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| United States Patent | 5632272 |
| Link to this page | http://www.wikipatents.com/5632272.html |
| Inventor(s) | Diab; Mohamed K. (Laguna Niguel, CA);
Kiani-Azarbayjany; Esmaiel (Laguna Niguel, CA);
Elfadel; Ibrahim M. (Laguna Niguel, CA);
McCarthy; Rex J. (Mission Viejo, CA);
Weber; Walter M. (Los Angeles, CA);
Smith; Robert A. (Corona, CA) |
| Abstract | The present invention involves method and apparatus for analyzing two
measured signals that are modeled as containing primary and secondary
portions. Coefficients relate the two signals according to a model defined
in accordance with the present invention. In one embodiment, the present
invention involves utilizing a transformation which evaluates a plurality
of possible signal coefficients in order to find appropriate coefficients.
Alternatively, the present invention involves using statistical functions
or Fourier transform and windowing techniques to determine the
coefficients relating to two measured signals. Use of this invention is
described in particular detail with respect to blood oximetry
measurements. |
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Title Information  |
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Drawing from US Patent 5632272 |
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Signal processing apparatus |
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| Publication Date |
May 27, 1997 |
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| Filing Date |
October 7, 1994 |
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| Parent Case |
Reference to Prior Related Application
This is a continuation-in-part application of U.S. patent application Ser.
No. 08/132,812 filed Oct. 6, 1993, and entitled "Signal Processing
Apparatus" now U.S. Pat. No. 5,490,505 and a continuation-in-part
application of U.S. patent application Ser. No. 08/249,690 filed May 26,
1994 entitled "Signal Processing Apparatus and Method", now U. S. Pat. No.
5,482,036 which is a continuation of U.S. patent application Ser. No.
07/666,060 filed Mar. 7, 1991, now abandoned. |
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Title Information  |
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References  |
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| | Reference | Relevancy | Comments | Reference | Relevancy | Comments | 3647299
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Public's "Guesstimation" of Royalty Value
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Market Review  |
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Technical Review  |
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Claims  |
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What is claimed is:
1. In a signal processor for processing at least two measured signals
S.sub.1 and S.sub.2 each containing a primary signal portion s and a
secondary signal portion n, said signals S.sub.1 and S.sub.2 being in
accordance with the following relationship:
S.sub.1 =s.sub.1 +n.sub.1
S.sub.2 =s.sub.2 +n.sub.2
where s.sub.1 and s.sub.2, and n.sub.1 and n.sub.2 are related by:
s.sub.1 =r.sub.a s.sub.2 and n.sub.1 =r.sub.v n.sub.2
and where r.sub.a and r.sub.v are coefficients,
a method comprising the steps of:
determining a value for the coefficient r.sub.a which minimizes correlation
between s.sub.1 and n.sub.1 ;
calculating the blood oxygen saturation from said value of r.sub.a ; and
displaying the blood oxygen saturation on a display.
2. In a signal processor for processing at least first and second measured
signals S.sub.1 and S.sub.2 each containing a primary signal portion s and
a secondary signal portion n, said signals S.sub.1 and S.sub.2 being in
accordance with the following relationship:
S.sub.1 =s.sub.1 +n.sub.1
S.sub.2 =s.sub.2 +n.sub.2
where s.sub.1 and s.sub.2, and n.sub.1 and n.sub.2 are related by:
s.sub.1 =r.sub.a s.sub.2 and n.sub.1 =r.sub.v n.sub.2
and where r.sub.a and r.sub.v are coefficients,
a method comprising the steps of:
determining a value for the coefficient r.sub.a which minimizes correlation
between s.sub.1 and n.sub.1 ; and
processing at least one of the first and second signals using the
determined value for r.sub.a to significantly reduce n from at least one
of the first or second measured signal to form a clean signal.
3. The method of claim 2, further comprising the step of displaying the
resulting clean signal on a display.
4. The method of claim 2, wherein said first and second signals are
physiological signals, said method further comprising the step of
processing said clean signal to determine a physiological parameter from
said first and second measured signals.
5. The method of claim 4, wherein said physiological parameter is arterial
oxygen saturation.
6. The method of claim 4, wherein said physiological parameter is an ECG
signal.
7. The method of claim 2, wherein the primary signal portion of said
measured signals is indicative of a heart plethysmograph, said method
further comprising the step of calculating the pulse rate.
8. A physiological monitor comprising:
a first input configured to receive a first measured signal S.sub.1 having
a primary portion, s.sub.1, and a secondary portion n.sub.1 ;
a second input configured to received a second measured signal S.sub.2
having a primary portion s.sub.2 and a secondary portion n.sub.2, said
first and said second measured signals S.sub.1 and S.sub.2 being in
accordance with the following relationship:
S.sub.1 =s.sub.1 +n.sub.1
S.sub.2 =s.sub.2 +n.sub.2
where s.sub.1 and s.sub.2, and n.sub.1 and n.sub.2 are related by:
s.sub.1 =r.sub.a s.sub.2 and n.sub.1 =r.sub.v n.sub.2
and where r.sub.a and r.sub.v are coefficients;
a transform module responsive to said first and said second measured
signals and responsive to a plurality of possible values for r.sub.a to
provide at least one power curve as an output;
an extremum calculation module responsive to said at least one power curve
to select a value for r.sub.a which minimizes the correlation between s
and n, and to calculate from said value for r.sub.a a corresponding
saturation value as an output; and
a display module responsive to the saturation value output of said extremum
calculation module to display said saturation value.
9. In a signal processor for processing at least firs and second measured
signals, each containing a primary signal portion and a secondary signal
portion, said first and second signals substantially adhering to a
predefined signal model, a method comprising the steps of:
sampling said first and second signals over a period to obtain a first
series of data points representing said first signal over said period and
a second series of data points representing said second signal over said
period;
transforming said first series of data points into a first transformed
series of points having at least a frequency component and a magnitude
component and transforming said second series of data points into a second
transformed series of points having at least a frequency component and a
magnitude component;
comparing said first and second transformed series of points to obtain a
third series of comparison values having a magnitude component and at
least a frequency component;
selecting at least one of said comparison values that has a magnitude
within a selected threshold; and
from said selected at least one comparison value, determining a resulting
value consistent with the predefined signal model.
10. The method of claim 9, wherein said step of comparing comprises
determining a series of ratios on a point-by point basis of the first
transformed series of points to said second transformed series of points,
and wherein said step of selecting at least one of said comparison values
comprises the step of selecting the lower of the ratios.
11. The method of claim 10, wherein said step of determining a resulting
value comprises calculating a blood oxygen saturation from the selected
lower of the ratios.
12. The method of claim 9, wherein said resulting value is blood oxygen
saturation.
13. The method of claim 9, wherein said resulting value is pulse rate.
14. In a signal processor for processing at least first and second measured
signals, each containing a primary signal portion and a secondary signal
portion, said first and second signals substantially adhering to a signal
model for blood constituent saturation, a method comprising the steps of:
sampling said first and second signals over a period to obtain a first
series of data points representing said first signal over said period and
a second series of data points representing said second signal over said
period;
transforming said first and second series of data points from time domain
to frequency domain to obtain a first transformed series of points and a
second transformed series of points, said first and second transformed
series of points having a magnitude component and at least a frequency
component;
determining a series of ratios of magnitudes with respect to frequency of
ones of said first transformed series of points to ones of said second
transformed series of points;
selecting at least one of the ratios from said series of ratios that has a
magnitude within a selected threshold; and
from said selected at least one of the ratios, determining a resulting
value consistent with the signal model.
15. The method of claim 14, wherein said ratios correspond to blood oxygen
saturation, said step of selecting at least one of said ratios comprises
selecting at least one of the ratios corresponding to the higher values of
blood oxygen saturation.
16. The method of claim 15, wherein said step of determining a resulting
value comprises calculating the blood oxygen saturation from the selected
at least one of the ratios.
17. The method of claim 16, further comprising the steps of:
combining with a window function at least one of said first transformed
series of points or said second transformed series of points with said
resulting value; and
performing a spectrum analysis on the combination to obtain the pulse rate.
18. The method of claim 16, wherein said resulting value is blood oxygen
saturation.
19. The method of claim 16, further comprising the steps of:
using a window function, combining at least one of said first transformed
series of points or said second transformed series of points with said
resulting value; and
performing an inverse window function to obtain a plethysmographic
waveform.
20. In a signal processor for processing at least first and second measured
signals, each containing a primary signal portion and a secondary signal
portion, said first and second signals substantially adhering to a signal
model, a method comprising the steps of:
sampling said first and second signals over a period to obtain a first
series of data points representing said first signal over said period and
a second series of data points representing said second signal over said
period;
performing a fast saturation transform with said first and second series of
data points to obtain a series of transformed data points in said
frequency domain;
determining a selected saturation value from said series of transformed
data points.
21. The method of claim 20, wherein said selected saturation value is
arterial blood oxygen saturation.
22. The method of claim 20, wherein said selected saturation value is
venous blood oxygen saturation.
23. The method of claim 20, wherein said step of performing said fast
saturation transform comprises calculating first and second pluralities of
intermediary transformed points from said first and second series of data
points, said method further comprising the step of determining a pulse
rate from said selected saturation value and from said first plurality of
intermediary transformed points. |
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Claims  |
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Description  |
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BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to the field of signal processing. More
specifically, the present invention relates to the processing of measured
signals, containing a primary signal portion and a secondary signal
portion, for the removal or derivation of either the primary or secondary
signal portion when little is known about either of these components. More
particularly, the present invention relates to modeling the measured
signals in a novel way which facilitates minimizing the correlation
between the primary signal portion and the secondary signal portion in
order to produce a primary and/or secondary signal. The present invention
is especially useful for physiological monitoring systems including blood
oxygen saturation systems.
2. Description of the Related Art
Signal processors are typically employed to remove or derive either the
primary or secondary signal portion from a composite measured signal
including a primary signal portion and a secondary signal portion. For
example, a composite signal may contain noise and desirable portions. If
the secondary signal portion occupies a different frequency spectrum than
the primary signal portion, then conventional filtering techniques such as
low pass, band pass, and high pass filtering are available to remove or
derive either the primary or the secondary signal portion from the total
signal. Fixed single or multiple notch filters could also be employed if
the primary and/or secondary signal portion(s) exist at a fixed
frequency(s).
It is often the case that an overlap in frequency spectrum between the
primary and secondary signal portions exists. Complicating matters
further, the statistical properties of one or both of the primary and
secondary signal portions change with time. In such cases, conventional
filtering techniques are ineffective in extracting either the primary or
secondary signal. If, however, a description of either the primary or
secondary signal portion can be derived, correlation canceling, such as
adaptive noise canceling, can be employed to remove either the primary or
secondary signal portion of the signal isolating the other portion. In
other words, given sufficient information about one of the signal
portions,that signal portion can be extracted.
Conventional correlation cancelers, such as adaptive noise cancelers,
dynamically change their transfer function to adapt to and remove portions
of a composite signal. However, correlation cancelers require either a
secondary reference or a primary reference which correlates to either the
secondary signal portion only or the primary signal portion only. For
instance, for a measured signal containing noise and desirable signal, the
noise can be removed with a correlation canceler if a noise reference is
available. This is often the case. Although the amplitude of the reference
signals are not necessarily the same as the amplitude of the corresponding
primary or secondary signal portions, they have a frequency spectrum which
is similar to that of the primary or secondary signal portions.
In many cases, nothing or very little is known about the secondary and/or
primary signal portions. One area where measured signals comprising a
primary signal portion and a secondary signal portion about which no
information can easily be determined is physiological monitoring.
Physiological monitoring generally involves measured signals derived from
a physiological system, such as the human body. Measurements which are
typically taken with physiological monitoring systems include
electrocardiographs, blood pressure, blood gas saturation (such as oxygen
saturation), capnographs, other blood constituent monitoring, heart rate,
respiration rate, electro-encephalograph (EEG) and depth of anesthesia,
for example. Other types of measurements include those which measure the
pressure and quantity of a substance within the body such as cardiac
output, venous oxygen saturation, arterial oxygen saturation, bilirubin,
total hemoglobin, breathalyzer testing, drug testing, cholesterol testing,
glucose testing, extra vasation, and carbon dioxide testing, protein
testing, carbon monoxide testing, and other in-vivo measurements, for
example. Complications arising in these measurements are often due to
motion of the patient, both external and internal (muscle movement, vessel
movement, and probe movement, for example), during the measurement
process.
Many types of physiological measurements can be made by using the known
properties of energy attenuation as a selected form of energy passes
through a medium.
A blood gas monitor is one example of a physiological monitoring system
which is based upon the measurement of energy attenuated by biological
tissues or substances. Blood gas monitors transmit light into the test
medium and measure the attenuation of the light as a function of time. The
output signal of a blood gas monitor which is sensitive to the arterial
blood flow contains a component which is a waveform representative of the
patient's arterial pulse. This type of signal, which contains a component
related to the patient's pulse, is called a plethysmographic wave, and is
shown in FIG. 1 as curve s. Plethysmographic waveforms are used in blood
gas saturation measurements. As the heart beats, the amount of blood in
the arteries increases and decreases, causing increases and decreases in
energy attenuation, illustrated by the cyclic wave s in FIG. 1.
Typically, a digit such as a finger, an ear lobe, or other portion of the
body where blood flows close to the skin, is employed as the medium
through which light energy is transmitted for blood gas attenuation
measurements. The finger comprises skin, fat, bone, muscle, etc., shown
schematically in FIG. 2, each of which attenuates energy incident on the
finger in a generally predictable and constant manner. However, when
fleshy portions of the finger are compressed erratically, for example by
motion of the finger, energy attenuation becomes erratic.
An example of a more realistic measured waveform S is shown in FIG. 3,
illustrating the effect of motion. The primary plethysmographic waveform
portion of the signal s is the waveform representative of the pulse,
corresponding to the sawtooth-like pattern wave in FIG. 1. The large,
secondary motion-induced excursions in signal amplitude obscure the
primary plethysmographic signal s. Even small variations in amplitude make
it difficult to distinguish the primary signal component s in the presence
of a secondary signal component n.
A pulse oximeter is a type of blood gas monitor which non-invasively
measures the arterial saturation of oxygen in the blood. The pumping of
the heart forces freshly oxygenated blood into the arteries causing
greater energy attenuation. As well understood in the art, the arterial
saturation of oxygenated blood may be determined from the depth of the
valleys relative to the peaks of two plethysmographic waveforms measured
at s | | |