J

NASA CASE NO.

PRINT FIG.

iYOEICE

/

. /

The invention disclosed in this document resulted from research in aeronautical and

space activities performed under programs of the National Aeronautics and Space

Administration. The invention is owned by NASA and is, therefore, available for

licensing in accordance with the NASA Patent Licensing Regulation (14 Code of

Federal Regulations 1245.2).

To encourage commercial utilization of NASA-owned inventions, it is NASA policy to

grant licenses to commercial concerns. Although NASA encourages nonexclusive

licensing to promote competition and achieve the widest possible utilization, NASA

will provide the necessary incentive to the licensee to achieve early practical

application of the invention.

Address inquiries and all applications for license for this invention to NASA Patent

Counsel, Langley Research Center, Code 212, Hampton, Virginia 23681-0001.

Serial No.: 08/323,943

10/13/94 LaRC

(NASA-Case-LAR-15129-1) PASSIVE

FETAL HEART RATE MONITORING

APPARATUS AND METHOD WITH ENHANCED

FETAL HEART BEAT DISCRIMINATION

Patent Application (NASA. Langley

Research Center) 26 p

G]/52

N95-14184

Unclas

0029850
LAR-15129-1 AWARDS ABSTRACT 

PASSIVE FETAL HEART RATE MONITORING APPARATUS AND METHOD WITH ENHANCED 

FETAL HEART BEAT DISCRIMINATION 

The invention relates in general to the determination of the fetal heart 

rate of a fetus inside an expectant mother through the identification of 

fetal heart beats, and more specifically, to an improved method and apparatus 

for detecting fetal heart beats from the signals generated by a passive fetal 

heart rate monitoring sensor and determining a fetal heart rate based on the 

identified fetal heart beats. 

According to the present invention, a passive fetal heart rate 

monitoring sensor is used to acquire a plurality of acoustic signals 

eminatting from a fetus. These signals are amplified, filtered, multiplexed, 

and digitized. The plurality of digitized signals are then simultaneously 

filtered by multiple parallel nonlinear filters to identify fetal heart 

beats. Estimates of a fetal heart rate are made from the identified fetal 

heart beats for each of the plurality of signals for each filtering method. 

A figure of merit is then used to weigh each fetal heart rate estimate thus 

generated and the highest ranking fetal heart rate estimate is selected as 

the most accurate fetal heart rate for a specific time frame. The fetal 

heart rate is displayed, stored, or outputted to a communications channel. 

The invention does not require the use of reference signals such as that 

used in adaptive cancellation techniques. Moreover, the use of parallel 

nonlinear filtering by a neural network and a Teager Energy Operator is 

superior to existing methods in the industry. There is also the 

determination of a plurality of fetal heart rate estimates based on 

identified fetal heart beats from each filtering result for each of the 

plurality of sensor signals. The ranking of this plurality of fetal heart 

rate estimates and the selection of the highest ranking fetal heart rate 

estimate increases the chances for identifying an accurate fetal heart rate. 

Inventors: Stephen A. Zahorian 543 Virginia Ave. 

SS# Norfork, VA 23508 

Employer: Old Dominion University, Norfolk, VA 

David L. Livingston 4927 Cherokee Hills Dr. 

ss# Salem, VA 24153 

Employer: Old Dominion University, Norfolk, VA 

Robert A. Pretlow I11 18109 NE 76th 

SS# Redmond, WA 98052 

Empl ed Eng. & Sciences, Hampton VA 

Initial Evaluator: Joseph S. Heyman 

Serial No. : 08/323,943 

Filed: 10/13/94 
LAR 15129-1 -1 - PATENT APPLICATION

PASSIVE FETAL HEART RATE MONITORING APPARATUS AND METHOD

WITH ENHANCED FETAL HEART BEAT DISCRIMINATION

Ori.qin of the Invention

5

The invention described herein was jointly made in the performance of

work under a NASA contract and under a NASA Grant, and is subject to Section

305 of the National Aeronautics and Space Act of 1958, as amended, Public

Law 85-568 (72 Stat. 435; 42 USC 2457) and Public Law 96-517 (35 U.S.C. 200

et seq.). In accordance with 35 USC 202, the grantee elected not to retain title.

Back,qround of the Invention

10 Field of the Invention

15

The invention relates in general to the determination of the fetal heart rate

of a fetus inside an expectant mother through the identification of fetal heart

beats, and more specifically, to an improved method and apparatus for detecting

fetal heart beats from the signals generated by a passive fetal heart rate

monitoring sensor and determining a fetal heart rate based on the identified fetal

heart beats.

Description of the Related Art

20

Various methodologies have been employed in the industry to determine

fetal heart rates within a noise-contaminated signal from a fetal heart monitoring

sensor. For example, a well known method in the industry is bandpass filtering

which removes low frequency noise from heart beat signals. However,

bandpass filtering fails to adjust for background noise frequencies which change

with respect to time. A more sophisticated methodology which adapts to
LAR 15129-1 -2- PATENT APPLICATION

changing background noise is described in U.S. Patent Number 4,781,200 to

Baker. The Baker patent discloses a portable, non-invasive system for

monitoring fetal heart rate employing adaptive cancellation and spectral

analysis. The Baker system is essentially an ambulatory system which uses

5 adaptive interference cancelling to isolate fetal heart beats and frequency

domainanalysesof multi-channelsignals to extract useful information pertaining

to fetal well-being, including fetal heart rate and absence of fetal movement.

Another system,described in U.S. Patent Number 5,209,237 to Rosenthal, also

employs a similar noise cancellation technique, but uses multiple reference

10 sensors to detect various background noises for adaptive cancellation and

subsequent time-domain, cross-spectral frequency analysis. While there are

certain benefits in using adaptive cancellation and spectral analysis, the use of

interference cancellation to identify fetal heart beats is dependent on the quality

of the reference signals. Noise not included in the reference signal may not be

15 filtered from the signals emanating from the abdomen. While generally known

sources of noise, such as the mother's heart beat and breathing, are easily

identified and filtered, extraneous noise from other organs or from the external

environment may be missed. Therefore, the resulting signal may not have been

sufficiently filtered of noise and an inaccurate fetal heart rate may have been

20 determined from erroneous fetal heart beat data.

The use of a linear prediction methodology has also been used in the

industry as the basis for identifying fetal heart beats during the determination of

a fetal heart rate. Such an approach is described in Pretlow III, R. A. and

Stoughton, J.,"Signal Processing Methodologies for an Acoustic Fetal Heart

25 Rate Monitor," Master's Thesis, April, 1991, Old Dominion University (hereafter

"Pretlow et al."). However, such a methodology, while an improvement over

adaptive cancellation, is still sensitive to noise not related to the fetal heart beat.

Moreover, linear prediction has a significant deficiency inherent in the linear

nature of its filter structure which prevents it from accurately modeling the

30 nonlinearity of the background noise. The use of a two-layer, feed forward
LAR 15129-1 -3- PATENT APPLICATION

neural network has been suggested for use in identifying heart beats and

determining heart rates. Such a method is discussed in detail in Hu, Y. H.,

Tompkins, W. J., and Xue, Q., "Artificial Neural Network for ECG Arrhythmia

Monitoring," Proceedings of IJCNN Intl. Joint Conf. on Neural Networks, 1992,

5 vol. 2 (hereafter "Hu et al."). However, the drawback of the methodology

discussed by Hu et al. is that it concentrates on eliminating noise from ECG data

and does not deal with supporting an acoustic-based monitoring apparatus.

Indeed, the ECG environment differs significantly from the acoustic signal

environment for fetal heart beats in terms of the type and amount of noise and

10 the strength of the heart beat signal. The previously-mentioned Pretlow et al.

article acknowledges the possibility of using a neural network to perform

signature matching to identify a fetal heart beat, but concludes that neural

network processing works best in offline waveform processing, such as for ECG

analysis, and has not been found practical for real time fetal heart beat

15 detection.

Summary of the Invention

20

25

Accordingly, it is an object of the invention to overcome the previously

described problems and deficiencies in existing technology for identifying fetal

heart beats and determining an accurate fetal heart rate.

In addition, it is an object of the invention to provide an improved

apparatus and method for identifying fetal heart beats and determining fetal

heart rates from signals generated from an acoustic fetal heart monitoring

sensor.

It is a further object of the invention to provide a more accurate apparatus

and method for identifying a fetal heart beat, through the simultaneous nonlinear

filtering of a plurality of acoustic signals, generated by a passive fetal heart rate

monitoring sensor, by the use of a Teager Energy Operator and/or a neural

network nonlinear filter or filters, and determining a fetal heart rate based upon
LAR 15129-1 -4- PATENT APPLICATION

the identified fetal heart beats.

In order to achieve the foregoing and other objects, in accordance with

the purposes of the present invention as described therein, an apparatus for

identifying fetal heart beats and determining a fetal heart rate comprises a

5 passive fetal heart monitoring sensor producing a plurality of acoustic signals,

multiplenonlinear filters simultaneously filtering the plurality of sensor signals,

and means for determining fetal heart rates. As embodied herein, there is a

passive fetal heart rate monitoring sensor which acquires acoustic signals

emitted from a fetus inside a body and outputs a plurality of sensor signals; a

10 signal processing device, which receives, amplifies, filters, multiplexes, and

digitizes the plurality of sensor signals, and which outputs a plurality of

processedsignals; multiple parallel nonlinear filters, which receive the plurality

of processedsignals, filter each of the plurality of processed signals to identify

fetal heart beats, and output a plurality of functional values indicative of the

15 presenceof fetal heart beats for each of the plurality of processed signals; and

fetal heart rate determining means, responsive to the plurality of functional

values indicative of the presence of fetal heart beats, for determining and

outputting a fetal heart rate.

A methodfor identifyingfetal heart beatsand determiningfetal heart rates

20 comprises multiple parallel nonlinear filtering of a plurality of acoustic sensor

signals generated from a passive fetal heart rate monitoring sensor and steps

for determining a fetal heart rate based on the identified heart beats. As

embodied herein, the method comprises a step for acquiring acoustic signals

emitted from the fetus and outputting a plurality of sensor signals, a step for

25 receiving,amplifying,filtering, multiplexing, and digitizing the plurality of sensor

signals to produce a plurality of processed signals, a step for multiple parallel

nonlinear filtering of each of the plurality of processed signals to identify the

presence of fetal heart beats and to output a plurality of functional values

indicative of the presence of fetal heart beats for each of the plurality of

30 processedsignals, and a stepfor determiningand outputting the fetal heart rate
LAR 15129-1 -5- PATENT APPLICATION

based on the plurality of functional values indicative of the presence of fetal

heart beats.

Brief Description of the Drawin,qs

The present invention and the objects achieved by it will be understood

5 from the description of the preferred embodiments, taken in conjunction with the

accompanying drawings of which:

FIG. 1 is an overview of the hardware platform for an embodiment of an

apparatus according to the present invention.

FIG. 2 is a block diagram of the pre-processing hardware, the signal

10 processing system, and the computer for an embodiment of an apparatus

according to the present invention.

FIG. 3 is a flow chart illustrating the multiple parallel nonlinear filtering for

the multiple sensor signals and the determination of a fetal heart rate for an

embodiment of a method according to the present invention.

15 FIG. 4 is a graph depicting the time domain acoustic fetal heart signal and

the Teager Energy Operator response to the acoustic signal according to an

embodiment of the present invention.

FIG. 5 is a schematic view illustrating a neural network according to an

embodiment of the present invention.

20 FIG. 6 is a schematic view illustrating a particular unit in the neural

network according to an embodiment the present invention.

FIG. 7 is a graph showing the time domain acoustic fetal heart signal and

the neural network response to the signal according to an embodiment of the

present invention.

25 FIG. 8 is a graph depicting the fetal heart rate tracing obtained from

acoustic monitoring of a patient with a gestation age of 39 weeks.
LAIR15129-1 -6- PATENT APPLICATION

Detailed Description of the Preferred Embodiments

Reference will now be made in detail to the present preferred

embodiments of the present invention, examples of which are illustrated in the

accompanying drawings, wherein like reference numerals refer to like elements

5 throughout.

FIG. 1 is an overview of the hardware platform for an apparatus according

to an embodiment of the present invention. In FIG. 1, an ambulatory, noninvasive, passive, fetal heart rate monitoring sensor 1 is placed on the abdomen

of an expectant mother. Multiple sensor elements inside sensor 1 receive the

10 signals emitted by a fetus inside the expectant mother and output a plurality of

corresponding sensor signals 2, commensurate with the number of sensor

elements inside sensor 1. Such ambulatory, non-invasive, sensor elements are

well known in the industry and an example of this type of sensor 1 is described

in U.S. Patent Number 5,140,992 to Zucherwar et al. entitled "PASSIVE FETAL

15 MONITORING SENSOR." The plurality of sensor signals 2 are received by a

preprocessing electronic unit 3 which amplifies, filters, and multiplexes the

signals in order to prepare and organize the sensor signals 2 for further

processing. The preprocessed signals outputted by the preprocessing

electronic unit 3 are received by the signal processing system 4 which

20 demultiplexes the preprocessed signals back into multiple sensor signals and

digitizes the preprocessed multiple sensor signals for further analysis by an

improved method of identifying fetal heart beats and determining a fetal heart

rate. In particular, multiple parallel nonlinear filters are used in achieving

significantly more accurate results for the identification of fetal heart beats.

25 Moreover, the determination of multiple fetal heart rate estimates, one estimate

from the result of each nonlinear filtering of each sensor signal, and the use of

a figure of merit method to select the best fetal heart rate estimate ensures the

most accurate result from this system. The signal processing system 4 is

programmed to carry out these functions of heart beat identification and heart
LAR 15129-1 -7- PATENT APPLICATION

rate determination.

As to the signal processing system 4, one embodiment of the present

invention has the signal processing system 4 inside a personal computer (PC)

5. The PC5 is well known inthe industryand, as shown in FIG. 2, is comprised

5 of a central processing unit (CPU) 12, memory 13, and fixed disk storage 14;

and further, has attached a keyboard 15 and a display 16. The PC 5 is used to

carry out input from and outputto variousdevices, such as the keyboard 15, the

display 16, or a communications channel (not shown). The signal processing

system4 is not limitedto being physically locatedinsidea PC. For example, the

10 preprocessing electronic unit 3 and the signal processing system 4 can be

combined in a signal processing device separate from the PC. In addition, the

embodimentsof the present invention are not limited to the identification of fetal

heart beats and determination of fetal heart rates by a programmed signal

processingsystem4. For example,a general purpose digital computer, such as

15 a generic version of PC 5, may be programmed to provide the same features.

After a fetal heart rate is determined, the result may be displayed on display 16

or stored in fixed storage 14 in the PC, for example, when appropriate

commands are entered via the keyboard 15.

FIG.2 is a block diagramdetailing the preprocessingelectronic unit 3, the

20 signal processingsystem4, and the PC 5. The preprocessing electronic unit 3

comprises amplifiers 6 which receive the plurality of sensor signals 2. The

sensor signals 2 outputted by the passive fetal heart rate monitoring sensor 1

is weak due to the inherentweakness of a fetal heart beat. One purpose of the

amplifiers 6 is to boost the initial sensor signals 2. Another purpose of the

25 amplifiers6 is to amplifythe sensor signals 2 to match the range of subsequent

components, e.g. an analog-to-digital converter. The amplifiers 6 respectively

are connected to corresponding filters 7. The sensor signals 2 are bandpass

filtered by bandpass filters 7 in order to eliminate competing signals that are

larger than the fetal heart beat signal. By focusing the bandwidth on the

30 relevant range of a fetal heart beat, the analog-to-digital converter and other
LAR 15129-1 -8- PATENT APPLICATION

subsequent processing will not be saturated with non-essential signals. The

bandpassfilters 7 are connectedto an analog time-division multiplexor (M/X) 8.

The multiplexorrotatesamong the filtered signals and time-division multiplexes

the filtered signals to allow simultaneous sampling of all sensor signals. The

5 time slice for each signal is very small (e.g., .5 ms) to accommodate the

monitoring of dynamic, real-time changes in fetal heart rates. The multiplexor

8 is connected to an analog-to-digital converter 9 in the signal processing

system 4. In one embodiment, the signal processing system is a digital signal

processing (DSP) card housed inside PC 5. The signal processing system 4

10 has a central processing unit (CPU) 10 and memory 11 in addition to signal

processing electronics (e.g., analog-to-digital converter 9). However, the

embodiments of the present invention are not limited to having a signal

processing system 4 as a DSP card residing inside a PC. For example, the

preprocessingelectronicunit3 may be combinedwith the features and functions

15 of the signal processing system4 in a separatesignal processing device outside

the PC. As a further example, a general purpose digital computer, such as a

generic version of PC 5, may be programmed to provide the same features as

the signal processing system 4. The preprocessed signal outputted from the

preprocessingelectronics unit 3 is received by the signal processing system 4.

20 The signal processing system 4 first converts the analog preprocessed signal

into digital words, at a specified sampling rate, e.g., 500 Hz or waveform

samples per second. Processing performed by the DSP CPU 10 receives the

digital words via data stored in the DSP memory 11 and demultiplexes the

digitized signal so that further processing may address time slices of digitized

25 acoustic data for each of the multiple sensor signals 17 (as shown in FIG. 3).

FIG.3 is a flow chart showing further processing by the DSP CPU 10 of

the signal processing system 4. The digitized signal is further filtered (e.g., at

a 500 samples per second rate) with a linear phase FIR digital filter 18 ("FIR

LPF"). The use of digital filters operating on the digitized signal reduces the

30 requirements on the analog bandpass filters 7 used in the hardware of the
LAR 15129-1 -9- PATENT APPLICATION

preprocessing electronics unit 3 and allows for greater flexibility since these

filters are very easily changed or tailored to any reasonable desired response.

The linear phase propertyof the FIRdigital filter also insures minimal waveform

distortion. The filtered digital signal may then be further decimated to a lower

5 sampling rate (e.g., 250 samples per second). Such decimation to a lower

sampling rate may be desirable since it reduces the computations required in

subsequent steps of processing.

After the digitalfiltering of the multiplesensor signal data 17 by filters 18,

the signalsare further analyzedto identify fetal heart beats and a resultant fetal

10 heart rate is determined from the identified fetal heart beats. Multiple parallel

nonlinearfilters are used to identify fetal heart beats from the digitized acoustic

sensor data. FIG. 3 shows two such nonlinear filters, i.e. a Teager Energy

Operator19 and a neural network20. These nonlinearfilters operate in parallel

and provide redundant identification of fetal heart beats for each of the plurality

15 of sensor signals 17. Such parallel redundant processing improves the

accuracy of identifying the fetal heart beats. While these benefits are created

by the use of multipleparallel nonlinearfilters, the embodiments of the invention

are not limited to multiple parallel nonlinear filters. For example, singular

nonlinearfiltering, by the Teager Energy Operator 19 or by the neural network

20 20, of the plurality of sensor signals 17 is also possible to yield improved

identification of fetal heart beats.

The particular embodiment in FIG. 3 shows a first nonlinear filtering by

a Teager Energy Operator 19. The Teager Energy Operator 19 highlights

regions of high energy (i.e., the heart beats). This nonlinear filter is known in

25 the industry and is described in Kaiser, James F., "On a Simple Algorithm to

Calculate the 'Energy' of a Signal," ICASSP-90, 1990, vol. 1, pp. 381-84

(hereafter "Kaiser"). The Teager Energy Operator is computed using

y(n) = x2(n-1) - x(n)*x(n-2).
LAR 15129-1 -10- PATENT APPLICATION

where x(n) is the acoustic signal and y(n) is the Teager energy signal. This

nonlinear operation results in a large output in regions which contain high

amplitude and rapidly varying signals. The Teager signal is further digitally

smoothed using a "box-car" average filter, well known in the industry. FIG. 4

5 depicts an example of an acoustic signal 28 and the Teager Energy Operator

filtered response signal 29. The acoustic signal 28 shown in FIG. 4 is the

amplitudeof the acoustic signal as time passes. The Teager Energy Operator

filtered responsesignal29 is the amplitude of the Teager Energy signal as time

passes.

10 The other nonlinear filter shown in FIG. 3 is a neural network. In one

embodiment of the present invention, the neural network 20 is a three-layer

feed-forwardneuralnetwork as shownschematicallyin FIG. 5. The three layers

of the networkinclude an input layer 30, a hidden layer 31, and an output layer

32. For this nonlinear filter, the data is first normalized by a root mean square

15 (RMS)value to insure that the subsequent processing receives data of uniform

amplitude. In otherwords,the RMS value of each sampled signal may be used

to control or adjust the input range of the analog-to- digital convertor to match

the measuredsignal level. Such a normalization method helps to maintain the

best possible signal-to-noise ratio for the signal data. The data is then

20 processed by the neural network 20 which nonlinearly "filters" the data using

twenty digital words 33 (corresponding to the current input point plus nineteen

previous input points 36) to compute an output point 36 for the current time

frame, using an equation:

y(n) = f {x(n), x(n-1),. ......x(n-19)}, for each index in the frame of data,

25 where

y(n) is the current neural network output data 36

f represents the neural network

x(n) is the current input data point 36

In addition, the neural network 20 comprises thirty hidden layer units 34
LAR 15129-1 -11 - PATENT APPLICATION

and one output layer unit 35. FIG. 6 is a schematic depiction of an arbitrary unit

("Unit X") corresponding to a hidden layer unit 34 or to the output layer unit 35.

With unit X corresponding to a particular hidden layer unit 34, the plurality of

inputs 37 (Xl-X,) correspond to the plurality of digital words 33 from the input

5 layer 30. Each input 37 is weighted by a weight 38 (W1-Wn), summed with a

bias term 39, and processed by a sigmoidal nonlinearity 40. An output value 41

is outputted to the output layer unit 35. With unit X corresponding to the output

layer unit 35, each of the plurality of inputs 37 (X_-Xn) corresponds to the

outputted value from each of the hidden layer units 34. An output value 41,

10 corresponding to the neural network output value 36, is outputted by the output

layer unit 35. Thus, the neural network 20 is fully interconnected with a weight

from each input layer unit 30 to each hidden layer unit 34, and from each hidden

layer unit 34 to the output layer unit 35, and a bias term 39 for each unit. Bipolar

sigmoidal nonlinearities 40 are used for all hidden layer units 34 and the output

15 layer unit 35. In equation form, the neural network can be described as follows:

The input to each hidden layer unit 34 is of the form:

t9

gi" ]_ Wijx(j)*b i for 0 _ i _ 29

j-0

where gi = input to ith hidden unit

20

Wij -- weight from input j to hidden unit i

x(j) = jth input

bi = bias for hidden unit i

The output of each hidden layer unit 34 is of the form:

1 - e -g_

hi

1 .e -gl

where g_ = input to ith hidden unit

h i = output of ith hidden unit
LAR 15129-1 -12- PATENT APPLICATION

The input to the final output layer unit 35 is of the form:

29

0.]Cwhj hi. b

j.o

5

where 0

whj

hj

b

input to output unit

weight from jr, hidden unit to output unit 35

= output of jt, hidden unit

= bias for output unit 35

The final output, i. e., from the output layer unit 35, is given by:

1

p1.e -o

where p = output of output unit 35

0 = input of output unit

The neural network 20 was trained with a back propagation method using

10 hand labeled training data, wherein regions of fetal heart beats and betweenbeat intervals were previously identified. Such data were obtained from clinical

tests. Backpropagation is a well known method in the industry used to train a

neural network, as described in Lippmann, R. P., "An Introduction to Computing

with Neural Nets," IEEE ASSP magazine, Apr. 1987, pp. 4-22 (hereafter

15 "Lippman"). The values for the weights and bias terms were determined from

this training procedure. After the completion of the training, the neural network

20 operates using the learned weights and biases. In essence, the neural

network 20 acts as a nonlinear filter trained to indicate the presence of a fetal

heart beat. FIG. 7 is a graph showing an example of a fetal heart tone as

20 represented by an acoustic signal 42 and the resulting, nonlinearly filtered timedomain output waveform 43 from the neural network 20. The acoustic signal 42

shown in FIG. 7 is the amplitude of the acoustic signal as time passes. The

neural network output waveform 43 is the amplitude of the neural network output
LAR 15129-1 -13- PATENT APPLICATION

as time passes.

As regards the neural network 20, although a layered, interconnected,

feed forward neural network is caused to learn by back propagation, the

embodiments of the present invention are not limited to this choice of a neural

5 networknor to this choice of training. For example, an interlinked feed forward

neural network also having feed back connections can be used. Such a feed

forward and feed back neural network can be trained by a method proposed by

J. J. Hopfield or another by G. E. Hinton et al. These learning methods are

described in Hopfield, J. J., "Neuralnetwork and physical systems with emergent

10 collective computational abilities," Proc. Natl. Acad. Sci. U.S.A., 1982, vol. 79;

Hopfield, J. K., "Neurons with graded response have collective computational

properties like those of two-state neurons," Proc. Natl. Acad. Sci. U.S.A., 1984,

vol. 81; Ackley, D. H., Hinton, G. E., and Senjnowski, T. J., "A Learning

Algorithmfor BoltzmannMachines,"Cognitive Sci., 1985, 9. Other variations of

15 the neural network are also possible. For example, neural networks having a

different number of hidden units, output units, or layers may also be trained to

identify fetal heart beats.

The resulting signal from each of the multiple parallel nonlinear filtering

is then analyzed with autocorrelation processing 21 (as shown in FIG. 3) as a

20 first step in determining a fetal heart rate estimate 23. The determination of a

fetal heart rate estimate for each of the plurality of results from the multiple

parallel nonlinear filtering of each of the plurality of sensor signals 17 will

increasethe chances of discoveringthe most accurate fetal heart rate estimate.

Peak evaluation processing 22, as the next step as shown in FIG. 3,

25 identifies peaks, selects the highest peaks, estimates a fetal heart rate based

on the selectedpeaks in the autocorrelation,and determines a figure of merit 24

for the fetal heart rate estimate 23. A peak is defined as a point in the

autocorrelation which is larger than immediately adjacent points. A peak is

selected for fetal heart rate estimation if the peak is larger than a threshold

30 number of adjacent points. A heart rate estimate is found by identifying and
LAR 15129-1 -14- PATENT APPLICATION

using the selected peaks in the autocorrelation over a search range appropriate

for fetal heart rates (typically 80 to 200 beats per minute). The heart rate

estimates 23 are determined in this manner for each of the multiple sensor

channels. The heart rate estimates 23 are then ranked using a figure of merit

5 24, which is derived from continuity constraints and a measure of periodicity in

the waveform, as described below. The highest ranking (i.e., having the highest

figure of merit) estimate is then selected in the next step by the merit comparison

processing 25 as the heart rate 26 for the current frame of data. The figure of

merit 27 is the figure of merit value corresponding to the selected fetal heart rate

10 26. This procedure is repeated for each frame of data. The time between

frames for the entire processing is very small (e.g., .5 sec.) in order to properly

monitor dynamic real-time changes in fetal heart rates.

The figure of merit 24 is determined as follows. First, one buffer is used

to store a small number (e.g., 5) of the previously selected values of fetal heart

15 rate 26. Another buffer is used to store a corresponding small number (e.g., 5)

of the previously corresponding values of figure of merit. A small number of

previously calculated fetal heart rates and corresponding merit values is used

to ensure the best dynamic response to fetal heart rate changes. A calculation

is made using the following variables and equations:

20 For ith rate estimate,

Merit = Peak(i) * ( 1.0 Avg. Merit /

Rate _ )

. (Rate(i) - Avg. Rate)

25

where

Merit = Figure of merit for the ith fetal heart rate estimate.

Peak(i) = The normalized autocorrelation for the ith fetal heart rate

estimate.
LAR 15129-1

m

Avg. Merit. 1 _ Merit(i)

m÷l 14)

-15- PATENT APPLICATION

5

where m is a small number (e.g., 4, which will provide for five

samples when i ranges from 0 to 4), and

Merit(i) = previous merit computations 27

Rate Dev. = The factor which controls the contribution of previous

estimates (typically = 30).

Rate(i) = fetal heart rate estimate 23

Avg. Rate

m

Merit(i). Rate(i)

i-0

m

Merit(i)

i-O

where m is a small number (e.g., 4, which will provide for five

10 samples when i ranges from 0 to 4);

Merit(i) = previous merit computations 27; and

Rate(i) = previous selected rates 26.

This Avg. Rate is the "weighted" average of a small number m of

previously selected fetal heart rates 26 having the highest ranking

15 merits 27 when these rates 26 were selected by the merit

comparison operation 25.

The heart rate estimate 23 with the highest figure of merit 24 is selected

as the current and most accurate fetal heart rate 26 during merit comparison 25.

If the figure of merit 27 for this calculation is above an empirically determined

20 threshold (typically about 35% of the maximum value), the resultant calculation

is assumed to be valid. When and if the output heart rate 26 is determined to

be valid and is to be displayed, it is displayed as a "correct" reading. If the merit

27 is lower than the empirical threshold value (i.e., an invalid rate), the rate 26
5

10

15

LAR 15129-1 -16- PATENT APPLICATION

is still displayed, but is color coded in the computer display to indicate a possible

error or "dropout."

Alternatively, the outputted fetal heart rates 26 may be saved in fixed disk

storage on the PC or transmitted to a communications channel, such as a

modem. An example of an output tracing of fetal heart rates from a device for

an embodiment according to the present invention, obtained from a patient with

a gestation age of 39 weeks, is shown in FIG. 8. The y-axis is measured in

beats per minute versus time as measured along the X-axis. Each gradation

along the X-axis equals approximately 20 seconds, or three gradations for about

a minute.

Numerous modifications and adaptations of the present invention will be

apparent to those skilled in the art. Thus, the following claims and their

equivalents are intended to cover all such modifications and adaptations which

fall within the true spirit and scope of the present invention.

What is claimed is:
//

LAR 15129-1 _ PATENT APPLICATION

PASSIVE FETAL HEART RATE MONITORING APPARATUS AND METHOD

WITH ENHANCED FETAL HEART BEAT DISCRIMINATION

Abstract of the Disclosure

An apparatus for acquiring signals emitted by a fetus, identifying fetal

5 heart beats and determining a fetal heart rate. Multiple sensor signals are

outputted by a passive fetal heart rate monitoring sensor. Multiple parallel

nonlinear filters filter these multiple sensor signals to identify fetal heart beats

in the signal data. A processor determines a fetal heart rate based on these

identified fetal heart beats. The processor includes the use of a figure of merit

10 weighting of heart rate estimates based on the identified heart beats from each

filter for each signal. The fetal heart rate thus determined is outputted to a

display, storage, or communications channel. A method for enhanced fetal heart

beat discrimination includes acquiring signals from a fetus, identifying fetal heart

beats from the signals by multiple parallel nonlinear filtering, and determining

15 a fetal heart rate based on the identified fetal heart beats. A figure of merit

operation in this method provides for weighting a plurality of fetal heart rate

estimates based on the identified fetal heart beats and selecting the highest

ranking fetal heart rate estimate.
____oASSIVE FETA_

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PREPROCESSING

ELECTRONIC UNIT

SIGNALSYSTEM PROCESSING -_

PERSONAL

COMPUTER

FIG. I
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19

TEAGER

ENERGY

OPERATOR

NEURAL

NETWORK

21

AUTOCORRELATION

AUTOCORRELATION

FIG. 3

22

PEAK

EVALUATION

PEAK

EVALUATION

MERIT-1

RATE-1

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MERIT-2

RATE-2 l' l

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MERIT

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26
28[

ACOUSTIC L

AMPLITUDE

29

\

TEAGER

ENERGY

SIGNAL

AMPLITUDE

FETAL HEART TONE - ACOUSTIC SIGNAL

TIME

FETAL HEART TONE - TEAGER ENERGYOPERATOR OUTPUT

U

TIME

FIG. 4
WORD 1

35

WORD 2

33

WORD 20

33

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FIG. 5

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UNITX

FIG. 6
ACOUSTIC

AMPLITUDE

NEURAL

NETWORK

OUTPUT

AMPLITUDE

FETAL HEART TONE - ACOUSTIC SIGNAL

42\

' ' ' I TIME

FETAL HEART TONE - NEURAL NETWORK OUTPUT

TIME

FIG. 7
HEART

BEATS

PER

MINUTE

HEART

BEATS

PER

MINUTE

24O

210

180

150

120

90

60

30

240

210

180

150

120

90

60

30

FETAL HEART RATE Il 3 GRADATIONS= MINUTE

TIME

TIME

FIG.8