Method for determining blood pressure utilizing a neural network

A method and device for indirect, quantitative estimation of blood pressure attributes and similar variable physiological parameters utilizing indirect techniques. The method of practice includes (i) generating a sequence of signals which are quantitative dependent upon the variable parameter, (ii) transmitting and processing the signals within a computer system and associated neural network capable of generating a single output signal for the combined input signals, (iii) directly determining an actual value for the parameter concurrent with the indirect generation of signals of the previous steps, (iv) applying weighting factors within the neural network at interconnecting nodes to force the output signal of the neural network to match the true value of the parameter as determined invasively, (v) recording the input signals, weighting factors and true value as training data within memory of the computer, and (vi) repeating the previous steps to develop sufficient training data to enable the neural network to accurately estimate parameter value upon future receipt of on-line input signals. Procedures are also described for preclassification of signals and artifact rejection. Following training of the neural network, further direct measurement is unnecessary and the system is ready for diagnostic application and noninvasive estimation of parameter values.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
Present invention relates to a device and method for calculation of 
variable physiological parameters such as blood pressure utilizing a 
neural network. More particularly, the present invention relates to a 
method for training a neural network to recognize and calculate blood 
pressure values based on an oscillometric waveform generated by an 
external blood pressure cuff. 
2. Prior Art 
Blood pressure is one of the primary physiological measurements used to 
access the condition of a patient's cardiovascular system. During acute 
care, as is provided in the operating room and intensive care unit, blood 
pressure measurements are routinely used to monitor and manage the 
condition of patients. 
Because noninvasive methods of estimating blood pressure are generally a 
traumatic and present little risk to the patient, they are often used 
instead of the invasive method, which requires that a catheter or needle 
be inserted into an artery. A major disadvantage associated with 
noninvasive methods has been their lack of close agreement with actual 
blood pressure as would be measured by an invasive method. In addition, 
lack of close agreement also exists between different noninvasive methods, 
further adding to the uncertainty of any particular reading derived by 
noninvasive methods. 
Oscillometry has become the most common method used for automatic, 
noninvasive blood pressure monitoring. One advantage of the oscillometric 
method over other noninvasive methods is its ability to estimate not only 
diastolic and systolic pressures, but also mean pressure. Conventional 
oscillometric blood pressure monitors use an inflatable air filled 
occlusive cuff that is placed around a limb, usually the upper arm. Small 
oscillations in the cuff pressure, which correspond to intraarterial 
pulses in the artery underlying the cuff, are recorded while the cuff 
pressure is increased from a pressure below diastolic to a cuff pressure, 
reach a maximum amplitude at cuff pressure, between diastolic and systolic 
pressure, and then decrease in amplitude with further increases in cuff 
pressure above systolic. As is characteristic of oscillometric waveforms, 
the cuff pressure oscillations initially increase in amplitude with 
increasing cuff pressure. 
Although oscillometry has become the most prominent noninvasive blood 
pressure monitoring method, there is still a general lack of theoretical 
understanding regarding the origin of the oscillometric waveform and the 
relationship between that waveform and the respective attributes of blood 
pressure identified as diastolic, mean and systolic pressure. This lack of 
theoretical understanding has led to the development of empirical 
algorithms which serve to estimate diastolic, mean, and systolic blood 
pressure. 
For example, it is generally believed that the minimum cuff pressure at 
which the cuff pressure oscillations reach their maximum provides a 
reasonable estimate of mean blood pressure. The maximum amplitude 
criteria, however, apparently underestimates true mean blood pressure and 
is dependent for accuracy on such factors as the magnitude of the 
intraarterial pulse pressure. The maximum amplitude criteria does not 
therefore provide ideal measurements in all conditions. 
Fixed ratio amplitude criteria have been used in commercial blood pressure 
monitors to estimate systolic and diastolic pressures. Such fixed ratio 
amplitude criteria involve identifying the cuff pressure at which the 
oscillations have decreased from the maximum by a fixed amount, such as 
fifty or eighty percent. Here again, fixed ratio amplitude criteria are 
dependent for accuracy on such factors as the magnitude of the 
intraarterial pulse pressure. The accuracy of such fixed ratio methods is 
dependent on empirical observations and has yet to be explained in theory. 
In short, blood pressure monitoring as represented by amplitude 
oscillometry processed by conventional algorithms merely generalizes 
relationships which are based on minimum cuff pressure versus maximum 
oscillation for mean blood pressure and some empirical percentage under 
the fixed ratio amplitude criteria for estimating systolic and diastolic 
pressures. Comparison of conventional noninvasive measurement techniques 
with invasive measurements of blood pressure has shown that noninvasive 
estimates may vary as much as forty percent from true value. 
At least three factors play a dominant role in limiting the performance of 
conventional oscillometric algorithms. First, oscillometric waveforms are 
susceptible to artifacts and noise from a variety of sources. Typical 
algorithms are not capable of dealing with artifacts, common noise and 
other variations which may be reflected in the oscillometric waveform. In 
fact, most conventional oscillometric algorithms are based either directly 
or indirectly on the assumption that blood pressure remains constant 
during the recording period, which may last as much as ten to thirty 
seconds. It is apparent that the processing of artifacts and noise 
interferring with quality signals degenerates the accuracy of any 
estimation of blood pressure. When this is combined with the occurrence of 
cyclic changes in blood pressure during recording of the oscillometric 
waveform, it becomes clear that the accuracy and usefulness of 
oscillometric estimates are at best an indicator of probable blood 
pressure rather than an accurate determination. 
A second factor that limits the performance of conventional oscillometric 
algorithms is the over simplistic practice of empirically interpreting the 
relationship between the oscillometric waveform and arterial blood 
pressure attributes to be a uniform percentage. This is an over 
simplification because this relationship in fact varies with changes in 
arterial blood pressure and pulse pressure. To generalize that values for 
diastolic pressures are best estimated by identifying the cuff pressure at 
which the oscillations have decreased by eighty percent from the maximum 
is at best a general guide. Although there have been a number of attempts 
to develop more sophisticated algorithms which deal with pulse 
transformations through pressure-volume curves, these algorithms depend on 
identification of subtle features within the oscillometric waveform which 
are very sensitive to artifacts and noise and are difficult to implement 
in a robust and practical form. 
A third factor that limits the performance of conventional oscillometric 
algorithms is the nonlinear relationship between the oscillometric 
waveform and intraarterial blood pressure. The relationship is further 
complicated by also being non-stationary with respect to time and 
subjects. For example, the shape of the oscillometric waveform is strongly 
dependent on the state of the cardiovascular system and the interaction of 
intraarterial pressure pulses with the nonlinear mechanical properties of 
the arteries. The fact that these relationships change with activity, age 
and disease further complicates use of an algorithm which tends to 
generalize relationships between the oscillometric waveform and blood 
pressure attributes. 
Most common pressure monitoring systems include blood pressure estimations 
based on the shape of the oscillometric pulses rather than the amplitude. 
This approach has likewise been subject to the problems set forth in the 
preceding paragraphs. Specifically, such methods require high fidelity 
recording of the oscillometric signal and tend to be very sensitive to 
signal noise and artifacts. 
What is needed therefore is a fresh approach to the evaluation of the 
oscillometric signals and waveform for blood pressure measurement which 
overcomes the (i) regular occurrence of noise and artifacts which 
regularly occur during blood pressure monitoring, (ii) nonlinear 
relationship and (iii) lack of theoretical understanding. 
OBJECTS AND SUMMARY OF THE INVENTION 
It is an object of the present invention to provide a method and device for 
collecting and processing noninvasive oscillometric blood pressure data 
and processing such data for a more accurate estimation of intraarterial 
diastolic, mean and systolic blood pressures. 
It is a further object of the present invention to provide a device and 
method for estimating a variable physiological parameter such as blood 
pressure without the need for making a direct, invasive measurement, while 
providing accuracy which more closely approaches the direct measurement. 
It is a further object of the present invention to provide a device and 
method for estimating physiological parameters such as blood pressure 
utilizing a neural network as a system for processing input data and 
ultimately computing the values of physiological parameters. 
It is yet another object of the present invention to provide a device and 
method for generating a body of training data for use as part of a neural 
network to assist in estimation and determination of physiological 
parameter values derived from generated data having a nonlinear 
relationship with the parameter values. 
A still further object of the present invention is to provide a method and 
device for determining the values of physiological parameters despite 
occurrence of artifacts, noise and other corrupting signal influences. 
Another object of the present invention is to provide a device and method 
for determining blood pressure and other similar physiological parameters 
without a need for reliance upon generalizing assumptions which undermine 
system accuracy. 
These and other objects are realized in a method for indirect, quantitative 
estimation of a variable physiological parameter based on indirect as 
opposed to direct measurement of parameter value. The method comprises the 
steps of (i) identifying the physiological parameter to be quantitatively 
monitored and estimated; (ii) generating a sequence of signals which are 
quantitatively dependent upon the variable physiological parameter, but 
which are not suitable for providing a direct quantitative readout based 
on direct measurement of the parameter; (iii) transmitting the signals to 
and processing such signals within a computer system including input nodes 
of a neural network supported by the computer system, which neural network 
is capable of generating at least one output signal for the combined input 
signals as an accurate estimate of the estimated value for the 
physiological parameter; (iv) determining an actual, true value for the 
physiological parameter concurrent with the previous steps; (v) making 
within the neural network which modify the value of the output signal to 
match the true value of the physiological parameter determined in the 
previous step; recording as training data within memory of the computer 
system the input signals, and true values associated with the sequence of 
signals generated under step ii; and sequentially repeating the previous 
steps sufficient to train the neural network to recognize relevant input 
signals and estimate the value of the physiological parameter based on 
association of on-line input signals with one or more trained neural 
networks. A device is also disclosed for implementing the above inventive 
method, as well as specific adaptations with respect to systolic, mean and 
diastolic blood pressure. Also disclosed is a neural network for 
pre-classifying waveforms and for disregarding noise and artifact signals. 
Other objects and features of the present invention will be apparent to 
those skilled in the art based on the following detailed description, 
taking in combination with the accompanying drawings.

DESCRIPTION OF PREFERRED EMBODIMENT 
FIG. 1 illustrates a block diagram of a blood pressure monitor and 
processing system constructed in accordance with principles of the present 
invention. This includes a blood pressure cuff 20 which is adapted with 
suitable hardware necessary to pressurize the cuff in accordance with 
conventional practice. This cuff 20 may be a banded configuration 
typically applied to limbs or extremities of a patient or may be a 
superficial temporal artery blood pressure monitor as applied to the 
patients head. For purposes of the disclosure set forth initially herein, 
a temporal artery blood pressure pad is disclosed. However, it would be 
apparent to those skilled in the art that a conventional occlusive blood 
pressure cuff could likewise be substituted in application of the present 
inventive principles. 
The cuff 20 interfaces at a pulse side of the patient to generate 
noninvasive, oscillometric blood pressure data which is processed in a 
computer 21. The computer is connected through a parallel interface card 
22 to a satellite box 23 which contains the hardware necessary to inflate 
and measure the pressure in a transducer bladder of the cuff 20. 
A software controlled direct current Romega 80 air pump is used to inflate 
the transducer bladder. Although most oscillometric blood pressure 
oscillometric monitors employ a deflation ramp, a software controlled 
direct current Romega 80 air pump is used to inflate the transducer 
bladder. Although most oscillometric blood pressure monitors employ a 
deflation ramp to record the oscillometric waveform, the superficial 
temporal artery monitor employs an inflation ramp. To damp out pump 
oscillations and provide a smooth pressure ramp for inflating the 
transducer bladder, the output of the air pump is fed through two rigid 
volume chambers of 10 ml each, separated by a pneumatic resistance. A 
manually adjustable needle valve 24 is used to control the flow rate from 
the damping chambers to the transducer bladder, thus allowing for variable 
pressure ramp rates. The transducer bladder of the superficial temporal 
artery blood pressure pad is connected to the output of the needle valve 
24 through an air line 25 approximately 1.5 meters long with an inside 
diameter of approximately 1.5 millimeters. A secondary line is connected 
from the output of the needle valve 24 to a pressure transducer and a 
software controlled solenoid valve. The pressure transducer is used to 
record the inflation ramp and oscillometric waveform from the superficial 
temporal artery blood pressure pad transducer bladder. The solenoid valve 
serves as a dump valve to release the pressure in the transducer bladder 
following each blood pressure determination. With respect to FIG. 1, it 
will be apparent to those skilled in the art that the satellite box 23 
houses the pump and pneumatic circuit, pressure transducer, analog 
amplifier/filter, 12 bit analog-to-digital converter and parallel port. 
This combined hardware services the inflation needs of the cuff 20, and 
provides initial filtering and processing of signals. Digital signals are 
then transmitted over connecting line 27 to the computer interface card 
22. Software within the computer 21 controls subsequent data collection, 
processing and data display. 
The oscillometric waveform comprises transducer bladder pressure 
oscillations plotted as a function of transducer bladder pressure and is 
constructed by software using the derivative of the transducer bladder 
pressure signal. The derivative of the transducer bladder pressure is the 
sum of the changes in pressure due to the inflation ramp and of the 
changes in pressure due to volume oscillations transmitted from the 
underlying artery. The goal in reconstructing the oscillometric waveform 
is to isolate the component of the derivative signal corresponding to the 
volume oscillations from the derivative signal and then integrate the 
resulting signal beat-to-beat to recover the original pressure 
oscillations. 
FIGS. 2 and 3 contain an oscillometric recording from the superficial 
temporal artery. FIG. 2 is the derivative of the transducer bladder 
pressure as the transducer bladder was inflated from approximately 0 to 90 
torr. The positive offset or bias in the derivative signal corresponds to 
the slope of the inflation ramp. FIG. 3 is of the oscillometric waveform 
constructed from the derivative of the transducer bladder pressure signal. 
Further refinement of the oscillometric waveform is carried out by noting 
that a single oscillation or beat should start and return to nearly the 
same diastolic pressure level. Consequently, the sum of the derivative 
signal over a beat should be zero. Any non-zero sum is assumed to be part 
of the ramp signal and is subtracted from the derivative signal over the 
period of the beat. The adjusted derivative signal is integrated over the 
period of the beat to obtain a more accurate reconstruction of the 
oscillation or beat. The reconstructed oscillometric waveform is shown in 
FIG. 4 in its conventional form. 
Whereas prior art techniques for estimating diastolic, mean and systolic 
intraarterial blood pressure involved empirical identification of certain 
points on the waveform of FIG. 4, the present invention looks at the 
waveform in its totality. For example, in FIG. 4 the referenced blood 
pressure attributes could be estimated using prior art techniques by 
applying software implementations of an 80 percent diastolic algorithm, a 
maximum amplitude mean algorithm and a 50 percent systolic algorithm. 
These points represent empirical, and somewhat arbitrary, points on the 
graph which have been shown to produce close approximations of the blood 
pressure attributes. As has already been pointed out, however, these are 
generalizations which may not accurately represent changes in blood 
pressure, because of differences in age and physiology within the patient. 
It has now been discovered that processing the signal components and 
waveform through a neural network not only enhances accuracy of blood 
pressure determination, but can also be an effective method for reducing 
or eliminating the effects of noise and artifacts which have previously 
been processed along with the periodic signals making up the waveform. 
Neural networks are based on models of the nervous system and employ 
adaptive signal processing techniques. Once a neural network is trained, 
it provides a means of computing an appropriate output signal when 
presented with a given input signal. Training data are used to modify the 
neural network weights as applied to various nodes making up the network 
until the network is optimized in a stochastic sense to provide the 
appropriate output for a given input. 
The present invention introduces an application of neural networks for 
identification or estimation of physiological parameters which can be 
estimated by indirect measurements made with respect to the patients body. 
Such indirect measurements are feasible where a physiological event can be 
monitored based on generation of a sequence of signals which are 
quantitatively dependent upon the variable physiological parameter. As has 
been indicated, blood pressure is a prime example of such an indirect 
estimation, based on monitoring signals generated in oscillometry. 
The neural network can be trained to compute estimates of intraarterial 
blood pressure from noninvasive oscillometric signals. Because the network 
processes the entire oscillometric signal rather than trying to identify a 
single occurrence, such as the point of maximum oscillation, the network 
is inherently more robust (less sensitive to noise and artifact) than 
standard oscillometric algorithms. Furthermore, unlike standard algorithms 
whose accuracy varies with factors such as blood pressure and pulse 
pressure, the network can provide nonlinear processing of the input signal 
and thus be relatively consistent over a wide range of pressures. 
A neural network may be specified in terms of its architecture. This 
includes the number of nodes and the interconnection relationships between 
them, node characteristics such as input/output functions, and learning or 
training rules which define the method by which the node interconnection 
are adapted during training. The power of a neural network arises in part 
from the use of nonlinear functions to process node inputs and the use of 
parallel distributed processing wherein a given piece of information is 
not restricted to a single node but may appear as input to many nodes 
which may operate on the network inputs concurrently. 
A three layer, feed forward neural network was designed to process 
oscillometric amplitude waveforms with the present invention as shown in 
FIG. 5. The three layer system includes an input layer 30, one hidden 
layer 31 and an output layer 32. Although reference is generally made 
throughout this disclosure to a single output layer it is to be understood 
that multiple output layers could be implemented where separate and 
distinct output values are to be developed from the same set of output 
data. For example, the present invention might embody three outputs 
representing the respective diastolic, mean and systolic blood pressure 
values for a single set of inputs from a patient. Accordingly, reference 
to single output is not to be limited in a restrictive sense, but rather 
shall be interpreted as meaning at least one output for the network. 
Each input node represented by P(n), P(n-1) etc. as set forth in FIG. 5 is 
connected through a weighted link 33 to every hidden layer node 34. 
Similarly, each hidden layer node 34 is in turn connected through a 
weighted link 35 to the single output layer node 36. 
With respect to its application for estimation of blood pressure 
attributes, forty input nodes 37 were provided for the neural network and 
adapted to receive forty incremental signal samples of a normalized 
oscillometric amplitude waveform (cuff pressure oscillation amplitude 
versus cuff pressure). These samples were taken over evenly spaced 
increments of 4 torr over a cuff pressure ranging from 20 to 176 torr. 
These forty input samples were stored in computer memory and then 
concurrently transmitted to the forty input nodes of the input layer 30. 
In other words, the first sample was transmitted to P(n), the second 
sample to P(n-1), etc. This total transmitted set of sample signals is 
concurrently received at the input layer 30 and represents a sample view 
of the waveform which represents a single blood pressure determination 
procedure. 
This input signal is processed through one or more hidden layers 31 with 
application of weighting factors at interconnecting nodes to establish an 
internode relation between the input signals 38 and a desired output 
signal 39. This processing includes adjustments made within the neural 
network which at the weighting links 33 and 35 which modify the value of 
the output signal 39 to match the particular value of the blood pressure 
or other physiological parameter which is to be determined. This is 
accomplished in a training sequence wherein the output value 39 is a known 
value which is generated by virtue of the adjustments made to the input 
signals 38 as the signals are processed through the network. The process 
of training the neural network to accomplish this result involves 
initially establishing appropriate, fixed weighting factors within the 
weighting links 33 and 35 such that upon occurrence of a similar set of 
input signals 38 in a future monitoring application of the neural network, 
an appropriate output signal 39 will be computed by reason of the applied 
waiting factors within the links 33 and 35 which have been saved in 
memory. This procedure will be outlined in greater detail hereafter. 
Nodes are commonly characterized by an internal threshold or offset and the 
type of nonlinearity through which the node inputs are passed. The 
internal thresholds and offsets of the hidden layer nodes 31 are 
determined adaptively utilizing a well known back propagation algorithm, 
which is a generalization of the Widrow-Hoff Delta Rule. The backward 
error propagation algorithm is a gradient descent algorithm designed to 
minimize the mean square error between the desired output and the actual 
output of the network. In order to generate an error term, the data set 
used to train the network must contain not only network inputs, but also 
the desired output which is specified and used in the supervised training 
of the network. 
Application of the back propagation algorithm consists of the following 
three steps: 
1. Input data is processed forward through the network to generate an 
output. An error term is computed using the difference between the desired 
output and the actual network output. 
2. The error term is propagated back through the network to modify the 
internode connection weights and node thresholds so as to minimize the 
mean square error. 
3. Steps 1 and 2 are repeated with new input data in an iterative adaptive 
process. Commonly, adaptation is halted and the connection weights are 
saved after the network has reached some specified level of convergence, 
such as when the error has dropped to 10 percent of the desired output. 
The following is a representative listing of the equations and steps used 
in implementing the back propagation algorithm for oscillometric waveform 
processing. 
1. Initialize internode connection weights to small random values. 
2. Present training data to the network (i.e. operate network in feed 
forward mode using input data). 
3. Adapt internode connection weights using the network output and the 
desired output as follows. 
Weight Update Equation: 
EQU w.sub.ij (n+1)=w.sub.ij (n)+new e.sub.j (n)x.sub.i (n) 
w.sub.ij (n) is the weight from hidden node i to an output node or from an 
input node i to a hidden node j at time n. 
x.sub.i (n) is either the output of node i or is an input. 
new is a gain term, convergence coefficient. 
e.sub.j (n) is the error term computed for node j. 
Error Term if j is a Hidden Layer Node: 
EQU e.sub.j (n)=[d.sub.k (n)-y.sub.j (n)][1-y.sub.j (n)][y.sub.j (n)]w.sub.kj 
(n) 
Error Term if k is an Output Layer Node 
EQU e.sub.k (n)=[d.sub.k (n)-y.sub.k (n)] 
x.sub.j (n) is the output of hidden layer node j k is over the output 
nodes. 
4. Repeat starting at step 2. 
For each hidden layer node 34, the weight of the sum of the inputs 33, 33a, 
33b, 33c and 33d (dot product of the node input and weight factor) were 
passed through a sigmoid nonlinearity of the form shown in FIG. 6. Because 
a continuous output reading of arterial blood pressure in torr was desired 
from the neural network, the sigmoid nonlinearity was not applied to the 
output layer. Instead, the output layer node functions as a simple summer 
of the weighted outputs of the hidden layer nodes. The sigmoid 
nonlinearity used in the hidden layer nodes is of the following 
mathematical form: 
##EQU1## 
The input of the forty samples to the network input node layer 30 is 
represented by a normalized oscillometric amplitude waveform as shown in 
FIG. 7. This amplitude waveform is part of the inventive process wherein 
the sequence of signals generated by the cuff and pressure transducer are 
sampled at 4 torr intervals to supply a set of forty-plus sample signals 
representing the total range of pressures covered by the blood pressure 
determination procedure. With respect to each sample signal, a feature is 
identified which constitutes the maximum amplitude of that sample signal. 
This same process could be applied to other monitoring procedures which 
involve oscillatory signals having an amplitude feature. This feature is 
then utilized to develop the referenced waveform in FIG. 7 wherein the 
respective sample amplitude values form a locus of points representing the 
amplitude of cuff pressure over the blood pressure determination. This 
waveform is the image or pattern corresponding to an actual blood pressure 
value as it is represented at the forty input nodes of the neural network. 
The neural network is trained to compute the actual blood pressure value 
from this waveform and present it at the output layer upon receipt of a 
similar set of input signals. 
Based on the foregoing description and FIG. 1, the general device for 
implementing the training and use of a neural network with respect to 
variable physiological parameters for measurement can be summarized as 
follows. The device includes a sensing means 20 for indirectly detecting 
changes in a physiological parameter which is to be quantitatively 
monitored and estimated. Selection of the sensing means will depend on the 
nature of the parameter and will generally be a conventional monitoring 
which is already being used to attempt such estimations. 
As has been indicated, a blood pressure cuff which currently generates 
oscillometric signals forms the sensing means for the blood pressure 
application. Pulse oximetry is another monitoring procedure which may be 
adapted for processing with a neural network. In this case, an estimation 
of blood oxygen saturation is transmitted through or reflected from body 
tissues. A third area of application is generally referred to as dilution 
cardiac output. This monitoring procedure estimates cardiac output or 
blood flow by processing the time dependent concentration or temperature 
signal produced by injection of a dye or thermal solution into the 
vascular system. Obviously, in the latter two cases different sensing 
devices will be utilized, and an appropriate signal, which is 
quantitatively dependent upon the variable physiological parameter, but 
which is not suitable for providing direct quantitative readout based on 
direct measurement of that parameter. 
The sensing means and an associated signal generating means 23 together 
cooperate to produce the required set of signals to be applied at input 
nodes in the neural network. The neural network includes a supporting 
computer system 21 coupled to the generating means and operates to control 
data collection, processing and display. It will be apparent to those 
skilled in the art that reference to the computer system would include 
other data processing devices such as hardware analog circuits or 
integrated circuits which could be specifically designed to implement a 
neural network without a separate computer system. 
The neural network has been described in one preferred embodiment, and can 
generally be described as including (i) a series of input nodes for 
receiving signals from the generating means, (ii) a series of hidden nodes 
coupled individually to each of their respective input nodes, and (iii) at 
least one output node which is coupled to each of the respective hidden 
nodes for supplying a desired output value. The neural network includes 
means for generating the single output signal from the signals received at 
the input nodes wherein the output signal provides the trained estimated 
value of the physiological parameter. 
The computer system also operates as a data storage means for storing 
training data generated within the neural network with respect to 
relationships between the input signals and desired values for the 
physiological parameter to be designated during such training and supplied 
as an output. The computer system may also provide a readout means for 
indicating the estimated value of physiological parameter based on the 
output signals from the neural network. 
When used as part of a training system, the present invention also includes 
direct detection means which are coupled to the computer system and 
adapted with means for determining an actual, true value for the 
physiological parameter. This direct detection means is applied concurrent 
with receipt of sample signals received from the generating means. Memory 
storage means is provided in the computer system for storing parameter 
true values in association with corresponding input signals fed to the 
neural network. 
Although the number of input nodes will vary depending on the number of 
input signals to be processed, or at least two input nodes are required to 
establish a minimum statistical image. Likewise, hidden nodes will differ 
in number and in levels. A single hidden layer will generally be adequate 
and will usually include at least two nodes making up the single hidden 
layer between the input nodes and the single output node. Where additional 
boundary conditions within the neural network are required, multiple 
hidden layers may be applied. 
The computer also provides a selection control means for sampling periodic 
signals generated from the generating means. As indicated in the previous 
example, forty sample signals were taken over the blood pressure 
monitoring procedure pressure range and were placed in memory for 
subsequent transmission on a concurrent basis to the input nodes of the 
neural network. Generally, at least one feature will be identified within 
these sample signals, which feature can be processed through the neural 
network as a feature signal having a dependent relationship with respect 
to the physiological parameter. 
Where the inventive system is used in a training mode, a portion of 
computer memory or other memory means is set aside to store training data 
including weighting factors and parameter values which can be used to 
generate a value for the physiological parameter including mean 
intraarterial blood pressure, systolic intraarterial blood pressure and 
diastolic intraarterial blood pressure. When the present invention is 
applied to a monitoring application, the invention need not include either 
connection with the invasive detection means required for determining the 
true value for the physiological parameter or recorded training data. 
Instead, the device need only include the trained neural network and 
interconnection weights necessary to determine neural network output 
estimates of the desired physiological parameter from the on-line data 
input. In this monitoring configuration, the device may include three 
separate neural networks respectively configured and trained to determine 
the named blood pressure attributes, or may be a single neural network 
with three outputs configured to generate the same result. Further detail 
with respect to technical implementation of the neural network in 
accordance with the teachings of this invention is unnecessary in view of 
current knowledge of those skilled in the art with respect to neural 
network systems generally. 
FIG. 8 represents the general procedural steps associated with the present 
invention in its broader terms. The first step involves identifying the 
physiological parameter to be quantitatively monitored and estimated. Item 
41 represents a blood pressure cuff and the associated physiological 
parameters of diastolic, mean and systolic blood pressure. The cuff 41 
also represents the associated hardware to support operation of the cuff 
in its conventional manner. The next step involves generating a sequence 
of signals 42 which are quantitatively dependent upon the variable 
physiological parameter, but which are not suitable for providing a direct 
quantitative readout based on direct measurement of the parameter. The 
third step comprises transmitting these signals to and processing such 
signals within a computer system 44, including input nodes 45 of a neural 
network 46 supported by the computer system 44 is similar to that 
described previously and provides capability of generating a single output 
signal 47 for the combined input signals 43. This output signal 47 
provides the estimated value of the physiological parameter corresponding 
to the referenced input signals 43. 
In the training mode, the device represented by FIG. 8 includes steps for 
determining the actual, true value of the physiological parameter 
concurrent with the generation of signals as represented by item 42. This 
procedure is represented by an intravenous device 49 which is invasively 
positioned within the patient to directly readout actual blood pressure 
values for transmission along line 50 and to the computer system 44. This 
true value for the parameter is processed by the computer system and 
stored as training data 51. This value is transmitted via line 52 as the 
desired output value 47. Adjustments are then made within the neural 
network 46 which modify the value of the signal transmitted from the 
output nodes 53 to a value which equals the desired output value 47 
transmitted from training data 51. Typically this is accomplished by 
applying weighting factors at interconnecting nodes within the neural 
network between the input nodes and hidden layer of nodes 54 and between 
the hidden layer of nodes 54 and the output node 53. 
During the training phase, the input signals 42 are presented to the neural 
network, along with the adjusted weighting factors required to modify the 
input signal through the hidden layer to reach an output value equal to 
the output value generated by the invasive measurement 49. These data are 
collectively recorded as training data 51 and used to train the neural 
network. After training, the neural network can be used for on-line 
monitoring of a patient's blood pressure in the absence of the invasive 
measurement. 
This series of steps is repeated a sufficient number of times to train the 
neural network to recognize relevant input signals and estimate the value 
of the desired physiological parameter based on association of on-line 
input signals at some future time. 
As indicated previously, this method is particularly applicable with 
respect to oscillatory signals which generate a waveform corresponding to 
a single monitoring procedure. In the present case, this diagnostic test 
procedure is represented by the sequence of a blood pressure cuff and 
implementing conventional oscillometry to generate the desired sequence of 
signals. To be useful in such a system, it is desirable that the 
oscillatory signals be changing in amplitude or frequency in a dependent 
relationship with respect to the physiological parameter. This enables the 
neural network to learn the various relationships through actual training 
wherein the true value of the parameter is taught to the neural network in 
association with the input signals received. 
An additional value of utilizing a neural network is its ability to analyze 
and interpolate from several sample signals and generate an accurate 
estimation of the parameter value without having the need to process the 
full sequence of signals originally generated 42. In accordance with this 
method, the computer system or other form of selection control means 
selects a plurality of sample signals from the sequence of signals 42 
which may be received directly through line 55. The computer system 
identifies at least one feature, such as signal amplitude, within the 
sample signals which can be processed through the neural network as a 
feature signal. The normalized oscillometric amplitude waveform 
illustrated in FIG. 7 demonstrates how 40 signals selected at 4 torr 
increments can generate a typical waveform without the need for processing 
all signals as is represented in the waveform illustrated in FIGS. 3 and 
4. The subject inventors have successfully developed accurate results in a 
blood pressure monitoring system by selecting only 3 sample signals and by 
processing those sample signals through the neural network in accordance 
with the teachings of this invention. Obviously, at least two sample 
signals will be required to generate a meaningful waveform, depending upon 
the training capacity of the neural network with respect to the desired 
parameter. Accordingly, the neural network system provides a much improved 
efficiency in that the processing and association steps of analysis can be 
accomplished with several signals, rather than the full range of 
designated signals. Generally the selected number of sample signals 
determining the minimum number of input nodes required with respect to the 
neural network. 
In the preferred embodiment, the specific method of practice involves 
developing a waveform for each single parameter estimation procedure 
wherein the predetermined number of sample signals corresponds 
approximately to the number of input nodes in the network. The sequential 
signals are stored in memory and are collectively and concurrently 
transmitted to the input nodes of the neural network as a representative 
waveform. 
Application of the inventive steps represented in FIG. 8 to a specific 
training session for generating blood pressure training data is 
accomplished in the following specific format. Specifically, a sequence of 
oscillometric signals are generated from a pressure sensing means. This 
means, represented by the blood pressure cuff 41 of FIG. 8, is coupled 
externally to a patients anatomy in a sensing proximity to a heart pulsing 
sensing location. The computer system 44 is adapted to identify a set of 
sample signals at defined increments wherein the primary feature of the 
oscillometric signal constitutes pulse amplitude. The process continues by 
measuring and recording pressure values within the pressure sensing means, 
along with the corresponding pulse amplitude signals described in the 
previous step. As is represented by item 49, invasive blood pressure 
measurements are made concurrent with the generation of the oscillometric 
signals representing heart pulse. This true value is transmitted to the 
computer system for recording as part of the training data 51. At the same 
time, the sample feature signals representing pulse amplitude are 
transmitted to the input nodes 45 for processing through the neural 
network. Appropriate adjustments are made with application of weighting 
factors to force the output signal of the network to more closely match 
the desired output value determined invasively. Repetition of this 
training continues until the neural network is capable of recognizing sets 
of input signals and determining accurate estimates of blood pressure 
values. 
When applied with respect to oscillometric techniques, the typical range of 
measuring and recording pressure values extends over defined increments 
from approximately 20 to 200 torr. The subject inventors have found an 
appropriate increment to be 4 torr, representing approximately 40 to 45 
sample signals. 
The present system was tested with respect to five dogs. A total of 425 
recordings of oscillometric amplitude waveforms, along with simultaneous 
invasive measurements of arterial diastolic, mean and systolic blood 
pressures were obtained (approximately 85 recordings per dog). Three 
separate neural networks were utilized, one each for estimating diastolic, 
mean or systolic blood pressure. These systems were trained utilizing the 
back propagation algorithm as previously discussed. 
The networks were trained and tested using a train-on-4/test-on-1 
procedure. Following training of the network on data from 4 of the dogs, 
adaptation of the internal hidden layer thresholds and network internode 
connection weights was halted and data from the fifth dog was processed 
forward through the network to obtain estimates of either arterial 
diastolic, mean or systolic blood pressure. The protocol was repeated five 
times such that data from each dog was tested on a network trained using 
data from the other four dogs. 
Since no clear rules exist for determining the optimum number of hidden 
nodes, the training and testing process was repeated using 3, 7, 15, 31, 
and 63 hidden layer nodes in each network. The convergence coefficient 
which appears in the equations of FIG. 6 was set to equal 0.001. 
respectively. Training data was passed adaptively through each network a 
total of 1,500 times. Following each adaptive pass, the training data (340 
oscillometric readings from 4 dogs, 85 readings per dog) was processed 
forward through the neural network to evaluate the level of convergence. 
The test data from the fifth dog (85 oscillometric recordings) was then 
processed forward through the network to evaluate neural network 
performance at different levels of convergence. The convergence 
coefficient, and total number of passes through the training data were 
selected to yield reasonable rates of convergence, final convergence 
levels, and steady state oscillations. 
The level of convergence was quantified in terms of the mean error, the 
standard deviation of the errors and the mean square error. The error was 
computed as the difference between the desired (invasive arterial blood 
pressure measurement) and the actual network output (noninvasive 
estimate). The mean square error is the variable which the back 
propagation algorithm is attempting to minimize and thus serves as an 
appropriate measure of the level of convergence. 
Network performance on the test data was also evaluated in terms of the 
mean difference and standard deviation of the differences between arterial 
measurements and the noninvasive neural network estimates of arterial 
blood pressure. Conventional oscillometric algorithms were also used to 
obtain estimates of arterial blood pressure. Mean blood pressure was 
estimated as the cuff pressure at which the oscillations first reached 
their maximum. Systolic blood pressure was estimated as the cuff pressure 
at which the oscillations had decreased to 50 percent of their maximum 
amplitude. Diastolic blood pressure was estimated as the cuff pressure at 
which the oscillations had increased to 80 percent of their maximum 
amplitude. 
Use of the present system in the application phase, as contrasted with the 
training phase, is represented in FIG. 9. The methods of processing input 
signals through the neural network are substantially the same as those 
used during the training phase; however, no concurrent measurements of an 
invasive nature are made nor are modifications of the network made, since 
the purpose of the application of weighting factors is to estimate such 
parameters without the discomforts and trauma of more difficult or 
invasive techniques. 
In summary, this method of quantitative estimation of the variable 
physiological parameter is practiced by identifying the parameter to be 
estimated, generating a sequence of on-line signals which are 
quantitatively dependent upon the variable parameter, and transmitting 
those signals to the input nodes of the neural network. At this stage, the 
neural network has been appropriately trained to identify the closest 
parameter value based on weighting factors which have been modified as 
part of the training process. Actual output values are obtained by 
processing the on-line signals within the neural network with generation 
of an output value corresponding to such input signals. In view of the 
fact that those skilled in the art will readily understand the methodology 
of this application phase as compared to the training phase earlier 
described, duplication of that earlier description is deemed unnecessary. 
Indeed, the foregoing description is incorporated herein by reference as 
it relates to processing on-line input data through the neural network and 
generating an estimated output value for the blood pressure or other 
parameter based on comparison and interpolation by the neural network as 
is enabled through its training data. 
FIGS. 10a, 10b and 10c disclose examples of learning curves obtained by 
processing the training data forward through the network after each 
adaptive pass. This data corresponds to a network having three hidden 
layer nodes. FIGS. 11a, 11b and 11c contain corresponding performance 
curves obtained by processing the test data forward through the same 
neural network having 63 hidden layer nodes. These figures represent 
training data which was presented within the network a total of 5,000 
times, as opposed to the earlier mentioned 1,500 presentations. 
As shown in FIG. 10a, 10b and 10c, the mean error learning curves change 
rapidly at first, sometimes changing sign and then begin the slow noisy 
ascent or descent toward 0. Both a standard deviation of the errors and 
the mean square error are characterized by noisy decaying exponentials. 
The rate of convergence was found to decrease with an increase in the 
number of hidden layer nodes. However, the mean error, the standard 
deviation of the errors, the mean square error, and the steady state 
oscillation also decreased with an increase in the number of hidden layer 
nodes. 
As shown in FIGS. 11a, 11b, and 11c, performance curves are generally of 
the same form as the corresponding learning curves. (FIGS. 10a, 10b and 
10c). However, although the mean difference approaches zero with increased 
training, the standard deviation of the differences may actually increase. 
Thus, increasing the level of convergence or reducing the mean square 
error during training does not necessarily insure better performance on 
test data. A possible explanation for such an effect is that the network 
becomes specific to the training data and loses its ability to generalize. 
In summary, although the convergence patterns vary depending on the number 
of hidden layer nodes and the number of passes through the training set, 
the different neural network architectures all successfully converged. In 
general, increasing the number of hidden layer nodes was associated with a 
higher level of convergence on the training data and improved performance 
on the test data in the form of decaying exponentials. Increasing the 
number of hidden layer nodes was also associated with smaller steady state 
oscillations in both the learning and performance curves. Increasing the 
number of passes through the training data was associated with a higher 
level of convergence; however, this did not always translate into an 
increase in performance on test data, particularly after prolonged 
training. The price for improved performance is an increase in the number 
of interconnections and thus the amount of time required to train or 
process data through the network. 
FIGS. 12a, 12b, 12c and 13a, 13b, 13c show, respectively, the differences 
and the standard deviation of the differences between the invasive 
measurements and the noninvasive neural network estimates of arterial 
blood pressure plotted against the number of hidden layer nodes. The 
network's performance was evaluated at different levels of training by 
processing test data forward through the networks after 50, 250 and 1,500 
adaptive passes through the training data. Both the mean difference and 
the standard difference FIGS. 12a, 12b and 12c and the standard deviation 
FIGS. 13a, 13b and 13c of the differences tended to decrease as the number 
of hidden layers was increased. The improvement in performance was in the 
form of a noisy decaying exponential. As previously noted, increased 
training did not necessarily ensure better performance. The best 
performance (minimum standard deviation of the differences) in estimating 
diastolic, mean or systolic blood pressure was achieved using networks 
with 63 hidden layer nodes (the maximum number of hidden layer nodes 
tested). For diastolic estimates the best performance was achieved after 
422 training passes; for mean estimates, after 18 training passes; and for 
systolic after 548 training passes. 
In comparison with conventional algorithms used for determining blood 
pressure parameters the neural network oscillometric blood pressure 
estimator performed as well or better based on data obtained from the 5 
dogs. The neural network approach for estimating blood pressure and other 
physiological parameters provides a potentially powerful alternative to 
the conventional algorithmic processing of oscillometric amplitude 
waveforms. One advantage arises because the neural network does not 
require detailed knowledge of the relationship between the input 
(oscillometric waveform) and the output (arterial blood pressure 
attributes). Instead, the neural network develops through supervised 
training, an internal set of rules used to transform or map inputs into 
the appropriate outputs. An additional, major advantage of neural networks 
is that they are very simple to implement. Once the neural network is 
appropriately trained, it can readily respond with generation of 
appropriate parameter estimations. In addition, the neural network system 
has a natural robustness in that it is not as sensitive to artifact and 
noise as conventional algorithmic processes. Unlike conventional 
algorithms, which usually depend on the identification of a single event 
(e.g., the lowest cuff pressure at which maximum oscillations occur), the 
entire oscillometric waveform can be processed by the neural network to 
obtain an estimate of the desired blood pressure attributes. 
The favorable results of the present invention as compared to conventional 
algorithm techniques are generally summarized in FIG. 14. This figure 
discloses a table of values comparing invasive measurements with 
conventional algorithm techniques, as well as the neural network system of 
the present invention. The first row in the table contains the mean 
differences, plus or minus the standard deviation between invasive 
measurements and noninvasive conventional algorithm and neural network 
estimates of blood pressure. These statistics were computed using the data 
generated with respect to the test animals previously described. The 
second row contains the means and standard deviations for each dog, 
demonstrating improvement with respect to intrasubject variations. The 
attendant graph provides a more dramatic example of how the accuracy of 
the conventional algorithm decreases with increasing blood pressure while 
the accuracy of the neural network remains relatively constant. 
Accordingly, it is apparent that the distributed and nonlinear processing 
capabilities of a neural network system as disclosed herein offers 
significant advantages and potential for maintaining the accuracy of blood 
pressure estimates over a wide range of physiological conditions. 
The neural network may also be utilized as part of a pre-classification 
system for identifying the nature of certain input signals. For example, 
when a set of input signals arrives at the input nodes of a neural 
network, certain patterns may be readily detectable which are unique to a 
child as opposed to an adult patient. Such a pre-classification 
application is useful for identifying various patient conditions which 
fall in broad categories generally identified as patient induced 
conditions. Age, body size, disease conditions and other conditions 
falling within other unique classifications can be detected by certain 
patterns which are reproduced at the input nodes of the neural network. 
Once detected, the neural network can then reduce the processing of such 
information by restricting the selective training data to that applicable 
for the selected classification. 
As an example, a neural network may be trained to recognize blood pressure 
attributes as they relate to pediatric patients. By using a 
pre-classifier, the neural network can immediately recognize that the 
input signals have a pediatric pattern, thereby limiting comparison of 
input data with training data specifically developed for pediatric 
patients. Similar applications of the neural network can be utilized in 
this pre-classification rule for equipment induced conditions that may 
represent a malfunction. Reference to training data which enables the 
neural network to recognize certain common malfunction conditions for 
monitoring equipment can lead to more timely alert of attending medical 
personnel for equipment correction or maintenance. 
In a similar manner, the neural network of the present invention can be 
trained to recognize noise and artifact input received at the input nodes 
of the neural network. This technique was specifically applied with 
respect to measurement of test animals as previously described. These 
specific procedures involved a initial determination of the oscillometric 
waveform quality based on human observation of the waveform graph. This 
was accomplished by observing the waveform and noting the occurrence of 
noise or artifact signal and then assigning a "quality" factor such as 
"excellent", "good" or "artifact". Training samples from a total of 245 
waveforms were selected and processed through a neural network having 60 
input nodes, 15 intermediate hidden nodes and a single output. This 
network was trained using a supervised stochastic method to calculate a 
"quality" number at the output node based on this goodness indicator. In 
actual experiments, the numbers selected were 500 representing an 
excellent waveform, 0 representing a good waveform and -500 indicating an 
artifact. At the end of the training the network was consistently able to 
calculate lower numbers for the artifact waveform and higher numbers for 
the good and excellent waveforms. It was thus able to distinguish the 
worst quality waveforms from the better ones, enabling the network to 
thereby distinguish and reject artifact and noise signals. This held true 
for both the training data set and the nontraining data set of signals. It 
was also noticed that when this procedure was tested on the nontraining 
data set, the network properly classified a few waveforms which had been 
misclassified during the initial human classification process. 
FIG. 15 presents a graph which illustrates normal oscillometric pulse 
amplitude versus cuff pressure. The quality or clean signal is represented 
by the small square box point indicators, whereas the random noise or 
artifact signal is superimposed and indicated with + signs. Processing of 
these respective signals confirms the ability of the neural network to 
distinguish and reject inappropriate signals and record and process 
quality signals. 
This latter function of pre-classification is represented in FIG. 16. Here 
again, the selected parameter is a blood pressure value generated by use 
of an oscillometric system represented by a cuff 60. A sequence of signals 
are generated 61 and transmitted to a pre-classification neural network 
62. In this case, the pre-classification network is trained to recognize 
signals which are corrupted by extraneous noise and to classify these as 
artifacts 63 which will be rejected or stored as training data for future 
recognition. All other signals are considered to be of good or excellent 
quality and are transmitted to the neural network as previously described 
66 for processing and estimation of blood pressure as an output signal 67. 
Development of training data is accomplished in a procedure similar to that 
outlined with respect to training of the neural network to recognize 
certain blood pressure parameter values. Typically the signal input is 
classified as corrupted or artifact and weighting factors are applied at 
interconnecting nodes within the neural network 62 to establish an 
internode relationship between the corrupted signal received at the input 
nodes and a desired output signal which is defined to be an artifact 63. 
These relationships and values are saved in computer memory for a future 
association with respect to signal input which is not predefined with 
respect to quality. 
During training processing for the blood pressure neural network the 
pre-classification neural network 62 may be useful for identifying and 
discarding noise and artifact signals such that these are not used as 
training data. This operates to enhance the accuracy of the training data 
stored as much as all artifact and noise signals are pre-classified and 
rejected. In this case, training data which is being coordinated with the 
output signal of the blood pressure neural network 66 is of pure value, 
overcoming a major cause of error in conventional algorithm processing 
which comprehends both quality and artifact signals on an equal basis. 
It will be apparent to those skilled in the art that the various examples 
presented in this disclosure are representative and are not to be 
considered as limiting with respect to the following claims.