlation coefficient, and mean ICP based results, respectively.

[0028] FIG. 9A through FIG. 9C show histograms of the four metrics

including inter-pulse geodesic distance, Euclidean distance, correlation coefficient, and mean ICP corresponding to one example from each of three groups SVS+, SVS-, and NPH, respectively.

[0029] FIG. 10A through FIG. 10D show plots of two SVS+ (FIG. 10A and

FIG. 10B) and two NPH (FIG. 10C and FIG. 10D) cases with multimodal histograms of inter-pulse geodesic distance. DETAILED DESCRIPTION OF THE INVENTION

[0030] FIG. 1 A and FIG. 1 B provide an illustration of a basis of the

intracranial pressure (ICP) dynamic system steady state detection method of the present invention.

[0031] FIG. 1 A displays a single pressure volume curve as a first-order approximation for an ICP dynamic system in a steady state, and in particular, a single pressure-volume (PV) curve. The operating point of the system for any given mean ICP corresponds to different points along this single PV curve. Points A and B on this curve have different mean ICP and hence, a different ICP pulse waveform amplitude and morphology.

However, pulse amplitude and waveform morphology will be the same for a given mean ICP. In other words, individual pulses move along this curve as mean ICP oscillates in the form of B waves etc.

[0032] FIG. 1 B shows an ICP dynamic system undergoing acute changes (e.g. occurrence of brain structural alternations). As shown in FIG. 1 B, when such structural alternations occur, the system follows different pressure volume curves at different time points so that the pulses with same mean ICP will likely have different amplitude and waveform

morphology because the system now jumps between different pressure volume curves. Therefore, ICP pulses at a similar mean ICP level resemble each other when the ICP dynamic system is in a steady state because they correspond to the same operating point of the ICP dynamic system. However, if the intracranial pressure dynamics change, the ICP pulse morphology will differ from the steady-state form assessed at the same mean ICP, reflecting a different operating point on the new PV curve.

[0033] FIG. 2 illustrates a schematic flow diagram of an exemplary

morphological clustering and analysis of an intracranial pulse (MOCAIP) algorithm 10 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. [0034] MOCAIP algorithm 10 comprises an automatic analysis of intracranial pulse morphology by designating the locations of three well- established sub-peaks in these pulses. MOCAIP 10 starts by detecting individual pulses from a continuous raw signal using an algorithm

developed in our previous work. Consecutive sequences of these raw pulses are clustered to find the biggest cluster. The average pulse of this cluster is termed a dominant pulse. A library of validated reference pulses is then used to recognize legitimate dominant pulses, and only these legitimate ones are further processed to detect candidates of sub-peaks. Finally, the distribution of the sub-peak positions in the same reference pulse library is leveraged to designate the candidate peaks to each of the three sub-peaks or a non-peak.

[0035] In the steady-state indicator 100 of FIG. 5 described in further detail below, the MOCAIP algorithm 10 is used on consecutive 30-second ICP segments to extract a dominant pulse, determine whether the dominant pulse is legitimate, and then designate the sub-peaks of legitimate dominant pulses.

[0036] Referring to FIG. 3, 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).

[0037] As shown in FIG. 4, 128 pulse morphological metrics are extracted using the identified peaks and troughs.

[0038] As seen in FIG. 2, intracranial pulse signals are acquired at block 20 from a sensor (e.g. sensor 210 of system 200 shown in FIG. 7) using conventional methods of installation. Signals produced from the sensors 210 are preferably recorded and analyzed via a processor 230, or may be stored in memory 240 of a computer and analyzed in real time without recording in the alternative. In the preferred embodiment, a series of dominant pulses are acquired for the given ICP recording. [0039] The acquired pulse signal data at block 20 is processed with a number of process steps to eliminate noise and to refine the peaks for analysis at block 90. In the embodiment shown in FIG. 2, a system with five major components is provided including a beat-by-beat pulse detection component 30, a pulse clustering component 40, a non-artifactual pulse recognition component 50, a peak detection component 60, and an optimal peak designation component 80. In addition, the algorithm makes use of a library of reference 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 30 is preferably conducted using an algorithm for extracting intracranial pressure latency relative to an electrocardiogram r wave, as understood in the art.

[0040] 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 40. This process results in a sequence of dominant ICP pulses that is further analyzed by the pulse recognition component 50. 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.

[0041] Referring now to block 30, 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.

[0042] 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. These artifacts and noise are common for typical ICP recordings and can interfere with the analysis of ICP pulse morphology.

[0043] 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 40. 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.

[0044] In one embodiment at block 40, 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.

[0045] In one embodiment, a hierarchical clustering approach is used to cluster ICP pulses at block 40 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.

[0046] 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.

[0047] 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 50. 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 pulse will be less coherent than a cluster formed by a non-artifactual pulse.

[0048] The input at block 50 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-artifactual 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 fail both checks will be excluded from further analysis in this embodiment. [0049] Once a valid ICP pulse has been extracted and verified at block 50, a set of peak candidates (or curve inflections) are detected at block 60 of FIG. 2. 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.

[0050] Generally, peak locations may be found at block 60 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.

[0051] Typically, a peak corresponds to the intersection of a convex to a concave region on a rising edge of an 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 60 produces a pool of N peak candidates

[0052] At block 80 of FIG. 2, the detected peaks are assigned. The

objective of block 80 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.

[0053] In an alternative embodiment, the detection and assignment of

peaks is accomplished with a regression model at block 70 instead of using unimodal priors during peak designation to improve the accuracy of the peak designation process.

[0054] Referring now to block 70 and block 80, 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.

[0055] During the peak assignment at block 80, 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 the data may lead to wrong or missed

assignments in some instances.

[0056] In the alternative embodiment at block 70, 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 advantage of using this model is that it exploits the values of the pulse itself during the peak assignment at block 80. Another advantage is the ability of the framework to exploit powerful machine learning algorithms.

[0057] Finally, the designated peaks at block 80 can be analyzed and

morphological features can be extracted at block 90 of FIG. 2. 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.

[0058] Further refinement of each of MOCAIP algorithm 10 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 10 can be reliably applied to process continuous signal recordings from real clinical environment to extract useful morphological features of the corresponding pulses.

[0059] Further details regarding MOCAIP method 10 may be found with reference to 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 , incorporated by reference in its entirety.

[0060] FIG. 5 is a schematic flow diagram of method 100 to calculate a

steady-state indicator for a continuous ICP recording of certain duration. The basic premise is that it is a necessary condition for ICP pulses at similar mean ICP levels to resemble each other so that the intracranial pressure dynamic system is at a steady state. Therefore, method 100 is configured to accommodate different ways of characterizing the degree of similarity between two pulses given a matching mean ICP. Method 100 starts by using the MOCAIP algorithm at block 10 to form a series of dominant pulses 1 10 for the given ICP recording. This pre-processing step is crucial in that it isolates the artifacts and noises in the ICP recording. Let us assume these pulses are denoted as x_i; i = 1, 2,^■■■ , N. Then for each dominant pulse x;, we find other pulses xj which satisfy the following:

|xi - Xj | < AICP,j≠ i Eq. 1 where ¾ and xj are the mean values of Xi and xj, respectively.

[0061] A similarity or distance measure between x; and all xj is then

calculated using one or more of blocks 120, 122 and/or 124. Repeating this calculation for all x;, one obtains a long vector of similarity/distance metrics between pulses of matched ICP. The length of this vector is hence upper- bounded by N². The following three measures were tested: 1 ) Inter-pulse Euclidean Distance 122; 2) Inter-pulse Pearson Correlation Coefficient 124; and 3) Inter-pulse Geodesic Distance. The calculation of the geodesic distance 120 will be described in further detail below with reference to FIG. 6. It is noted that the pulses are required to have equal length for

calculating the above three metrics. Given the variable heart rates, this is handled by first defining the end of the pulse to be at a point on the last descending edge of a pulse where its amplitude equals Me of the peak amplitude of the last peak. By this definition, the pulse length is smaller than that of the actual pulse length of each pulse. Then the final pulse for distance calculation is obtained via histogram 126 and by using the 95- percentile of this effective pulse length from all pulses in this ICP recording to extract a sub-segment from the original raw dominant pulse.

[0062] After obtaining distance metrics among all mean ICP matched

pulses, a steady-state indicator (blocks 130, 132, and 134) may be calculated by different measures characterizing the distribution of these metrics. In particular, the mean, 130 the standard deviation 132, and 90^th percentile value 134 are used as three different methods of deriving such an indicator. For Euclidean and geodesic distances, it is expected that the steady-state ICP will have a smaller mean and 90^th percentile values because ICP pulses resemble each other after matching the mean ICP. On the other hand, the metrics based on correlation coefficient will have a larger mean and 90^th percentile values. Irrespective of the metrics used, the standard deviation of these metrics is expected to be smaller because a higher standard deviation will likely result from the analyzed ICP segment being a mixture of a steady state and a dynamical state.

[0063] In a strict mathematic term, geodesic distance is a generalization of length of a straight line connecting two points to that of a curve connecting the two points in a curved space. In the method 100 of the present invention, each pulse x_; is treated as a point in a high-dimensional

Euclidean space. If assuming no geometric structures exist in the point cloud formed by pulses x_i; i = 1, 2,^■■■ , N, conventional Euclidean distance between two pulses can be used as distance metric. However, many seemingly high-dimensional data originating from nature usually reside in a low-dimensional manifold. Revealing this low-dimensional manifold using nonlinear dimension reduction methods such as ISOMAP and Local Linear Embedding (LLE) may be helpful in visualizing the hidden geometrical structure of the data.

[0064] FIG. 6 shows a two dimensional projection of dominant ICP pulses using the local linear embedding (LLE) algorithm in accordance with the present invention. As shown in FIG. 6, an LLE algorithm was used to project the original ICP dominant pulses from a subject onto a two dimensional space to reveal the geometric structure. Therefore, a more sensible measure of distance between two pulses should take into consideration this geometric structure.

[0065] Geodesic distance is an example of such a measure. Approximation of the calculation of geodesic distance was performed by using a k- neighborhood graph. Assume a graph G(V, E) is used to describe x^ i = 1, 2,^■■■ , N where vertex ¼ represents x; and an edge is placed between ¼ and Vj if xj is among the k-nearest neighbors of x_; and we assign the Euclidean distance between x; and xj as the weight of this edge. This process is executed for all x_; to form the graph. After forming the graph, geodesic distance between any pairs of the pulses can be approximated by the total length of the shortest path along the graph linking the two vertices representing these two pulses. Dijkstra's algorithm was used to find this shorted path.

[0066] FIG. 7 illustrates a schematic diagram of an exemplary system 200 (e.g. ICP monitor or the like) for direct detection of the deviation of ICP dynamics from a steady state. The system 200 includes one or more sensors 210 configured for acquiring signals representative of physiological characteristics of the subject patient. 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 steady-state ICP indicator 280 that calculates inter-pulse distance between ICP pulses generated from MOCAIP module 270.

[0067] Experimental Results

[0068] Studies were performed to validate the method of the present

invention using a retrospective analysis of continuous ICP recordings from two patient populations to compare different methods of computing steady- state indicators as shown in FIG. 5.

[0069] The first population comprised of a group of patients undergoing a diagnostic and therapeutic evaluation of symptomatic slit-ventricle syndrome (SVS). SVS patients typically have been shunted for many years, and have chronically very small ("slit") ventricles with medically refractory headaches. Our protocol consisted of placing an intraparenchymal ICP monitor in the brain, and then externalizing the peritoneal end of the shunt so as to be able to completely shut off CSF flow. In most patients, this resulted in acute hydrocephalus with expansion of the ventricles with or without marked changes in ICP. These patients either underwent an endoscopic third ventriculostomy or revision of the shunt. In other patients, the ventricles did not expand.

[0070] These SVS patients received multiple brain imaging assessments during these evaluations to assess ventricle size changes. This provided a controlled "laboratory" in which we could observe acute changes in the steady state due to acute ventricle volume changes over a period of hours. Based on the radiological reports of the consecutive imaging studies of these patients, we were able to establish which periods between two consecutive imaging studies were associated with observable ventricular changes. We assumed that the ICP recordings measured between these periods of ventricular change represented definitively change of the steady- state state. We use SVS+ to denote cases in this group. For those periods without evident acute ventricular changes or any brain structural changes, we assume that the ICP recordings in these periods were from a steady- state ICP dynamic system and denote them as SVS- cases.

[0071] In addition, we studied a cohort consisting of 56 elderly patients with suspected normal pressure hydrocephalus (NPH). Overnight continuous ICP monitoring was conducted as part of their NPH diagnosis evaluation prior to a 3-day lumbar drain trial in the hospital. Given the fact that NPH is a chronic condition that progresses over a period of months, we therefore assumed that the overnight ICP recordings of these patients were essentially acquired from a steady-state ICP dynamic system. Patients in both populations consented for the ICP monitoring and data analysis as approved by the UCLA Institute Review Board (IRB).

[0072] As shown in FIG. 5, three distance/similarity metrics, including

geodesic distance 120, Euclidean distance 122, and Pearson correlation coefficient, 124 were used for comparing pulse shapes. In addition, three different steady-state indicators 130, 132, and 134 can be derived from the histogram 126 formed by each of these distance/similarity metrics.

Therefore, our data analysis experiment is set up to compare these nine different steady-state indicators among NPH, SVS+, and SVS- cases.

[0073] Mean ICP is a conventional measure used for routine diagnosis. We therefore also obtain the histograms of the mean ICP for the overnight recordings from NPH patients and those from the second patient population between consecutive brain imaging studies. In a similar fashion, we derive the mean, the standard deviation, and the 90th percentile values from these histograms to compare the NPH, SVS+, and SVS- conditions.

[0074] A one-way analysis of variance was used first to see if each of the above 12 metrics was different among the three conditions. Then a t-test was used to compare each pair of the three case groups.

[0075] Among the 32 pairs of consecutive brain imaging studies for the SVS patients, two cases were removed because of poor ICP pulse quality throughout the recording and four additional cases were excluded because the time interval between the two brain imaging studies was greater than 40 hours. The remaining 26 pairs were from 1 1 SVS patients. The mean age for these 1 1 patients (8 females) was 36 ± 16 years.

[0076] The number of cases with ventricular changes between two

consecutive studies is 1 1 . The mean duration of ICP recordings was 20.0 ± 6.8 hours for these positive cases and 20.6 ± 10.5 hours for the negative cases. The mean ICP was 7.1 ± 2.7 mmHg for the positive and 7.7 ± 2.3 mmHg for the negative cases. The mean standard deviation was 5.1 ± 2.8 mmHg for the positive and 6.7 ± 4.2 mmHg for the negative cases. There were 1929 ± 802 and 1915 ± 969 number of dominant pulses for the positive and the negative cases, respectively. The average time interval between two consecutive dominant pulses was 44.1 ± 33.5 seconds for the positive cases and 37.5 ± 4.5 seconds for the negative cases.

[0077] There were 20 female and 36 male patients in the NPH group. The average age for the NPH group was 72 ± 10 years. Mean duration of overnight ICP monitoring for the 56 NPH patients was 10.7 ± 2.4 hours. The mean ICP for these patients was 3.8 ± 3.8 mmHg. The mean standard deviation of these overnight ICP recordings was 2.9 ± 0.8 mmHg. The mean number of dominant pulses is 1 122 ± 307 and the average time interval between two dominant pulses was 36.0 ± 12.6 seconds.

[0078] To obtain the results reported below, A"ICP" has been chosen to be one mmHg to match mean ICP when deriving the steady-state indicators.

[0079] The results of twelve one-way analysis of variance experiments are shown in FIG. 18A through FIG. 8D, which illustrate the results of geodesic distance, Euclidean distance, Pearson correlation coefficient, and mean ICP in FIG. 8A, FIG. 8B, FIG. 8C, and FIG. 8D, respectively. Each plot in each panel displays the result from using a particular statistical parameter of the distance histogram as a steady-state indicator. It can be seen that steady-state indicators based on the mean and standard deviation of the histograms of mean ICP, Pearson correlation coefficients, and geodesic distance achieved a significant p (p <0.05) for the ANOVA test while the steady-state indicators based on the standard deviation of geodesic and Euclidean distance also reached a significant p value.

[0080] Since both SVS- and NPH cases represent a steady-state situation, the group analysis using ANOVA is not sufficient to 1 ) test if SVS+ cases can be separated from both SVS- and NPH cases; and 2) if a steady-state indicator is different between SVS- and NPH cases. Therefore, we conducted three t-tests to compare these three groups in a pair-wise fashion. These results are reported in Table 1 . We can observe that mean ICP steady-state indicators performed well in differentiating NPH from SVS+. Mean ICP was also different between SVS+ from SVS- groups (p = 0.041 ). However, mean and standard deviation of mean ICP histogram could also incorrectly differentiate between SVS- and NPH groups. Pearson correlation coefficient-based steady state indicators failed to differentiate between SVS+ and SVS- and incorrectly differentiated SVS- and NPH groups even though they could correctly differentiate between SVS+ and NPH. The Euclidean distance based approach did not incorrectly

differentiate between SVS- and NPH groups but it also failed to differentiate between SVS- and SVS+ groups. The only steady-state indicator that could differentiate correctly between SVS+ and NPH/SVS- but also avoided discriminating between SVS- and NPH groups is based on the standard deviation of the geodesic distance (the second row in Table 1 ).

[0081] To assess the distributions of the four metrics including mean ICP, geodesic distance, Euclidean distance, and Pearson correlation

coefficients, histograms of these metrics in one example case from each of the three case groups are presented in FIG. 9A through FIG. 9C. It is clear that by comparing these histograms, all three inter-pulse metrics of the SVS+ example have two peaks in their histograms while histograms of both NPH and SVS- groups have only one peak. However, the peaks in the histograms of the geodesic distance metric were better separated than those of the Euclidean and correlation coefficients. In contrast to this observation, histograms of mean ICP for all three cases had only one peak. It should be noted that this kind of multimodal histograms only existed for four additional SVS+ cases but it did not exist for any of the SVS- cases and only existed for 2 out of the 56 NPH patients.

[0082] It was also found that the appearance of multimodal histogram of geodesic distance did not depend on the existence of abnormally high mean ICP, as shown in FIG. 10A through FIG. 10D, where we display histogram, mean ICP trend, and percentage of distant pulses (geodesic distance > 15 mmHg) for two SVS+ cases in FIG. 10A and FIG. 10B, and for two NPH cases (geodesic distance > 7 mmHg) in FIG. 10C and FIG. 10D, respectively. The two vertical lines in the time series plots indicate the time of brain imaging studies. We can observe that abnormally high mean ICP values were not present and that the periods of high percentage of distant pulses were confined in narrow temporal windows of approximately two hours long for the SVS+ cases but they are more scattered for the two NPH cases.

[0083] In conclusion, tests were performed to verify the hypothesis that a consistent relationship between mean ICP and ICP pulse morphology is an indicator of an ICP dynamic system being at a steady state. It was shown that by using standard deviation of the inter-pulse geodesic distances as a steady-state indicator, we were able to differentiate between SVS+ and NPH, SVS+ and SVS- and avoid discrimination between SVS- and NPH groups. Since it is a reasonable assumption that acute brain ventricular changes perturb the steady state of the ICP dynamic system that was present for the NPH and SVS- groups, our results demonstrate the effectiveness of the method of the present invention to derive a steady- state indicator of an ICP dynamic system.

[0084] While a steady-state indicator based on conventional metrics

including Euclidean distance and Pearson correlation coefficient was able to differentiate between NPH and SVS+ groups, differentiating between SVS+ and SVS- groups is also more clinically useful because continuous ICP monitoring can be leveraged using the proposed approach to detect deviation of the ICP dynamic system from a steady state, which can be used as a nonspecific harbinger for acute intracranial changes typically seen in brain injured patient under neurocritical care.

[0085] The comparison between SVS+ and SVS- groups helps demonstrate the superiority of using geodesic distance to measure inter-pulse distance as compared to Euclidean and Pearson correlation coefficient.

[0086] The method of deriving a steady-state indicator of ICP dynamic

system includes the novel adoption of geodesic distance to quantify the similarity among ICP pulses. It is likely that ICP pulse, like many other naturally occurring signals, is inherently low-dimensional. Exploiting the geometric structure in this low-dimensional space where ICP pulses reside is critical in developing metrics that can meaningfully quantify the distance between pulses. As has been done in many existing manifold learning algorithms, k-nearest neighbor (KNN) was used to construct a weighted graph to facilitate the approximation of geodesic distance using the shortest graph distance between vertices. Other approaches to constructing the graph known in the art may also be considered.

[0087] Standard deviation of the geodesic distance was found in the

present studies to be the best steady-state indicator. It should be noted that other metrics known in the art for characterizing the distance histogram may also be implemented. In particular, characterizing the multimodal distribution of inter-pulse distance as shown in FIG. 9A through FIG. 9C and FIGS 10A through FIG. 10D is promising. The existence of multiple peaks in the distribution of inter-pulse distance can be considered as a strong marker of a transient intracranial pressure dynamic system because each peak may represent a cluster of pulses from a steady-state period and the coexisting of these clusters may indicate that the system undergoes transition between different steady states.

[0088] Although the present description is primarily directed to acute brain ventricular change, the same approach and data analysis experiment are applicable to investigating whether changes in the steady state of an ICP dynamic system can be detected when other forms of acute intracranial change occur. In neurocritical care of brain injury patients, such changes may include: acute intracranial mass increase, massive edema, and acute hydrocephalus, or the like. With the systems and methods of the present invention applied to work in these conditions, the potential of extending ICP monitoring to brain injury patients susceptible to such acute changes is promising and significant as early detections and treatments of these conditions may result from continuously tracking a valid steady-state indicator of the ICP dynamic system. [0089] Monitoring of mean ICP is useful for making patient management and therapeutic decisions by itself as conventionally practiced, however, it is appreciated that more information with regard to brain compliance, cerebral blood flow, prediction of acute mean ICP elevation, cerebral vasculature status, and autoregulation can be further derived by use of the systems and methods of the present invention and analyzing the whole ICP pulse waveform using more advanced signal processing and pattern recognition techniques.

[0090] Additional functionality, such as determination of when the change starts to occur, which is a desirable feature for monitoring purposes, and determining what types of acute intracranial changes are occurring, may be obtained by combining the current systems and methods with other brain monitoring modalities and/or a more detailed ICP pulse waveform analysis, e.g., the elevation of the third sub-peak of an ICP pulse may indicate cerebral hypoperfusion.

[0091] It has been shown that a hallmark of an ICP dynamic system at a steady state is manifested as ICP pulses at similar mean ICP resembling each other. It has also been demonstrated that geodesic distance is superior in measuring the inter-pulse distance to derive a steady-state indicator of the ICP dynamic system as compared to conventional metrics, such as Euclidean distance and Pearson correlation coefficient. In addition, monitoring of the changes in the steady state of the ICP dynamic system can potentially provide more information than monitoring mean ICP alone.

[0092] 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).

[0093] 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.

[0094] 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). [0095] From the discussion above it will be appreciated that the invention can be embodied in various ways, including the following:

[0096] 1 . A method for analysis of continuous intracranial pressure (ICP) pulse waveforms from a patient, comprising: acquiring an intracranial pulse signal from the patient; and detecting deviation of the ICP waveform from a steady-state.

[0097] 2. The method of any previous embodiment, wherein the deviation of the ICP waveform from a steady-state results from acute intracranial changes.

[0098] 3. The method of any previous embodiment, wherein the detected deviation comprises acute ventricular enlargement.

[0099] 4. The method of any previous embodiment, wherein the ICP

waveform is associated with a ICP dynamic system of the patient.

[00100] 5. The method of any previous embodiment, wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of the intracranial pulse (MOCAIP).

[00101] 6. The method of any previous embodiment, further comprising: applying MOCAIP to generate a consecutive series of dominant pulses for a given ICP waveform.

[00102] 7. The method of any previous embodiment, wherein a distance

between dominant pulses of matched average ICP is calculated to detect said deviation.

[00103] 8. The method of any previous embodiment, wherein the distance between pulses comprises an inter-pulse geodesic distance.

[00104] 9. The method of any previous embodiment, wherein the distance between pulses further comprises one or more of an inter-pulse Euclidean distance and an Inter-pulse Pearson correlation coefficient.

[00105] 10. The method of any previous embodiment, further comprising: generating a final histogram of one or more of the inter-pulse Euclidean distance, Inter-pulse Pearson correlation coefficient, and inter-pulse geodesic distance .

[00106] 1 1 . The method of any previous embodiment, wherein the final histogram comprises a multi-modality histogram of inter-pulse distance to function as a marker of unsteady state of the ICP dynamics.

[00107] 12. The method of any previous embodiment, further comprising: generating a steady state indicator as a function of said histogram.

[00108] 13. The method of any previous embodiment, wherein the steady state indicator is calculated according to one or more the mean value, standard deviation, or k-th percentile value.

[00109] 14. A system for analysis of continuous intracranial pressure (ICP) pulse waveforms from a patient, comprising: (a) a processor; and (b) programming executable on the processor for: (i) acquiring an intracranial pulse signal from the patient; and (ii) detecting deviation of the ICP waveform from a steady-state.

[00110] 15. The system of any previous embodiment, wherein the deviation of the ICP waveform from a steady-state results from acute intracranial changes.

[00111] 16. The system of any previous embodiment, wherein the ICP

waveform is associated with a ICP dynamic system of the patient.

[00112] 17. The system of any previous embodiment, wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of the intracranial pulse (MOCAIP).

[00113] 18. The system of any previous embodiment, the programming

further configured for: applying MOCAIP to generate a consecutive series of dominant pulses for a given ICP waveform.

[00114] 19. The system of any previous embodiment, wherein a distance between dominant pulses of matched average ICP is calculated to detect said deviation.

[00115] 20. The system of any previous embodiment, wherein the distance between pulses comprises an inter-pulse geodesic distance.

[00116] 21 . The system of any previous embodiment, wherein the distance between pulses further comprises one or more of an inter-pulse Euclidean distance and an Inter-pulse Pearson correlation coefficient.

[00117] 22. The system of any previous embodiment, said programming further configured for: generating a final distance between pulses as a function of the histogram of one or more of the an inter-pulse Euclidean distance, inter-pulse Pearson correlation coefficient, and inter-pulse geodesic distance .

[00118] 23. The system of any previous embodiment said programming

further configured for: generating a steady state indicator as a function of said histogram.

[00119] 24. A system as recited in claim 23, wherein the steady state

indicator is calculated according to one or more the mean value, standard deviation, or k-th percentile value.

[00120] 25. A monitor for analysis of continuous intracranial pressure (ICP) pulse waveforms from a patient, comprising: (a) a processor; (b) a sensor coupled to the processor; and (c) programming executable on the processor for: (i) acquiring an intracranial pulse signal with the sensor from the patient; and (ii) detecting deviation of the ICP waveform from a steady- state.

[00121] 26. The monitor of any previous embodiment , wherein the deviation of the ICP waveform from a steady-state results from acute intracranial changes.

[00122] 27. The monitor of any previous embodiment, wherein the ICP

waveform is associated with a ICP dynamic monitor of the patient.

[00123] 28. The monitor of any previous embodiment, wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of the intracranial pulse (MOCAIP).

[00124] 29. The monitor of any previous embodiment, the programming

[00125] 30. The monitor of any previous embodiment, wherein a distance between dominant pulses of matched average ICP is calculated to detect said deviation.

[00126] 31 . The monitor of any previous embodiment, wherein the distance between pulses comprises an inter-pulse geodesic distance. [00127] 32. The monitor of any previous embodiment, wherein the distance between pulses further comprises one or more of an inter-pulse Euclidean distance and an Inter-pulse Pearson correlation coefficient.

[00128] 33. The monitor of any previous embodiment, said programming

further configured for: generating a final distance between pulses as a function of the histogram of one or more of the an inter-pulse Euclidean distance, inter-pulse Pearson correlation coefficient, and inter-pulse geodesic distance .

[00129] 34. The monitor of any previous embodiment, said programming

[00130] 35. The monitor of any previous embodiment, wherein the steady state indicator is calculated according to one or more the mean value, standard deviation, or k-th percentile value.

[00131] 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

Table of p values from conducting f-tests comparing twelve different steady-state indicators for NPH vs. SVS+, SVS + vs. SVS -, and SVS vs. NPH groups. It can seen that only the steady-state indicator based on the standard deviation of the Geodesic distance correctly differentiates between NPH and SVS +, SVS + and SVS - groups while avoids discriminating between SVS - and NPH.

Claims

CLAIMS What is claimed is:

1 . A method for analysis of continuous intracranial pressure (ICP) pulse waveforms from a patient, comprising:

acquiring an intracranial pulse signal from the patient; and

detecting deviation of the ICP waveform from a steady-state.

2. A method as recited in claim 1 , wherein the deviation of the ICP waveform from a steady-state results from acute intracranial changes.

3. A method as recited in claim 2, wherein the detected deviation comprises acute ventricular enlargement.

4. A method as recited in claim 1 , wherein the ICP waveform is associated with a ICP dynamic system of the patient.

5. A method as recited in claim 1 , wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of the intracranial pulse (MOCAIP).

6. A method as recited in claim 5, further comprising:

applying MOCAIP to generate a consecutive series of dominant pulses for a given ICP waveform.

7. A method as recited in claim 6, wherein a distance between dominant pulses of matched average ICP is calculated to detect said deviation.

8. A method as recited in claim 7, wherein the distance between pul comprises an inter-pulse geodesic distance.

9. A method as recited in claim 7, wherein the distance between pulses further comprises one or more of an inter-pulse Euclidean distance and an Inter- pulse Pearson correlation coefficient.

10. A method as recited in claim 8, further comprising:

generating a final histogram of one or more of the inter-pulse Euclidean distance, Inter-pulse Pearson correlation coefficient, and inter-pulse geodesic distance .

1 1 . A method as recited in claim 10, wherein the final histogram comprises a multi-modality histogram of inter-pulse distance to function as a marker of unsteady state of the ICP dynamics.

12. A method as recited in claim 1 1 , further comprising:

generating a steady state indicator as a function of said histogram.

13. A method as recited in claim 12, wherein the steady state indicator is calculated according to one or more the mean value, standard deviation, or k-th percentile value.

14. A system for analysis of continuous intracranial pressure (ICP) pulse waveforms from a patient, comprising:

(a) a processor; and

(b) programming executable on the processor for:

(i) acquiring an intracranial pulse signal from the patient; and

(ii) detecting deviation of the ICP waveform from a steady-state.

15. A system as recited in claim 14, wherein the deviation of the ICP waveform from a steady-state results from acute intracranial changes.

16. A system as recited in claim 14, wherein the ICP waveform is associated with a ICP dynamic system of the patient.

17. A system as recited in claim 14, wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of the intracranial pulse (MOCAIP).

18. A system as recited in claim 17, the programming further configured for:

19. A system as recited in claim 18, wherein a distance between dominant pulses of matched average ICP is calculated to detect said deviation.

20. A system as recited in claim 19, wherein the distance between pulses comprises an inter-pulse geodesic distance.

21 . A system as recited in claim 20, wherein the distance between pulses further comprises one or more of an inter-pulse Euclidean distance and an Inter-pulse Pearson correlation coefficient.

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

generating a final distance between pulses as a function of the histogram of one or more of the an inter-pulse Euclidean distance, inter-pulse Pearson correlation coefficient, and inter-pulse geodesic distance .

23. A system as recited in claim 22, said programming further configured for:

generating a steady state indicator as a function of said histogram.

24. A system as recited in claim 23, wherein the steady state indicator is calculated according to one or more the mean value, standard deviation, or k-th percentile value.

25. A monitor for analysis of continuous intracranial pressure (ICP) pulse waveforms from a patient, comprising:

(a) a processor;

(b) a sensor coupled to the processor; and

(c) programming executable on the processor for:

(i) acquiring an intracranial pulse signal with the sensor from the patient; and

(ii) detecting deviation of the ICP waveform from a steady-state.

26. A monitor as recited in claim 25, wherein the deviation of the ICP waveform from a steady-state results from acute intracranial changes.

27. A monitor as recited in claim 25, wherein the ICP waveform is associated with a ICP dynamic monitor of the patient.

28. A monitor as recited in claim 25, wherein acquiring an intracranial pulse signal comprises morphological clustering and analysis of the intracranial pulse (MOCAIP).

29. A monitor as recited in claim 28, the programming further configured for:

30. A monitor as recited in claim 29, wherein a distance between dominant pulses of matched average ICP is calculated to detect said deviation.

31 . A monitor as recited in claim 30, wherein the distance between pulses comprises an inter-pulse geodesic distance.

32. A monitor as recited in claim 31 , wherein the distance between pulses further comprises one or more of an inter-pulse Euclidean distance and an Inter-pulse Pearson correlation coefficient.

33. A monitor as recited in claim 32, said programming further configured for:

34. A monitor as recited in claim 33, said programming further configured for:

generating a steady state indicator as a function of said histogram.

35. A monitor as recited in claim 34, wherein the steady state indicator is calculated according to one or more the mean value, standard deviation, or k-th percentile value.