Fetal outcome predictor and monitoring system

A fetal health assessor and outcome predictor and monitoring system includes a system for automatically assessing fetal health and predicting fetal outcomes based on fetal and maternal data. The system includes a case-based reasoning processor for categorizing the fetus into one of a plurality of cases based on biographical data about the fetus and mother. The system also includes a transducer for converting physical conditions of the fetus and mother (e.g. characteristics based on heart rates and uterine contractions) to signals representative of the conditions. A computer processor converts the signals to a set of signal features. An assessor and predictor receives the processed signal features and assesses fetal health and predicts fetal outcome based on identified fuzzy relationships between fetal and maternal data and fetal outcomes.

The present invention relates to health care, and more particularly, to a
 method and system for monitoring fetal health and for predicting fetal
 outcomes based on data about the mother and the fetus.
 BACKGROUND OF THE INVENTION
 With the continuously escalating cost of health care, one significant issue
 is balancing the cost of health care versus the quality of health care.
 While some percentage of health care dollars are wasted on dubious or
 inappropriate care, it is also true that, even in the United States, there
 is still preventable disease and death that occurs. The question is how to
 assure the highest possible quality of care without incurring excessive
 costs.
 One area of health care where the cost-quality debate rages is in the care
 of pregnant women, where the intent is to deliver healthy babies while
 preserving the health of the mother. Current national health objectives
 have targeted reductions in the following areas: infant deaths (before,
 during, and after birth); low birth weight; severe complications of
 pregnancy; and instances of severe mental retardation. Current obstetrical
 practices include: identifying risk factors; monitoring fetal heart rate
 and maternal uterine contractions (with a Doppler ultrasound device and a
 tocodynamometer, respectively); ultrasound measurements; amniocentesis;
 and other laboratory tests which were virtually unknown twenty years ago.
 Unfortunately, the use of these various monitoring and measurement methods
 have not significantly improved infant health. Currently-available methods
 for categorizing pregnancies as healthy or potentially at risk do not
 appear to be sufficiently accurate and reliable. In addition, since these
 various monitoring and measurement methods have been implemented, it is
 believed that the cesarean delivery rate has dramatically increased. While
 the cesarean delivery rate is now on a slight decline, national health
 objectives also include a targeted reduction in the cesarean delivery
 rate.
 Much of antepartum care (occurring in physicians' offices from conception
 until labor) and all of intrapartum care (occurring in the hospital while
 the patient is in labor) is concerned with the early identification of
 potentially adverse situations and subsequent early intervention to avoid
 or minimize adverse events. Yet, there is no highly sensitive and specific
 technology to assess fetal well-being. A method that accurately and
 reliably categorizes pregnancies as healthy or potentially at risk is
 needed to improve perinatal outcomes while managing health care costs.
 Typical intrapartum monitoring includes electronic fetal monitoring (EFM)
 which is often made up of a dual-track strip chart or display of both
 fetal heart rate (FHR) and uterine contractions (UC). Obstetricians
 currently determine when it is advisable to intervene (delivering the
 child by cesarean section as opposed to vaginally) by viewing the EFM data
 and relying upon their past training and experience. Because of the great
 uncertainty inherent in this approach, it is natural for obstetricians to
 seek to err on the side of a cesarean section, which is more likely to
 result in a safe delivery for a fetus, even though the cesarean section
 may not have been necessary; is much more costly; and has a potentially
 greater health impact on the mother. Thus, a much larger number of
 cesarean sections end up being performed than are necessary. For all of
 these reasons, a reliable predictor of fetal outcomes would be very useful
 in assisting obstetricians in determining when it is appropriate to
 intervene in the delivery process.
 Others have proposed systems for automatically assessing fetal health based
 upon the fetal heart rate. An example of this is disclosed by Dawes, et
 al., Int. J. Biomed. Comput., 25 (1990) pp. 287-294, "Criteria for the
 Design of Fetal Heart Rate Analysis Systems," and Dawes, et al.,
 Obstetrics & Gynecology, Vol. 80, No. 4, October 1992, pp. 673-678,
 "Short-term Fetal Heart Rate Variation, Decelerations, and Umbilical Flow
 Velocity Waveforms Before Labor." The Dawes articles suggest measuring the
 short-term time variations in the fetal heart rate and determining that
 the fetal health is in jeopardy when the variations fall below a
 predetermined threshold. It is believed that such approaches are not
 sufficiently robust. Furthermore they do not reflect and predict fetal
 outcomes with sufficient accuracy. It is believed that this lack of
 robustness is due in part to a lack of any type of artificial intelligence
 that would allow a system to adapt to and learn about new situations and
 an inability to accurately assess fetal health from noisy data.
 It is against this background, and the desire to solve the problems of and
 improve on the prior art, that the above invention has been developed.
 SUMMARY OF THE INVENTION
 The present invention is based upon the belief that EFM data has previously
 been improperly or incompletely analyzed and interpreted in determining
 fetal outcomes. The present invention relies upon determining hidden
 relationships between known EFM data in different scales and known fetal
 outcomes in past cases for which adequate data are available, and then
 recognizing characteristics in new EFM data in a current patient that will
 allow the system to predict the fetal outcome based thereon.
 Particularly, the present invention is related to a method of automatically
 providing an output representative of the health of a fetus carried by a
 mother, based on fetal and maternal data, with a computer-based predictor.
 The method includes receiving biographical data about the fetus and the
 mother and categorizing the fetal and maternal data combination as one of
 a plurality of types of cases based on the biographical data. The method
 also includes receiving physical data about the current condition of the
 fetus and the mother and processing the physical data to extract
 characteristic features. The method further includes selecting a subset of
 the characteristic features based on the type of case that the fetal and
 maternal data combination has been categorized as, and providing an output
 representative of the health of the fetus from the subset of
 characteristic features based on previously-developed fuzzy relationships
 between fetal and maternal data and fetal health.
 The method may further include communicating the output to a user. The
 reported output may be representative of the current status of the fetus.
 The reported output may be representative of the health of the fetus at a
 birth that could occur at a predetermined time in the future. The
 predetermined time may be approximately twenty minutes after the output is
 reported. The method may further include developing fuzzy relationships
 between fetal and maternal data and fetal health based on data and known
 fetal health from past pregnancies and births. The method may further
 include using the reported fetal health to adjust and optimize the
 selecting operation. The method may further include using the reported
 fetal health to adjust and optimize the providing operation.
 The present invention is also directed to a method of automatically
 reporting fetal health for a fetus carried by a mother, based on fetal and
 maternal data, with a computer-based assessor. The method includes
 receiving fetal and maternal data and processing the fetal and maternal
 data to generate and select an optimal set of fetal and maternal features.
 The method also includes applying the optimal selected set of fetal and
 maternal features to the assessor to assess and predict fetal health based
 on identified fuzzy relationships between fetal and maternal data and
 fetal health.
 The method may further include a case-based reasoning processor that
 receives biographical data about the fetus and the mother and, using
 case-based reasoning, determines the optimal set of data features to be
 applied to the assessor. The optimal set of features may be selected based
 on a learning process that allows the assessor to categorize the fetal and
 maternal data as belonging to one of a plurality of types of cases and to
 select the optimal set of features from a larger set of features based on
 the case type.
 The present invention is also directed to a system for automatically
 assessing fetal health and predicting fetal outcomes based on fetal and
 maternal data. The system includes a transducer for converting a physical
 condition of a fetus or mother to a signal representative thereof and a
 processor for converting the signal to a set of signal features, the set
 of features being based in part on fetal and maternal biographical data.
 The system also includes an assessor receptive of the processed signal
 features for assessing fetal health and predicting fetal outcome based on
 identified fuzzy relationships between fetal and maternal data and fetal
 outcomes.
 The present invention is also directed to a method of automatically
 assessing fetal health and predicting fetal outcome based on fetal and
 maternal data with a computer-based assessor and predictor. The method
 includes comparing known fetal and maternal data from past pregnancies to
 the corresponding known fetal outcomes of the past pregnancies and
 automatically developing inference rules for mapping the known fetal and
 maternal data to the known outcomes. The method also includes receiving
 fetal and maternal data for a current pregnancy and automatically applying
 the developed inference rules to the received fetal and maternal data to
 assess fetal health and predict a fetal outcome for the current pregnancy.
 The comparing operation may also include extracting characteristic signal
 features from the known data and comparing these extracted features to the
 known fetal outcomes. The method may further include automatically
 adjusting the inference rules based on the assessed fetal health and
 predicted fetal outcome for the current pregnancy.
 The present invention is also directed to a method of developing an
 assessor and predictor for automatically assessing fetal health and
 predicting fetal outcomes based on fetal and maternal data. The method
 includes receiving known data from a plurality of births that have already
 occurred, the known data including fetal and maternal data from a time
 period prior to and during birth, the known data further including data
 relating to the health of the fetus immediately after birth, the fetal
 outcome data. The method also includes processing the known data from the
 plurality of births to extract signal characteristics and automatically
 developing inference rules that map the fetal and maternal data from a
 particular birth from the plurality of births into the fetal outcome data
 from the particular birth from the plurality of births.
 The method may further include using the assessor and predictor to assess
 fetal health and predict fetal outcomes and automatically adjusting the
 inference rules based on the assessed fetal health and predicted fetal
 outcomes.
 The present invention is also directed to a method for automatically
 predicting the future health of a particular person based on present data
 about the person. The method includes automatically developing inference
 rules based on known data about persons and known data about the future
 health of those same persons and receiving physical data about the present
 status of a particular person. The method also includes automatically
 processing the received data to generate a set of characteristic features
 and applying the inference rules to the derived characteristic features to
 predict the future health of the particular person.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
 The fetal outcome predictor and monitoring system of the present invention
 includes an Intelligent Fetal Monitoring System (IFMS) 10 as shown in FIG.
 1. The IFMS 10 is used in conjunction with an Obstetrical Information
 Management System (OIMS) 12 that receives, processes, and stores data from
 a patient, such as an expectant mother 14. A user 16, such as a physician,
 can use the IFMS 10 to assist in making decisions about the course of
 treatment to be taken for the expectant mother, such as continuing to wait
 for a child to be delivered vaginally or whether to intervene and deliver
 the child by cesarean section. The IFMS 10 can be used in a physician's
 office prior to the mother entering labor, or can be used once a mother
 has entered labor whether in a physician's office, at the hospital, or
 elsewhere. The IFMS 10 can be used to predict fetal outcomes either: (1)
 for births that might occur at the time of prediction; or (2) for births
 that might occur at some predetermined time in the future (e.g., twenty
 minutes) after the prediction. Alternatively, although not specifically
 addressed herein, the teachings of the present invention could be adapted
 to predict many other types of patient outcomes based on current data.
 The OIMS 12 preferably includes, among other things, a transducer (not
 shown) for converting uterine contractions of the mother to electrical
 signals, as well as a transducer (not shown) for measuring the heart rate
 of the fetus inside the mother. The FHR and UC data in the form of
 electrical signals are passed along an RS-232 cable 18 from the OIMS 12 to
 the IFMS 10. Any other suitable means of communication other than an
 RS-232 cable could also be used. The OIMS 12 may include any suitable
 system for performing these unctions. Examples of such systems are
 produced by Hewlett-Packard, Corometrics, Toshiba, General Electric,
 Siemens, and others. For example, the OIMS 12 may be an HP Series 50 OB
 TraceVue system, Model No. M1381A, Rev. A.01 with an HP Bedside Monitor,
 Model No. HP 8440A, available from Hewlett-Packard, or any other suitable
 system.
 As is also shown in FIG. 1, the IFMS 10 can be implemented in any suitable
 processor such as a personal computer. An example of such a suitable
 computer is any PC-based computer having the following minimum system
 specifications: Pentium II processor or equivalent, operating at 300 MHz.;
 128 Megabytes of RAM; 2 Gigabytes of disk drive storage; an RS-232 serial
 port; a 2-D graphics accelerator; and Windows NT software, version 4.0.
 Any PC meeting the preceding specifications or better should serve as a
 sufficient platform for the IFMS 10. As PCs with higher speed, greater
 capacity, and other improved capabilities continue to become available, it
 may be preferable to implement the IFMS 10 in such improved PCs.
 The data processing performed by the IFMS 10 can be seen to be divisible
 into several different functional blocks, as shown in FIG. 2. The IFMS
 includes seven components, grouped into three layers: the top layer is the
 learning/ supervisory level of the system, the intermediate layer is the
 system adaptation level, and the bottom layer is the analysis and
 evaluation level which performs the result analysis, explanation, display
 and system evaluation functions.
 During patient monitoring, the new FHR and UC input received from the OIMS
 12 will enter the Case-Base 20 for data preprocessing (i.e., formatting
 and noise filtering). The Case-Based Reasoning block 22 will then perform
 case-based reasoning, based on a priori knowledge stored in the Case-Base
 20 and patient information, to select an appropriate existing case for use
 in fetal signal analysis, FHR and UC features extraction and selection,
 and for fetal outcome prediction. The selected case will dictate a
 specific data extension mode to be performed by Data Extension block 24,
 and a specific set of FHR and UC features to be extracted from the input
 data by the Features Extraction and Selection block 26. These features
 will be used by the Outcome Prediction block 28 to accurately predict
 fetal outcome. The outcome prediction result will be analyzed, explained
 and displayed on the terminal screen of the IFMS 10. It will also be
 evaluated by the Result Analysis, In. Explanation, Display and Evaluation
 block 30. The system performance evaluation result will be used by the
 Rule-Base Adjustment block 32 for rule-base adjustment so that the next
 fetal outcome prediction for similar case will be better. Finally, the
 knowledge-base 20 will be updated with new/enhanced fuzzy models under the
 coordination of the Case-Based Reasoning block 22.
 Following is a detailed description for each processing block.
 1. Case-Base:
 The Case-Base block 20 includes a knowledge-base and a Case-Based
 Management System (CBMS). The knowledge-base is a data-base containing two
 types of information. The first type of information is patient
 biographical data which is grouped into various cases. The biographical
 data could include any of various types of data, such as gender
 (male/female), gestational age (e.g., less than or above 40 weeks, or
 relative to some other age), medication (normal, diabetic, hypertension,
 narcotic. Etc.), and fetal outcome including arterial cord blood gas
 base-excess (CG-artery-BE) and arterial cord blood gas pH (CG-artery-pH).
 The second type of information is an optimal set of features which
 uniquely characterizes the dynamic behavior of the patient FHR and UC
 responses, and the fuzzy relationship (i.e., fuzzy model) between these
 features and the fetal outcome for each case. These fuzzy models were
 derived from patient data during system training/learning. The CBMS was
 developed to allow an efficient case information storage and retrieval,
 quick model access, and automatic data analysis for accurate outcome
 prediction.
 2. Case-Based Reasoning:
 The main function of the Case-Based Reasoning block 22 is the selection of
 an appropriate case and its corresponding fuzzy model for fetal outcome
 prediction. This type of reasoning is used to assign a patient to one of
 the existing cases in the case-base, if there is a match (i.e., high
 correlation) between the characteristic features derived from the FHR and
 UC input and the ones in the case-base; or to generate a new case. When
 there is a match, the fuzzy model derived for this case during system
 training will be used for outcome prediction. If a new case has to be
 generated, the fuzzy model of the closest case (i.e., case having highest
 correlation with the input features) will be used as a starting model for
 outcome prediction after some adjustments.
 Model adjustment is done iteratively by the Rule-Base Adjustment block 32
 through a learning algorithm to minimize the outcome prediction error.
 3. Data Extension:
 The IFMS has the capability of providing 20-minute forward data extension
 so that clinicians may assess current and/or predicted fetal status and
 balance his/her decision properly. This function is performed by the Data
 Extension block 24 which uses on-line structure adjustment and weight
 learning adaptation algorithms of the Volterra Polynomial Basis Function
 (VPBF) network for dynamic data prediction.
 Consider the nonlinear discrete system described by:
EQU X.sub.t+1 =G(X.sub.t, u.sub.t) y.sub.t =h(X.sub.t, u.sub.t) (1)
 where G(.) is a nonlinear function vector, h(.) a nonlinear function,
 X.sub.t the state vector, y.sub.t the output, and u.sub.t the input. On
 the basis of the input and output relation of a system, the above
 nonlinear discrete system can also be expressed by a NARMA (Nonlinear
 Auto-Regressive Moving Average) model:
EQU y.sub.t =f(y.sub.t-1, y.sub.t-2, . . . , y.sub.t-n, u.sub.t-1, u.sub.t-2, .
 . . , u.sub.t-m) (2)
 where f(.) is some nonlinear function, n and m are the corresponding
 maximum delays.
 The nonlinear function f(.) in the NARMA model can be approximated by a
 single-layer neural network. This includes a linear combination of basis
 functions
 ##EQU1##
 where x.sub.t =[y.sub.t-1, y.sub.t-2, . . . , y.sub.t-n, u.sub.t-1,
 u.sub.t-2, . . . , u.sub.t-m ], .phi..sub.k (x.sub.t) is the basis
 function and w.sub.k the weight.
 Using Volterra polynomials as the basis functions, the representation of
 the nonlinear function f(x.sub.t) is then given by:
EQU f*(x.sub.t)=w.sub.1 +w.sub.2 y.sub.t-1 +w.sub.3 y.sub.t-2 + . . .
 +w.sub.n+1 y.sub.t-n
EQU +w.sub.+2 u.sub.t-1 + . . . +w.sub.n+m+1 u.sub.t-m
EQU +w.sub.n+m+2 y.sup.2.sub.t-1 +w.sub.n+m+3 y.sub.t-1 y.sub.t-2 + . . .
 w.sub.N u.sup.l.sub.l-mN
EQU =.SIGMA.w.sub.k.phi..sub.k (x.sub.t).sub.k=1 (4)
 where
EQU [.phi..sub.1, .phi..sub.2, .phi..sub.3, . . . , .phi..sub.n+1,
 .phi..sub.n+2, . . . , .phi..sub.n+m+1, .phi..sub.n+m+2, .phi..sub.n+m+3,
 . . . , .phi..sub.N ](X.sub.t)
EQU =[1, y.sub.t-1, y.sub.t-2, . . . , y.sub.t-m, u.sub.t-1, . . . , u.sub.t-m,
 y.sup.2.sub.t-1, y.sub.t-1 y.sub.t-2, . . . , u.sup.l.sub.l-m ] (5)
EQU N=(n+m+l)!/[l!(n+m)!] (6)
 is the set of the Volterra polynomial basis functions.
 Increasing the order l, the number N of basis functions becomes larger and
 larger.
 The IFMS estimates the function f*(x.sub.t) using a proper-sized neural
 network so that the approximation accuracy is within the required bound.
 The estimated function f*(x.sub.t) in the NARMA model can also be expressed
 by
EQU f*(x.sub.t)=W.sub.t-1.sup.T.PHI..sub.t-1 (7)
 where the weight vector W.sub.t-1 and the basis function vector
 .PHI..sub.t-1 are
EQU W.sub.t-1 =[w.sub.1 (t-1) w.sub.2 (t-1) . . . w.sub.L (t-1)].sup.T (8)
 .PHI..sub.t-1 =[.phi..sub.1.degree.(x.sub.t) .phi..sub.2.degree.(x.sub.t)
 . . . .phi..sub.L.degree.(x.sub.t)].sup.T (9)
 and the initial weight vector is W.sub.o =[w.sub.1.degree. w.sub.2.degree.
 . . . w.sub.L.degree.].sup.T.
 Let y.sub.t be the system output. The estimation problem is then to find a
 vector W belonging to the set defined by
EQU .XI.(W)={W:.vertline.y.sub.t
 -W.sup.T.PHI..sub.t-1.vertline..ltoreq..delta..sub.L,
 .A-inverted.t.epsilon.N.sup.+ }. (10)
 A recursive weight learning algorithm for the VPBF network is as follows:
EQU W.sub.t =W.sub.t '-.alpha..sub.t.beta..sub.t.eta..sub.t
 P.sub.t.PHI..sub.t-1 e.sub.t (11)
EQU W.sub.t '=W.sub.t-1 +.alpha..sub.t.beta..sub.t P.sub.t.PHI..sub.t-1 e.sub.t
 (12)
EQU P.sub.t =P.sub.t-1 -.beta..sub.t.gamma..sub.t
 P.sub.t-1.PHI..sub.t-1.PHI..sub.t-1.sup.T P.sub.t-1 (13)
EQU e.sub.t =y.sub.t -W.sub.t-1.sup.T.PHI..sub.t-1 (14)
EQU .alpha..sub.t
 =(1-.delta..vertline.e.sub.t.vertline..sup.-1)(1+.PHI..sub.t-1.sup.T
 P.sub.t-1.PHI..sub.t-1).sup.-1 (15)
 ##EQU2##
 .gamma..sub.t
 =(.vertline.e.sub.t.vertline.-.delta.)(.vertline.e.sub.
 t.vertline.+(2.vertline.e.sub.t.vertline.-.delta.).PHI..sub.t-1.sup.T
 P.sub.t-1.PHI..sub.t-1).sup.-1 (17)
 ##EQU3##
 where the lower and upper bounds of .eta..sub.t are given by
EQU s.sup.- =1+(.alpha..sub.t e.sub.t.PHI..sub.t-1.sup.T P.sub.t W.sub.t-1
 -c.sub.t)/.parallel..alpha..sub.t e.sub.t
 P.sub.t.PHI..sub.t-1.parallel..sub.2.sup.2 (19)
EQU s.sup.+ =1+(.alpha..sub.t e.sub.t.PHI..sub.t-1.sup.T P.sub.t W.sub.t-1
 +c.sub.t)/.parallel..alpha..sub.t e.sub.t
 P.sub.t.PHI..sub.t-1.parallel..sub.2.sup.2 (20)
 where
 ##EQU4##
 M is the upper bound of the 2-norm of the weight vector W.sub.t, and
 .delta. is the desired approximation error.
 The IFMS 10 displays the 20-min extension of the FHR & UC tracings in a
 separated window, with features enhanced by different colors. This option
 will be activated by a button on the main screen menu.
 4. Features Extraction and Selection:
 The function of the Feature Extraction and Selection block 26 includes
 extracting and selecting an appropriate set of fetal heart rate (FHR)
 features and uterine contraction (UC) features for proper characterizing
 patient characteristics. The features that will be automatically generated
 by the IFMS 10 include traditional features and new features.
 Some of the traditional features for FHR include:
 Baseline FHR: compiled over 5 minutes.
 Beat-to-beat variability: compiled over 5 minutes.
 Ten-second variability: compiled over 10 seconds: 30 such distributions
 combined to give one value for each 5 minutes.
 One-minute variability: compiled over 1 minute: 5 such distributions
 combined to give one value for each 5 minutes.
 Number of accelerations of .gtoreq.10 bpm for .gtoreq.15 seconds: number in
 5 minutes.
 Number of accelerations of .gtoreq.15 bpm for .gtoreq.15 seconds: number in
 5 minutes.
 Number of decelerations of .gtoreq.10 bpm for .gtoreq.60 seconds: number in
 5 minutes.
 Deceleration duration: average of all in 5 minutes.
 Deceleration depth: average of all in 5 minutes.
 Contraction number: number in 5 minutes
 Contraction area (for one cycle): average of all in 5 minutes.
 There are also new features derived from the scale-space domain.
 The actual features used may be the entire set, or a subset, of the
 preceding features. Alternatively, additional features may be used. The
 actual features used will be automatically selected by the system.
 First, a gaussian convolution is used as a primitive
 scale-parameterization, as shown in FIG. 5. The gaussian convolution of a
 signal f(x) depends both on x, the signal's independent variable, and on
 .sigma., the gaussian's standard deviation. The convolution is given by:
EQU F(x,.sigma.)=f(x)*g(x,.sigma.)=.sub.-.infin..function..sup..infin.
 f(u)(1/.sigma.(2+L .pi.))exp{-(x-u).sup.2 /(2.sigma..sup.2)}du (22)
 where "*" denotes convolution with respect to x. This function defines a
 surface on the (x, .sigma.)-plane, where each profile of constant .sigma.
 is a gaussian-smoothed version of f(x), the amount of smoothing increasing
 with .sigma.. We will call the (x,.sigma.)-plane scale-space, and the
 function F, defined in (22), the scale-space image of f.
 Then, at any given value of .sigma., the extrema in the n.sup.th
 -derivative of the smoothed signal are given by the zero-crossings in the
 (n+1).sup.th derivative, computed using the relation:
EQU .differential..sup.n F/.differential.x.sup.n =f*(.differential..sup.n
 g/.differential.x.sup.n) (23)
 where the derivatives of the gaussian are readily obtained. In terms of the
 scale-space image, the inflections at all values of .sigma. are the points
 that satisfy
EQU F.sub.xx =0F.sub.xxx.noteq.0 (24)
 Using subscript notation to indicate partial differentiation. As shown in
 FIG. 6, the contours of F.sub.xx =0 mark the appearance and motion of
 inflection points in the smoothed signal, and provide the raw material for
 a qualitative description over all scales, in terms of inflection points.
 Finally, the scale-space image is reduced to a simple interval tree, as
 shown in FIG. 7, concisely but completely describing the qualitative
 structure of the signal over all scales of observation. This
 simplification rests on the basic property of the scale-space image: as
 .sigma. is varied, extremal points in the smoothed signal appear and
 disappear at singular points (the tops of the arches). Passing through
 such a point with decreasing a, .sigma. pair of extrema of opposite sign
 appear in the smoothed signal. At these points, and only these points, the
 undistinguished interval in the which the singularity occurs splits into
 three subintervals. In general, each undistinguished interval, observed in
 scale-space, is bounded on each side by the zero contours that define it,
 bounded above by the singular point at which it merges into an enclosing
 interval, and bounded below by the singular point at which it divides into
 sub-intervals. Consequently, to each interval I corresponds a node in a
 tree, whose parent node denotes the larger interval from which I merged,
 and whose offspring represent the smaller intervals into which I
 subdivide. Each interval also defines a rectangle in scale-space, denoting
 its location and extent on the signal and its location and extent on the
 scale dimension. Collectively, these rectangles tesselate the
 (x,.sigma.)-plane. The interval tree may be viewed in two ways: as
 describing the signal simultaneously at all scales, or as generating a
 family of single-scale descriptions, each defined by a subset of nodes in
 the tree that cover the x-axis. The interval tree seems to be flexible
 enough to capture human perceptual intuitions. It was used to extract
 scale-space features related to FHR accelerations and decelerations.
 Since the number of features derived from the FHR and UC tracings are
 numerous, only the most contributing features are selected to build the
 fuzzy model for fetal outcome prediction for each case. In the IFMS, the
 feature selection task is done through a special clustering algorithm
 which determines a cluster-specific contribution of each feature to the
 variance of the data. The contribution weight of a feature is proportional
 to the deviation (squared) of the feature's within-cluster mean from its
 grand mean. The more deviant a feature is from a standard (the grand mean,
 in this case), the more interesting it is. Each contribution weight is a
 part of clustering criterion to be maximized, not a posterior quality
 measure. A "separate-and-conquer" version of the K-Means clustering method
 produces clusters one by one, not simultaneously, and relaxes the problem
 of defining a partition size in advance. The features with greatest
 contribution towards a cluster (or cluster structure) were used to
 generate a fuzzy model that approximately describes the behavior of the
 data subset within the cluster.
 5. Outcome Prediction:
 The Outcome Prediction block 28 uses the selected/adjusted fuzzy model to
 predict fetal outcome. The fuzzy model used by the IFMS 10 is a
 multi-input single-output (MISO) linguistic model of Takagi-Sugeno type to
 represent the fuzzy relationship between the input features and fetal
 outcome:
EQU IF u.sub.1 is B.sub.i1 AND . . . AND u.sub.r is B.sub.ir THEN y.sub.i
 =b.sub.i0 +b.sub.i1 u.sub.1 + . . . +b.sub.ir u.sub.r i=(1,m) (25)
 assuming that the fuzzy model has m rules. The crisp output inferred by
 this fuzzy model according to the Min-Max method of fuzzy reasoning is
 ##EQU5##
 where y.sub.i *denotes the predicted outcome based on the ith fuzzy rule,
 and .tau..sub.i, i=(1,m) are the degrees of firing (DOF) of the rules:
EQU .tau..sub.i =B.sub.s1 (u.sub.1) B.sub.i2 (u.sub.2) . . . B.sub.ir
 (u.sub.r) (27)
 for given crisp input values u.sub.1, u.sub.2, . . . , u.sub.r.
 To obtain an analytical expression of the transformation input-output we
 replace the min operator in (27) by the product and, in addition, assume
 that the reference antecedent fuzzy sets are defined by Gaussian
 membership functions:
EQU B.sub.ij (u.sub.j)=exp{-1/2[(u.sub.j -x.sub.ij *)/.sigma..sub.ij ].sup.2 }
 (28)
 with parameters x.sub.ij * and .sigma..sub.ij. Then we obtain the following
 expression for the DOF of the ith rule:
EQU .tau..sub.i =B.sub.i1 (u.sub.1).B.sub.i2 (u.sub.2) . . . B.sub.ir (u.sub.r)
 ##EQU6##
 By substitution of (29) into (26) we obtain an expression for the crisp
 output of the linguistic model that is determined by the parameters of the
 antecedent and consequent fuzzy sets y.sub.i *, x.sub.ij *, and
 .sigma..sub.ij, i=(1,m), j=(1,r):
 ##EQU7##
 where v.sub.i, i=(1,m) are the normalized DOF of the individual rules:
 ##EQU8##
 Using (30) we can represent the linguistic model (25) as a three-layer
 network as shown in FIG. 3. The output of this model will then be given
 by:
 ##EQU9##
 6. Result Analysis, Explanation, Display and Evaluation:
 Based on the predicted outcome from the Outcome Prediction block 28, the
 Analysis, Explanation, Display and Evaluation block 30 will use a rule set
 to explain the behavior of the fetus. This explanation may take any of
 several forms, including acoustic or visual forms. For example, one of the
 following statements may be displayed in the "Advice window" on the
 terminal screen:
 "No evidence of fetal distress."
 "Further evaluation may be necessary."
 "Non-reassurring tracing."
 Other statements or alarms could also be provided for the user.
 The Display function of this block also provides a full display capability
 for the user to review the FHR and UC input tracings, with color
 enhancement of various extracted features for ease of information
 assessment.
 Another function performed by the Analysis, Explanation, Display and
 Evaluation block 30 is the evaluation of outcome prediction performance of
 the Outcome Prediction block 28. It uses an outcome prediction error
 measure to assess the prediction accuracy and guides the IFMS system
 tuning during the system learning and training phase with known data sets.
 7. Rule-Base Adjustment:
 The learning algorithm for a multi-input single-output (MISO) linguistic
 model (22) was derived as follows. For a given collection of crisp
 input-output data (u.sub.1k, u.sub.2k, . . . , u.sub.rk), k=(1,K), we can
 formulate the model parameter estimation problem as a minimization of the
 square of chi instantaneous errors between the output y of the fuzzy model
 (22) and the current output reading y.sub.k with respect to the unknown
 parameters:
EQU E.sub.k =1/2(y-y.sub.k).sup.2 =1/2e.sup.2 (33)
 We then obtain the following rules for back-propagation learning for fuzzy
 model adjustment:
 b.sub.i0 (k+1)=b.sub.i0 (k)-.alpha.(.differential.E.sub.k
 /.differential.b.sub.i0)=b.sub.i0 (k)-.alpha.v.sub.i e (34)
EQU b.sub.ij (k+1)=b.sub.ij (k)-.alpha.(.differential.E.sub.k
 /.differential.b.sub.ij)=b.sub.ij (k)-.alpha.v.sub.i eu.sub.j, j=(1,r)
 (35)
EQU x.sub.ij *(k+1)=x.sub.ij *(k)-.alpha.(.differential.E.sub.k
 /.differential.x.sub.ij)=x.sub.ij *(k)-.alpha.v.sub.i (b.sub.i0 +b.sub.i1
 u.sub.1 + . . . +b.sub.ir u.sub.r -y)e[(u.sub.j -x.sub.ij
 *(k))/.sigma..sub.ij.sup.2 (k)] (36)
EQU .sigma..sub.ij (k+1)=.sigma..sub.ij
 (k)-.alpha.(.differential.Ek/.differential..sigma..sub.ij)=.sigma..sub.ij
 (k)-.alpha.v.sub.i (b.sub.i0 +b.sub.i1 u.sub.1 + . . . +b.sub.ir u.sub.r
 -y)e[(u.sub.j -x.sub.ij *(k)).sup.2 /.sigma..sub.ij.sup.3 (k)] (37)
 where .alpha.&gt;0 is the learning rate.
 The bloc-diagram of the learning algorithm, combining with the three-layer
 neural network is shown in FIG. 4. The parameter updating process stops
 when .DELTA.y.sub.i *, .DELTA.x.sub.ij *, and .DELTA..sigma..sub.ij are
 sufficiently small (i.e., less than a pre-specified threshold).
 In the forward pass, we calculate the current DOF of the rules, the
 .tau..sub.i 's and their normalized values v.sub.i 's, and the estimated
 output of the fuzzy model y; current estimates y.sub.j *(k), x.sub.ij
 *(k), and .sigma..sub.ij (k) are used in this calculation. In the backward
 pass, the current parameter estimates y.sub.i *(k), x.sub.ij *(k) and
 .sigma..sub.ij (k) are updated according to the learning rules (34), (35),
 (36), and (37) with rates:
 .DELTA.y.sub.i *=-.alpha.v.sub.i e; .DELTA.x.sub.ij *=-v.sub.i e(y.sub.i
 *-y)[(u.sub.j -x.sub.ij *(k))/.sigma..sub.ij.sup.2 (k)];
 .DELTA..sigma..sub.ij =-.alpha.v.sub.i e(y.sub.i *-y)[(u.sub.j -x.sub.ij
 *(k)).sup.2 /.sigma..sub.ij.sup.3 (k)] (38)
 where y.sub.i * is given by:
EQU y.sub.i *=b.sub.i0 +b.sub.i1 u.sub.1 + . . . +b.sub.ir u.sub.r. (39)
 All of the above data processing is performed by a computer as described
 above. As currently being developed, the computer has been programmed in
 the C.sup.++ programming language. Alternatively, the computer could be
 programmed in some other suitable programming language, or all or portions
 of the data processing functions could be implemented in hardware.
 The foregoing description is considered as illustrative only of the
 principles of the invention. Furthermore, since numerous modifications and
 changes will readily occur to those skilled in the art, it is not desired
 to limit the invention to the exact construction and process shown as
 described above. Accordingly, all suitable modifications and equivalents
 may be resorted to falling within the scope of the invention as defined by
 the claims which follow.