Passive fetal heart rate monitoring apparatus and method with enhanced fetal heart beat discrimination

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

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
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. 
2. Description of the Related Art 
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 changing background noise is described in U.S. 
Pat. No. 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 adaptive interference cancelling to isolate 
fetal heart beats and frequency domain analyses of multi-channel signals 
to extract useful information pertaining to fetal well-being, including 
fetal heart rate and absence of fetal movement. Another system, described 
in U.S. Pat. No. 5,209,237 to Rosenthal, also employs a similar noise 
cancellation technique, but uses multiple reference 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 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 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 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 
nonlinearity of the background noise. The use of a two-layer, feed forward 
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, 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 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 detection. 
SUMMARY OF THE INVENTION 
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 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 passive fetal heart monitoring sensor producing a plurality of acoustic 
signals, multiple nonlinear 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 signal processing device, which 
receives, amplifies, filters, multiplexes, and digitizes the plurality of 
sensor signals, and which outputs a plurality of processed signals; 
multiple parallel nonlinear filters, which receive the plurality of 
processed signals, filter each of the plurality of processed signals to 
identify fetal heart beats, and output a plurality of functional values 
indicative of the presence of 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 method for identifying fetal heart beats and determining fetal heart 
rates 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 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 processed signals, and a step for determining and 
outputting the fetal heart rate based on the plurality of functional 
values indicative of the presence of fetal heart beats.

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 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, 
non-invasive, passive, fetal heart rate monitoring sensor 1 is placed on 
the abdomen of an expectant mother. Multiple sensor elements inside sensor 
1 receive the 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. Pat. No. 5,140,992 
to Zucherwar et al. entitled "PASSIVE FETAL 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 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. 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 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 PC 5 is well known in the industry and, as shown in FIG. 2, is 
comprised 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 output to various devices, such 
as the keyboard 15, the display 16, or a communications channel (not 
shown). The signal processing system 4 is not limited to being physically 
located inside a PC. For example, the 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 embodiments of the present 
invention are not limited to the identification of fetal heart beats and 
determination of fetal heart rates by a programmed signal processing 
system 4. For example, a general purpose digital computer, such as 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 diagram detailing the preprocessing electronic unit 3, 
the signal processing system 4, 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 inherent weakness of a fetal heart 
beat. One purpose of the amplifiers 6 is to boost the initial sensor 
signals 2. Another purpose of the amplifiers 6 is to amplify the 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 
relevant range of a fetal heart beat, the analog-to-digital converter and 
other subsequent processing will not be saturated with non-essential 
signals. The bandpass filters 7 are connected to an analog time-division 
multiplexor (M/X) 8. The multiplexor rotates among the filtered signals 
and time-division multiplexes the filtered signals to allow simultaneous 
sampling of all sensor signals. The time slice for each signal is very 
small (e.g., 0.5 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 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 
preprocessing electronic unit 3 may be combined with the features and 
functions of the signal processing system 4 in a separate signal 
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 preprocessing electronics unit 3 is 
received by the signal processing system 4. 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 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 requirements on the analog bandpass filters 7 
used in the hardware of the 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 property of 
the FIR digital filter also insures minimal waveform distortion. The 
filtered digital signal may then be further decimated to a lower 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 digital filtering of the multiple sensor signal data 17 by 
filters 18, the signals are further analyzed to identify fetal heart beats 
and a resultant fetal heart rate is determined from the identified fetal 
heart beats. Multiple parallel nonlinear filters 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 Operator 19 and a neural 
network 20. These nonlinear filters operate in parallel and provide 
redundant identification of fetal heart beats for each of the plurality 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 multiple parallel nonlinear filters, the embodiments 
of the invention are not limited to multiple parallel nonlinear filters. 
For example, singular nonlinear filtering, by the Teager Energy Operator 
19 or by the neural network 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 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 
EQU y(n)=x.sup.2 (n-1)-x(n)*x(n-2). 
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 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 amplitude of the acoustic signal as time passes. 
The Teager Energy Operator filtered response signal 29 is the amplitude of 
the Teager Energy signal as time passes. 
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-forward neural network as shown schematically in FIG. 5. 
The three layers of the network include 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 (RMS) value to insure that the subsequent 
processing receives data of uniform amplitude. In other words, 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 measured signal 
level. Such a normalization method helps to maintain the best possible 
signal-to-noise ratio for the signal data. The data is then 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: 
EQU y(n)=f{x(n), x(n-1), . . . x(n-19)}, 
for each index in the frame of data, 
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 
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 (X.sub.1 -X.sub.n) correspond to 
the plurality of digital words 33 from the input layer 30. Each input 37 
is weighted by a weight 38 (W.sub.1 -W.sub.n), 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.sub.1 
-X.sub.n) corresponds to the outputted value from each of the hidden layer 
units 34. An output value 41, 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 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: 
##EQU1## 
for 0.ltoreq.i.ltoreq.29 where 
g.sub.1 =input to i.sup.th hidden unit 
W.sub.ij =weight from input j to hidden unit i 
x(j)=j.sup.th input 
b.sub.i =bias for hidden unit i 
The output of each hidden layer unit 34 is of the form: 
##EQU2## 
where g.sub.i =input to i.sup.th hidden unit 
h.sub.i =output of i.sup.th hidden unit 
The input to the final output layer unit 35 is of the form: 
##EQU3## 
where O=input to output unit 
wh.sub.j =weight from j.sup.th hidden unit to output unit 35 
h.sub.i =output of jth hidden unit 
b=bias for output unit 35 
The final output, i.e., from the output layer unit 35, is given by: 
##EQU4## 
where p=output of output unit 35 
O=input of output unit 
The neural network 20 was trained with a back propagation method using hand 
labeled training data, wherein regions of fetal heart beats and 
between-beat 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, April 
1987, pp. 4-22 (hereafter "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 represented by an acoustic 
signal 42 and the resulting, nonlinearly filtered time-domain 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 
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 network nor 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., "Neural network and 
physical systems with emergent 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 Algorithm for 
Boltzmann Machines," Cognitive Sci., 1985, 9. Other variations of 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 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 increase the chances of discovering 
the most accurate fetal heart rate estimate. 
Peak evaluation processing 22, as the next step as shown in FIG. 3, 
identifies peaks, selects the highest peaks, estimates a fetal heart rate 
based on the selected peaks 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 number of adjacent points. A heart rate 
estimate is found by identifying and 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 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 26. This procedure is repeated for each 
frame of data. The time between frames for the entire processing is very 
small (e.g., 0.5 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 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: 
For i.sup.th rate estimate, 
##EQU5## 
where Merit=Figure of merit for the i.sup.th fetal heart rate estimate. 
Peak(i)=The normalized autocorrelation for the i.sup.th fetal heart rate 
estimate. 
##EQU6## 
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 
##EQU7## 
where m is a small number (e.g., 4, which will provide for five 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 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 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 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.