Source: http://www.google.de/patents/US6017313
Timestamp: 2013-05-20 21:26:02
Document Index: 773400339

Matched Legal Cases: ['art 2', 'art 2', 'art 1', 'art 1', 'art 1', 'art 1']

Patent US6017313 - Apparatus and method for blood pressure pulse waveform contour analysis - Google PatenteSuche Bilder Maps Play YouTube News Gmail Drive Mehr » Erweiterte Patentsuche | Webprotokoll | Anmelden Erweiterte Patentsuche PatenteMethods and apparatus for processing an arterial blood pressure waveform to extract clinically useful information on the state of the cardiovascular system are disclosed herein. In order to obtain the parameters of the modified Windkessel model, the diastolic portion of a subject's blood pressure waveform...http://www.google.de/patents/US6017313?utm_source=gb-gplus-sharePatent US6017313 - Apparatus and method for blood pressure pulse waveform contour analysis Ver�ffentlichungsnummerUS6017313 APublikationstypErteilung Anmeldenummer09/045,420 Ver�ffentlichungsdatum25. Jan. 2000Eingetragen20. M�rz 1998 Priorit�tsdatum20. M�rz 1998Auch ver�ffentlicht unterUS6394958US6689069US20030023173 ErfinderChristopher W. BratteliJay N. CohnStanley M. FinkelsteinDennis J. MorganUrspr�nglich Bevollm�chtigterHypertension Diagnostics, Inc. US-Klassifikation600/485600/481Internationale KlassifikationA61B7/04A61B7/00A61B5/021 UnternehmensklassifikationA61B7/00A61B7/045A61B5/021 Europ�ische KlassifikationA61B7/00A61B5/021A61B7/04BReferenzenPatentzitate (37)Nichtpatentzitate (34) Referenziert von (16)Externe LinksUSPTO USPTO-Zuordnung EspacenetApparatus and method for blood pressure pulse waveform contour analysisUS 6017313 A Zusammenfassung Methods and apparatus for processing an arterial blood pressure waveform to extract clinically useful information on the state of the cardiovascular system are disclosed herein. In order to obtain the parameters of the modified Windkessel model, the diastolic portion of a subject's blood pressure waveform is scanned over a plurality of ranges and the range that produces the best fit of data and lowest error estimates are selected. In addition, multiple empirically determined starting values of the `A` parameters are used to find the best fit of the model data to the actual arterial blood pressure waveform data.
What is claimed is: 1. Apparatus for analyzing a digitized arterial blood pressure waveform comprising a computer programmed to carry out the steps of: a) fitting a mathematical model of a curve to a diastolic portion of the waveform to determine a first set of curve fitting parameters; b) fitting the mathematical model to the diastolic portion of the waveform to determine a second set of curve fitting parameters; c) determining a first model parameter of an electrical analog model of a vasculature for each of the first and second sets of curve fitting parameters; d) determining an estimate of the error associated with each of the first model parameters; e) selecting as superior the first model parameter with the least error associated with it; f) determining a second model parameter of an electrical analog model of the vasculature for each of the first and second sets of curve fitting parameters; g) determining an estimate of the error associated with each of the second model parameters; h) selecting as superior the second model parameter with the least error associated with it; and i) reporting to a user of the apparatus the first and second model parameters selected as superior, wherein the first and second model parameters reported to the user are not necessarily obtained from the same set of curve fitting parameters.
2. Apparatus according to claim 1 wherein the electrical analog model is a second or higher order electrical analog model.
5. The apparatus according to claim 1, wherein the apparatus comprises a computer programmed to carry out the steps of: j) identifying a region of the waveform around the dicrotic notch; k) marking a point on the waveform within the region as the onset of diastole; and l) marking a point representative of the end of diastole.
6. Apparatus for analyzing a digitized arterial blood pressure waveform comprising a computer programmed to carry out the steps of: a) identifying a diastolic portion of the waveform; b) fitting a mathematical model of a curve to the identified diastolic portion of the waveform to determine a first set of curve fitting parameters using a first set of initial condition parameters as an initial condition; c) fitting the mathematical model to the diastolic portion of the waveform to determine one or more additional sets of curve fitting parameters using one or more additional sets of initial condition parameters as initial conditions wherein each set of initial conditions are different from one another; d) determining a measure of the quality of fit for each set of curve fitting parameters to identify sets of parameters that meet a minimum requirement; e) determining one or more model parameters of an electrical analog model of the vasculature using each of the sets of the curve fitting parameters that meet the minimum requirement; f) changing the identified diastolic portion of the waveform and repeating steps b, c, d and e; and g) determining an error associated with each model parameter determined in step e in order to establish which model parameter determined in step e has the least error associated with it.
7. Apparatus according to claim 6 further wherein the electrical analog model has at least two parameters associated with it each representing different aspects of the vasculature, and wherein the computer is programmed to perform the following steps: h) for each of the two or more different model parameters, identifying which one of the model parameters determined in step g has the least error associated with it and associating the identified model parameters with the waveform being processed, wherein a first identified parameter may be obtained from a first diastolic data set of the diastolic waveform and a second identified parameter may be obtained from a different diastolic data set of the diastolic waveform.
8. Apparatus according to claim 6 wherein the electrical analog model is a second or higher order electrical analog model.
10. The apparatus according to claim 6, wherein the apparatus comprises a computer programmed to carry out the steps of: h) identifying a region of the waveform around the dicrotic notch; i) marking a point on the waveform within the region as the onset of diastole; and j) marking a point representative of the end of diastole.
11. A method for analyzing a digitized arterial blood pressure waveform using a computer comprising the steps of: a) fitting a mathematical model of a curve to a diastolic portion of the waveform to determine a first set of curve fitting parameters; b) fitting the mathematical model to the diastolic portion of the waveform to determine a second set of curve fitting parameters; c) determining a first model parameter of an electrical analog model of a vasculature for each of the first and second sets of curve fitting parameters; d) determining an estimate of the error associated with each of the first model parameters; e) selecting as superior the first model parameter with the least error associated with it; f) determining a second model parameter of an electrical analog model of the vasculature for each of the first and second sets of curve fitting parameters; g) determining an estimate of the error associated with each of the second model parameters; h) selecting as superior the second model parameter with the least error associated with it; and i) reporting to a user of the apparatus the first and second model parameters selected as superior, wherein the first and second model parameters reported to the user are not necessarily obtained from the same set of curve fitting parameters.
12. A method according to claim 11, wherein the electrical analog model is a second or higher order electrical analog model.
14. The method according to claim 11, wherein analyzing a digitized arterial blood pressure waveform using a computer comprises: j) identifying a region of the waveform around the dicrotic notch; k) marking a point on the waveform within the region as the onset of diastole; and l) marking a point representative of the end of diastole.
15. A method for analyzing a digitized arterial blood pressure waveform using a computer comprising the steps of: a) identifying a diastolic portion of the waveform; b) fitting a mathematical model of a curve to the identified diastolic portion of the waveform to determine a first set of curve fitting parameters using a first set of initial condition parameters as an initial condition; c) fitting the mathematical model to the diastolic portion of the waveform to determine one or more additional sets of curve fitting parameters using one or more additional sets of initial condition parameters as initial conditions wherein each set of initial conditions are different from one another; d) determining a measure of the quality of fit for each set of curve fitting parameters to identify sets of parameters that meet a minimum requirement; e) determining one or more model parameters of an electrical analog model of the vasculature using each of the sets of the curve fitting parameters that meet the minimum requirement; f) changing the identified diastolic portion of the waveform and repeating steps b, c, d and e; and g) determining an error associated with each model parameter determined in step e in order to establish which model parameter determined in step e has the least error associated with it.
16. A method according to claim 15, further wherein the electrical analog model has at least two parameters associated with it each representing different aspects of the vasculature, and including the additional step of: h) for each of the two or more different model parameters, identifying which one of the model parameters determined in step g has the least error associated with it and associating the identified model parameters with the waveform being processed, wherein a first identified parameter may be obtained from a first diastolic data set of the diastolic waveform and a second identified parameter may be obtained from a different diastolic data set of the diastolic waveform.
17. A method according to claim 15, wherein the electrical analog model is a second or higher order electrical analog model.
19. The method according to claim 15, wherein analyzing a digitized arterial blood pressure waveform using a computer comprises: h) identifying a region of the waveform around the dicrotic notch; i) marking a point on the waveform within the region as the onset of diastole; and j) marking a point representative of the end of diastole.
20. A method for identifying a diastolic portion of a digitized arterial blood pressure waveform comprising the steps of: a) marking a point representative of the onset of diastole; b) marking a point representative of the end of diastole, the end of diastole determined by locating the point where the waveform ceases monotonic decay; and c) determining the point at which the waveform ceases to decay monotonically by finding the point at which the waveform changes at less than about a 0.1% rate.
21. Apparatus for identifying a diastolic portion of a digitized arterial blood pressure waveform comprising a computer programmed to carry out the steps of: a) marking a point representative of the onset of diastole; and b) marking a point representative of the end of diastole, the end of diastole determined by locating the point where the waveforn ceases monotonic decay, wherein the point at which the waveform ceases to decay monotonically is determined by finding the point at which the waveform changes at less than about a 0.1% rate.
22. Apparatus for identifying a diastolic portion of a digitized arterial blood pressure waveform comprising a computer programmed to carry out the steps of: a) identifying a region of the waveform around the dicrotic notch; b) marking a point on the waveform within the region as the onset of diastole; c) marking a point representative of the end of diastole; and d) marking the point representative of the end of diastole based on the decay of the waveform being monotonic.
23. Apparatus according to claim 22, wherein the point at which the waveform ceases to decay monotonically is determined by finding the point at which the waveform changes at less than about a 0.1% rate.
Referring to FIG. 5, there is illustrated an overview of the step 60 for analyzing selected beats. The process 70 of step 60 begins with a check to determine if a predetermined number of beats has been analyzed successfully (72-74), and, if so, proceeds to calculate the weighted averages of variables (76), the generation of a representative ensemble beat for display (78), and output of the results from the analysis (80). The beat length of the ensemble beat is assigned to the median beat length of all of the beats included. The ensemble beat is generated by averaging the data values across beats point by point using the upstroke point of each beat as the fiduciary point. The reported C.sub.1 and C.sub.2 parameters are taken as the weighted average of measures across at least five (5) and at most ten (10) beats, according to one example embodiment. The values are weighted by the ratio of the R.sup.2 of the fit to the propagated error variance of the fit (model), as described more fully below.
If beat analysis is not complete, the process 70 obtains a beat for analysis (82). If there are no more diastolic data sets for the current beat, and the fit criteria are met for C.sub.1, C.sub.2 and L (84-86), the running sums for all variables are calculated (88). If the fit criteria are not met, the process returns to step 72.
If not all empirical A.sub.i parameter sets have been applied (92), a parameter set is obtained (94). A Newton-Raphson curve fit of data is performed to obtain a final set of A.sub.i parameters from the empirical starting point (96), and a calculation of R.sup.2 of fit and error estimates of model data is performed (98). Curve fitting for a given diastolic data set and model parameters are driven by minimization of the mean square error agreement between the modeled arterial waveform and the actual data. For a given fit, the coefficient of determination (R.sup.2 value) is used to determine whether a particular fit meets a goodness of fit standard, taken in this example embodiment as an R.sup.2 greater than or equal to 0.975, as more fully described below.
If the fit criteria are not met (100), the process returns to step 92 and a different set of starting empirical A.sub.i parameters are obtained and the waveform re-fitted; if the criteria are met, the process proceeds to calculation of model values C.sub.1, C.sub.2 and L based on the this A.sub.i parameter set (102). An estimate of the coefficient of variation (CV.sub.e =propogated error of value unadjusted for SVR or pressure error, divided by the value) is then calculated (104) for C.sub.1, C.sub.2 and L. If the fit produced the smallest CV.sub.e for C.sub.1 (108), it is saved (108), for example in RAM 22 or in the storage device 24. Similar checks and saves are done for C.sub.2 and L (110-116), and the process returns to step 84. If more data sets are obtainable for the beat by scanning the near-notch region, steps 90-116 are repeated. Thus, the particular fit (model) on a beat will be accepted to later contribute to the C.sub.1 or C.sub.2 value reported if it produces a minimization of the CV.sub.e for that measure as the dicrotic start region is scanned. The near notch region is a region of the arterial waveform just surrounding the notch. In the above-noted process, the starting point for the diastolic data set is taken from an initial point in that region, and then moved forward until all sets have been considered.
Thus, by this process, each near-notch region, identified by the scanning over a range of diastolic starting locations, produces independent values of C.sub.1, C.sub.2 and L. The limits of the scan range or window are taken as the location of the first positive going zero crossing of the second derivative to the subsequent negative going zero crossing of the second derivative, surrounding the notch point. A maximum scan window is defined to handle the case where the second zero crossing does not occur, as described more fully below.
Thus, as described above, process 70 applies a set of initial empirical model parameter values and a curve fitting algorithm such as the Newton-Raphson technique to the diastolic portion of the beat to obtain resulting curve fitting parameter values. If these parameter values meet the selection criteria for the regression between the model and data, the values are saved. If not, the next set of empirical model parameters are applied and the process repeated. This process is repeated for each scan location in a given beat so that at the end of beat analysis there is saved the fit state that results in the smallest estimated coefficient of variation, CV.sub.e, value for each of C.sub.1, C.sub.2 and L independently.
Finally, the process determines whether the best saved fits for C.sub.1 and C.sub.2 both meet the criteria for the regression between model and data (R.sup.2 &gt;=0.975). If so these fit states are saved as representative fits for the given beat and the next beat processed. If both C.sub.1 and C.sub.2 do not meet the criteria, then the results for the beat are not included and the process proceeds to analyze the next beat. This procedure is then repeated until a minimum acceptable number of beats (e.g., 5 to 10 beats) have been analyzed and accepted. The hemodynamic values are then calculated as the average values for these beats. The model values of C.sub.1, C.sub.2 and L are taken as the weighted average of the respective values from these beats. The weighting factor is the R.sup.2 coefficient of determination regression value divided by the propagated error variance value for the particular measure. In addition, a representative arterial blood pressure waveform for visual display is generated as the average or ensemble of all the beats cross correlating at, or above, 0.95.
Step 58 performs a beat to beat cross correlation using Pearson's cross-correlation to determine a dominant family of beats, and a heart rate variability restriction is applied in order to select a group of heart beats for further analysis. Beats are accepted that are within .+-.5% of the median beat length. The group with the greatest cross correlation coefficients with other beats meeting the criteria is taken as the dominant family of beats. The heart rate variability restriction limits the number of beats in the dominant family. Preferably, three groupings are made based on the median beat length (MBL) of all the beats. Group 1 contains beats of length between 0.85*MBL and 0.95*MBL. Group 2 contains beats of length between 0.95*MBL and 1.05*MBL. Group 3 contains beats of length between 1.05*MBL and 1.15*MBL. The grouping with the most number of beats, most often the middle group, is used for subsequent ensembling and analysis.
As noted above, the example process of the invention utilizes a modified Newton-Raphson or equivalent curve fitting technique for determination of the model parameters for the above noted third-order equation. The process involves an iterative process to minimize the difference between the actual data and the model. The routine of the example embodiment utilizes a minimum of five initial empirical parameter sets as a starting point in the iterative solution for the curve fitting process, but an alternate embodiment may add additional sets generated in a pseudo-random fashion or in such a way as to span parameter space in an efficient manner. For instance, one might search the corners of the parameter space (2.sup.5 =32 corners).
______________________________________A.sub.1  A.sub.2  A.sub.3 A.sub.4 A.sub.5                                   A.sub.6______________________________________Group 1  *     0.8      5.0   2.0     10.0  6.0Group 2  *     0.75     30.0  20.0    40.0  1.75Group 3  *     0.8      40.0  8.0     8.0   6.0Group 4  *     0.5      20.0  10.0    20.0  1.0Group 5  *     0.479    28.064                       8.352   13.482                                     2.125______________________________________
"*" parameters are computed as A.sub.1 =P.sub.0 -A.sub.3 cos A.sub.6. Other sets of initial empirical A parameters could be generated in a pseudo-random fashion, to extend this set of empirical `A` parameters.
&#916;A=(J.sup.T J).sup.-1 J.sup.T D
A.sub.new =A.sub.old +w&#916;A
A=(A.sub.1, A.sub.2, A.sub.3, A.sub.4, A.sub.5, A.sub.6) ##EQU3##
Other embodiments might have w vary between 0.05&lt;=w&lt;=0.5 but the example embodiment described here utilizes a value of 0.5. D is the difference vector between the actual data and the model fit for a given set of `A` parameters.
If the iteration maximum has not been reached, the iter counter is incremented (178) and the variable olderr (old error) is set to errx (180). One curve fit iteration is performed to generate a new set of A parameters (182). The Jacobian matrix is generated and the errx for this fit is calculated (184). If errx is less than errmin and A.sub.2 &lt;=20 and proceeds to step (170). Otherwise, it proceeds to step 192. At step 170, iErrorCode is set to eFitFound to indicate that an acceptable fit was found. Errmin is set to errx and minIter is set to iter (172). The current `A` parameter values are saved (174) and the covariance matrix is saved (176). The process continues at 192.
The goodness of a curve fit is assessed by the coefficient of determination and is calculated as follows: ##EQU4## where n.sub.initial is taken as the length of diastole from the left most scan point to the end of diastole.
A successful curve fit to a diastolic data set establishes a set of model parameters which determines a C.sub.1 and C.sub.2 value for the beat. It has already been described how data set boundaries are determined by waveform marking and how a single curve fit is obtained. Described below is how a subset of diastolic data within the data boundaries is chosen for the curve fitting and how compliance values are subsequently calculated.
Var(f)&#8776;g(A)
A=(A.sub.1, A.sub.2, A.sub.3, A.sub.4, A.sub.5, A.sub.6); ##EQU5## is the gradient of f w.r.t. A, evaluated at A; C=[J.sup.T J].sup.-1 is the covariance matrix at A that is returned from curve fitting, and where the pressure error is assumed to be 1 mm Hg; and
This expression gives a measure of the expected error in, e.g., RC.sub.1. For this implementation, the error in R (systemic vascular resistance) is assumed to be equal to 0, but there are numerous ways of estimating an error for R as well, and thus modifying the total error estimate. The assumption that the pressure error be 1 mm Hg is inconsequential if it is assumed that this error is constant over the entire data acquisition, since it cancels out of the equation for the weighted average values. R does not exactly cancel out, since it varies beat by beat, but no increased performance has been seen in our experiments by accounting for this effect.
the hat symbol () indicates the value is adjusted appropriately for R (e.g., RC.sub.1 is divided by R).
Precision was determined by taking the population average of the coefficient of variations in human research subjects who had triplicate measurements. When the above techniques of noise reduction through multiple samplings and a priori elimination of expected outliers were applied and compared with analysis of a single averaged beat at a single start point, the average CV.sub.e dropped from 14.5% to 9.5% (C.sub.1) and from 24.9% to 13.8% (C.sub.2) for control or normal subjects, that is persons without obvious cardiovascular disease. For subjects exhibiting cardiovascular disease, that is, non-normals, the improvements were 14.5% to 11.0% (C.sub.1) and 30.2% to 19.9% (C.sub.2). By repeating measurements on an individual, it is easy to obtain precision values for arterial compliance. For precision, one needs only compare measurements to each other. While the foregoing improvements are believed to be true, they are not presented herein as proof of the efficacy of the embodiments of the present invention, and should not be relied upon for any such purpose.
To test the expected decrease in arterial compliance with age, a database of 115 healthy male and female subjects between the ages of 19 and 75 was analyzed and compliance plotted versus age. When the analyses with and without noise reduction were compared, the R.sup.2 value of the correlation of C.sub.1 and C.sub.2 with age improved considerably. The R.sup.2 values were 0.25 or better for the measures. Additional confidence in these measures was obtained by examining the compliance values from several groups and comparing them to what the expected result should be. Those expected to have reduced arterial compliance because of hypertension, smoking tobacco products, coronary artery disease, or post-menopausal status should have lower compliance when compared to controls. In each case, non-normals had compliance values between 10% and 35% lower than normals using the analysis approach of the example embodiment of the invention herein described. Again, while the foregoing analysis is believed to be true, it is not presented herein as proof of the efficacy of the embodiments of the invention set forth herein, and should not be relied upon for any such purpose.
As noted above, the example embodiment of the present invention uses the modified Windkessel model of the vasculature, and produces as output, the values C.sub.1, C.sub.2 and L, with R being calculated from mean arterial pressure and cardiac output. How mean arterial pressure and cardiac output are determined is not essential to the inventions claimed herein and are therefore not discussed further. However, method and apparatus for obtaining these measurements are described in U.S. '177 and U.S. Pat. No. 5,241,966, issued Sep. 7, 1993, and entitled "Method and Apparatus for Measuring Cardiac Output," the entire disclosure of which is herein incorporated by reference.
In addition to the C.sub.1, C.sub.2 and L parameters, the Total Vascular Impedance (TVI) may also be calculated and output as data from device 10 as the impedance function evaluated at the frequency of the measured heart rate w. The calculation for TVI is as follows: ##EQU7## where
The modified Windkessel model of the arterial system is shown in FIG. 1. The model includes components P.sub.1, P.sub.2, C.sub.1, C.sub.2, L and R in which:
L=inertance (mm Hg/(ml/s.sup.2))
P.sub.2 =distal or peripheral artery pressure (mm Hg)
P(t)=A.sub.1 e.sup.-A.sbsp.2.sup.t +A.sub.3 e.sup.-A.sbsp.4.sup.t cos(A.sub.5 t+A.sub.6)
In U.S. '177, a curve fitting algorithm, such as the Gauss-Newton parameter estimating algorithm, is then applied to the marked diastolic data set of the waveform to ascertain the `A` coefficients of the modified Windkessel model. An automatic stopping procedure was employed to stop iteration when an acceptable level of error was reached or when convergence slowed below a preset threshold. Also, U.S. '177 proposed that when the process started to diverge it returned to the previous best case. Additionally, the routine included a weighted iteration interval to improve convergence. Using a measure of cardiac output and mean arterial pressure to calculate R, the modified Windkessel parameters C.sub.1, C.sub.2 and L could then be calculated as well. In U.S. '177, it is contemplated that the parameters R, C.sub.1, C.sub.2 and L are calculated for each beat in the set under analysis, and subsequently averaged to produce mean values more reliable for accuracy than any of the individual values. Alternatively, U.S. '177 teaches that median values can be selected.
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