U.S. patent application number 14/659205 was filed with the patent office on 2015-11-12 for selection of filter parameters based on signal quality.
The applicant listed for this patent is Covidien LP. Invention is credited to Paul F. Stetson.
Application Number | 20150320360 14/659205 |
Document ID | / |
Family ID | 32711565 |
Filed Date | 2015-11-12 |
United States Patent
Application |
20150320360 |
Kind Code |
A1 |
Stetson; Paul F. |
November 12, 2015 |
SELECTION OF FILTER PARAMETERS BASED ON SIGNAL QUALITY
Abstract
Methods and devices for reducing noise effects in a system for
measuring a physiological parameter, including receiving an input
signal, obtaining an assessment of the signal quality of the input
signal, selecting coefficients for a digital filter using the
assessment of signal quality; and filtering the input signal using
the digital filter, without comparing the filter's output signal
with the input signal.
Inventors: |
Stetson; Paul F.; (Oakland,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Covidien LP |
Mansfield |
MA |
US |
|
|
Family ID: |
32711565 |
Appl. No.: |
14/659205 |
Filed: |
March 16, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11247427 |
Oct 11, 2005 |
8983800 |
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14659205 |
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10341722 |
Jan 13, 2003 |
7016715 |
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11247427 |
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Current U.S.
Class: |
600/324 ;
600/336 |
Current CPC
Class: |
A61B 5/14551 20130101;
A61B 5/02416 20130101; A61B 5/725 20130101; G06K 9/00503 20130101;
A61B 5/01 20130101; A61B 5/7239 20130101; A61B 5/7221 20130101;
A61B 5/7203 20130101; A61B 5/021 20130101; A61B 5/14552
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/1455 20060101 A61B005/1455 |
Claims
1. A method, comprising: receiving, at a processor, an input
signal; determining, with the processor, an assessment of signal
quality for the input signal by determining a skew of a time
derivative of the input signal or a variation in signal amplitude
of the input signal; selecting, with the processor, one or more
coefficients for a digital filter using the assessment of signal
quality without comparing an output of the digital filter with the
input signal; and filtering the input signal using the digital
filter.
2. The method of claim 1, wherein determining, with the processor,
the assessment of signal quality comprises determining the skew of
a time derivative of the input signal and determining the variation
in signal amplitude of the input signal.
3. The method of claim 2, wherein determining, with the processor,
the assessment of signal quality comprises applying a first weight
to the skew of the time derivative of the input signal and applying
a second weight to the variation in signal amplitude of the input
signal.
4. The method of claim 1, wherein selecting, with the processor,
the one or more coefficients for the digital filter comprises
selecting one or more coefficients from a plurality of discrete
preset values.
5. The method of claim 1, wherein the digital filter comprises a
linear digital filter.
6. The method of claim 1, wherein the digital filter comprises a
non-linear digital filter.
7. The method of claim 1, wherein receiving, at the processor, the
input signal comprises receiving the input signal from a pulse
oximetry sensor, and wherein the input signal comprises a red
waveform and an infrared waveform.
8. The method of claim 7, wherein determining, with the processor,
the assessment of signal quality for the input signal comprises
determining a measure indicative of a degree of similarity between
the red waveform and the infrared waveform.
9. The method of claim 7, wherein determining, with the processor,
an assessment of signal quality for the input signal comprises
determining a measure indicative of a low light level.
10. The method of claim 7, wherein determining, with the processor,
an assessment of signal quality for the input signal comprises
determining a measure indicative of an arterial pulse shape.
11. A monitor, comprising: a processor configured to: receive an
input signal from a medical sensor; determine an assessment of
signal quality for the input signal by determining a skew of a time
derivative of the input signal or a variation in signal amplitude
of the input signal; and determine one or more digital filter
coefficients using the assessment of signal quality; and a digital
filter configured to filter the input signal using the one or more
digital filter coefficients, wherein the processor is configured to
determine the one or more digital filter coefficients without
comparing an output of the digital filter to the input signal.
12. The monitor of claim 11, wherein the digital filter comprises a
linear digital filter.
13. The monitor of claim 11, wherein the processor is configured to
determine the skew of a time derivative of the input signal and the
variation in signal amplitude of the input signal to determine the
assessment of signal quality for the input signal.
14. The monitor of claim 13, wherein the processor is configured to
apply a first weight to the skew of the time derivative of the
input signal and apply a second weight to the variation in signal
amplitude of the input signal to determine the assessment of signal
quality for the input signal.
15. The monitor of claim 11, wherein the medical sensor comprises a
pulse oximetry sensor and the input signal comprises a red waveform
and an infrared waveform.
16. The monitor of claim 15, wherein the processor is configure to
determine a measure indicative of a degree of similarity between
the red waveform and the infrared waveform to determine the
assessment of signal quality of the input signal.
17. A system, comprising: a medical sensor configured to generate a
physiological signal; a processor configured to: receive the
physiological signal from the medical sensor; determine an
assessment of signal quality for the physiological signal by
determining a skew of a time derivative of the physiological signal
or a variation in signal amplitude of the physiological signal; and
determine one or more digital filter coefficients using the
assessment of signal quality; and a digital filter configured to
filter the physiological signal using the one or more digital
filter coefficients; wherein the processor is configured to
determine the one or more digital filter coefficients without
comparing an output of the digital filter to the physiological
signal.
18. The system of claim 17, wherein the medical sensor comprises a
pulse oximetry sensor, and wherein the processor is configured to
determine a measure indicative of an arterial pulse shape to
determine the assessment of signal quality for the physiological
signal.
19. The system of claim 18, comprising a memory storing a plurality
of digital filter coefficients, and wherein the processor is
configured to select the one or more digital filter coefficients
from the plurality of digital filter coefficients.
20. The system of claim 17, wherein the processor is configured to
determine the skew of the time derivative of the physiological
signal and the variation in signal amplitude of the physiological
signal and to apply a first weight to the skew of the time
derivative of the physiological signal and a second weight to the
variation in signal amplitude of the physiological signal to
determine the assessment of signal quality of the physiological
signal.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation of U.S. patent
application Ser. No. 11/247,427, filed Oct. 11, 2005, which is a
Continuation of U.S. Pat. No. 7,016,715, filed Jan. 13, 2003, the
disclosures of which are hereby incorporated by reference for all
purposes.
BACKGROUND
[0002] Present embodiments relate to the processing of signals
obtained from a medical diagnostic apparatus, such as a pulse
oximeter, using a digital filter to reduce noise effects.
[0003] A typical pulse oximeter measures two physiological
parameters, percent oxygen saturation of arterial blood hemoglobin
(SpO.sub.2 or sat) and pulse rate. Oxygen saturation can be
estimated using various techniques. In one common technique, the
photocurrent generated by the photo-detector is conditioned and
processed to determine the ratio of modulation rations (ratio of
ratios) of the red to infrared signals. This modulation ratio has
been observed to correlate well to arterial oxygen saturation. The
pulse oximeters and sensors are empirically calibrated by measuring
the modulation ratio over a range of in vivo measured arterial
oxygen saturations (SaO.sub.2) on a set of patients, healthy
volunteers, or animals. The observed correlation is used in an
inverse manner to estimate blood oxygen saturation (SpO.sub.2)
based on the measured value of modulation ratios of a patient. The
estimation of oxygen saturation using modulation ratios is
described in U.S. Pat. No. 5,853,364, entitled "METHOD AND
APPARATUS FOR ESTIMATING PHYSIOLOGICAL PARAMETERS USING MODEL-BASED
ADAPTIVE FILTERING," issued Dec. 29, 1998, and U.S. Pat. No.
4,911,167, entitled "METHOD AND APPARATUS FOR DETECING OPTICAL
PULSES," issued Mar. 27, 1990. The relationship between oxygen
saturation and modulation ratio is further described in U.S. Pat.
No. 5,645,059, entitled "MEDICAL SENSOR WITH MODULATED ENCODING
SCHEME," issued Jul. 8, 1997. Most pulse oximeters extract the
plethysmographic signal having first determined saturation or pulse
rate, both of which are susceptible to interference.
[0004] A challenge in pulse oximetry is in analyzing the data to
obtain a reliable measure of a physiologic parameter in the
presence of large interference sources. Various solutions to this
challenge have included methods that assess the quality of the
measured parameter and decide on displaying the measured value when
it is deemed reliable based upon a signal quality. Another approach
involves a heuristic-based signal extraction technology, where the
obtained signals are processed based on a series of guesses of the
ratio, and which require the algorithm to start with a guess of the
ratio, which is an unknown. Both the signal-quality determining and
the heuristic signal extraction technologies are attempts at
separating out a reliable signal from an unreliable one, one method
being a phenomenological one and the other being a heuristic
one.
[0005] A known approach for the reduction of noise in medical
diagnostic devices including pulse oximeters involves the use of an
adaptive filter, such as an adaptive digital filter. The adaptive
filter is actually a data processing algorithm, and in most typical
applications, the filter is a computer program that is executed by
a central processor. As such, the filter inherently incorporates
discrete-time measurement samples rather than continuous time
inputs. A type of digital filter that is used in pulse oximeter
systems is a Kalman filter. While conventional adaptive digital
filters in general and Kalman filters in particular have been
assimilated in medical diagnostics system to help reduce noise in a
signal, there are still many challenges that need to be addressed
to improve the techniques that are used to reduce noise effects in
signals; noise effects such as those present in a medical
diagnostic device. One of the shortcomings of using a Kalman filter
is that a Kalman filter is an adaptive filter whose functioning is
mathematically-based and where its aim is to compare the output of
the filter with a desired output, and reduce the error in the
comparison by continuously varying the filter's coefficients. So, a
Kalman filter generates filter coefficients in an adaptive manner
to minimize an error. While this method has been adopted by many,
it is still a method that is somewhat blind regarding the signal
that it is being filtered. Such an approach does not take into
account the unique attributes that an input signal may possess and
which are physiologically based. Another shortcoming of the Kalman
filtering is that the Kalman filter is linear in its input-output
relationship. One can appreciate that in certain conditions, the
requirement that the filter be linear in its input-output
relationship is too constraining Yet another shortcoming of a
Kalman filter is that filter parameters are continuously tuned,
which can be computationally expensive.
[0006] There is therefore a need to develop a filter for reducing
noise effects in signals that does not suffer from the
above-mentioned constraints of conventional adaptive filters.
BRIEF SUMMARY
[0007] Present embodiments are directed towards methods and devices
for reducing noise effects in a system for measuring a
physiological parameter, including receiving an input signal;
obtaining an assessment of the signal quality of the input signal;
selecting coefficients for a digital filter using the assessment of
a signal quality; and filtering the input signal using the digital
filter, without comparing the filter's output signal with the input
signal.
[0008] In certain aspects, the filter coefficients are selected
from a plurality of discrete preset values. In certain embodiments,
the discrete and preset values are fixed or non-changing values.
The digital filter can have either a linear or preferably a
non-linear input-output relationship.
[0009] In pulse oximetry applications, the quality of the input
signal may be assessed by obtaining or measuring signal parameters
that include the skew of the time derivative of the signal; the
correlation between signals from different wavelengths; the
variation in signal amplitude, as well as others. Other
assessments, such as maximum values or spectral peak frequencies,
may also be used in determining filter parameters.
[0010] In some embodiments, the selection of filter parameters or
coefficients is performed in real time, with the coefficients of
the digital filter being determined using a current input sample.
In certain other embodiments, the selection of filter parameters is
performed using a previously stored input signal sample.
[0011] In pulse oximetry applications, the input signals can be a
function of an oxygen saturation, or a pulse rate. Furthermore,
these signals correspond with sensed optical energies from a
plurality of wavelengths.
[0012] For a further understanding of a nature and advantages of
the present embodiments, reference should be made to the following
description taken in conjunction with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a block diagram of an exemplary oximeter.
[0014] FIG. 2 is a block diagram depicting the operation of the
signal-quality-based filter operation in accordance with
embodiments.
DETAILED DESCRIPTION
[0015] The methods and systems in accordance with embodiments are
directed towards selecting and adjusting the parameters of a
digital filter based an assessment of the quality of the input
signals to the filter. Embodiments are particularly applicable to
and will be explained by reference to measurements of oxygen
saturation of hemoglobin in arterial blood and patient heart rate,
as in pulse oximeter monitors and pulse oximetry sensors. However,
it should be realized that the present embodiments are equally
applicable to any generalized patient monitor and associated
patient sensor, such as ECG, blood pressure, temperature, etc., and
is not to be limited for use only with oximetry or pulse
omimetry.
[0016] FIG. 1 is a block diagram of one embodiment of a pulse
oximeter that may be configured to implement embodiments. The
filter embodiments can be a data processing algorithm that is
executed by the microprocessor 122, described below. Light from
light source 110 passes into patient tissue 112, and is scattered
and detected by photodetector 114. A sensor 100 containing the
light source and photodetector may also contain an encoder 116
which provides signals indicative of the wavelength of light source
110 to a detector/decoder 144 in a pulse oximeter 120 to allow the
oximeter to select appropriate calibration coefficients for
calculating oxygen saturation. Encoder 116 may, for instance, be a
resistor.
[0017] Sensor 100 is connected to a pulse oximeter 120. The
oximeter includes a microprocessor 122 connected to an internal bus
124. Also connected to the bus is a RAM memory 126 and a display
128. A time processing unit (TPU) 130 provides timing control
signals to light drive circuitry 132 which controls when light
source 110 is illuminated, and if multiple light sources are used,
the multiplexed timing for the different light sources. TPU 130
also controls the gating-in of signals from photodetector 114
through an amplifier 133 and a switching circuit 134. These signals
are sampled at the proper time, depending upon which of multiple
light sources is illuminated, if multiple light sources are used.
The received signal is passed through an amplifier 136, a low pass
filter 138, and an analog-to-digital converter 140. The digital
data is then stored in a queued serial module (QSM) 142, for later
downloading to RAM 126 as QSM 142 fills up. In one embodiment,
there may be multiple parallel paths of separate amplifier filter
and A/D converters for multiple light wavelengths or spectrums
received.
[0018] Based on the value of the received signals corresponding to
the light received by photodetector 114, microprocessor 122 will
calculate the oxygen saturation using various algorithms. These
algorithms require coefficients, which may be empirically
determined, corresponding to, for example, the wavelengths of light
used. These are stored in a ROM 146. In a two-wavelength system,
the particular set of coefficients chosen for any pair of
wavelength spectrums is determined by the value indicated by
encoder 116 corresponding to a particular light source in a
particular sensor 100. In one embodiment, multiple resistor values
may be assigned to select different sets of coefficients. In
another embodiment, the same resistors are used to select from
among the coefficients appropriate for an infrared source paired
with either a near red source or far red source. The selection
between whether the near red or far red set will be chosen can be
selected with a control input from control inputs 154. Control
inputs 154 may be, for instance, a switch on the pulse oximeter, a
keyboard, or a port providing instructions from a remote host
computer. Furthermore, any number of methods or algorithms may be
used to determine a patient's pulse rate, oxygen saturation or any
other desired physiological parameter.
[0019] The brief description of an exemplary pulse oximeter set
forth above, serves as a contextual fabric for describing the
methods for reducing noise effects in the received signals
according to embodiments, which are described below. The
embodiments which are used to reduce the noise effects in the
signal using an assessment of the quality of the input signal, are
described below in conjunction with the block diagram of FIG.
2.
[0020] A signal quality indicator is a measured parameter that is
capable of estimating the reliability and accuracy of a signal. For
example, when measuring blood oxygen saturation using a pulse
oximeter, a signal quality indicator is able to indirectly assess
whether an estimate of a value of blood oxygen saturation is an
accurate one. This determination of accuracy is made possible by
the thorough and detailed study of volumes of measured values and
various indicators to determine which indicators are indicative of
signal quality and what, if any, is the correlation between the
indicator and the accuracy of the estimated value.
[0021] In pulse oximetry, examples of signal quality indicators
include the skew of the time derivative of the signal; the
correlation between signals from different wavelengths; the
variation in signal amplitude, as well as others. Other
assessments, such as maximum values or spectral peak frequencies,
may also be used in determining filter parameters. In addition to
these signal quality indicators, other signal quality indicators
may also be used for the selection of filter coefficients. In pulse
oximetry, these additional signal quality indicators include: a
signal measure indicative of the degree of similarity of an
infrared and red waveforms; a signal measure indicative of a low
light level; a signal measure indicative of an arterial pulse
shape; a signal measure indicative of the high frequency signal
component in the measure value; a signal measure indicative of a
consistency of a pulse shape; a signal measure indicative of an
arterial pulse amplitude; a signal measure indicative of modulation
ratios of red to infrared modulations and a signal measure
indicative of a period of an arterial pulse. These various
indicators provide for an indirect assessments of the presence of
known error sources in pulse oximetry measurements, which include
optical interference between the sensor and the tissue location;
light modulation by other than the patient's pulsatile tissue bed;
physical movement of the patient and improper tissue-to-sensor
positioning. These additional signal quality indicators are
described in further detail in a co-pending US patent application
entitled: "SIGNAL QUALITY METRICS DESIGN FOR QUALIFYING DATA FOR A
PHYSIOLOGICAL MONITOR," U.S. Pat. No. 7,006,856, filed Jan. 10,
2003, the disclosure of which is herein incorporated by reference
in its entirety for all purposes.
[0022] FIG. 2 is a block diagram 200 depicting the operation of the
signal-quality-based selection of filter parameters in accordance
with embodiments. In one embodiment, the digital filter is a linear
filter. For a linear filter is chosen, the filter can have either a
finite or an infinite impulse response. Alternately, the filter may
be a non-linear filter. Inputs 202 are applied to the digital
filter 204 and to a signal quality assessment subsystem 206 that
assesses how noisy the inputs look. Subsystem 206 calculates
various signal quality metrics and provides the information to the
selection subsystem 208, which selects filter parameters according
to the criteria calculated by the signal quality subsystem 206.
Storage subsystem 210 interfaces with the subsystems 206 and 208 to
store and provide signal quality metrics as well as filter
parameters. In one embodiment, the selection of filter parameters
is performed in real time, with the filter parameters being
determined using current input samples.
[0023] In an alternate embodiment, the filter parameters are
calculated using a buffer 212 of recent input samples. In addition,
signal assessment criteria and filter parameters can also be held
in storage 210 for reference or for use in the calculation of new
values.
[0024] As set forth above, various signal quality indicators may be
used to select filter parameters. Additionally, the selection of
the filter parameters may be based on more than one signal quality
indicator. Furthermore, the selection of the filter parameters may
be based on the output of an algorithm that combines several signal
quality indicators. In one embodiment in an oximeter system, the
variance in the raw saturation value is used to determine the
filter's smoothing coefficients. In this embodiment, the selection
is achieved by comparing the variance in the raw sat signal to
several thresholds, and the filter's smoothing coefficients are
selected depending on which range the variance falls in.
[0025] In an alternate embodiment in an oximeter system used for
average pulse estimation, the filter parameter selection algorithm
uses a combination of various signal quality metrics, Z to select
values for filter coefficients for the digital filter, where
[0026] Z=w.sub.1*SQ1+w.sub.2*SW2+w.sub.3*SQ3, where
[0027] w.sub.1, w.sub.2, and w.sub.3 are weighting factors
[0028] SQ1 is a measure of the variance in the raw saturation
signal
[0029] SQ2 is a measure of the correlation between signals from
different wavelengths
[0030] SQ3 is a measure of the skew of the derivative waveform
[0031] Yet alternately, instead of using Z to select the filter
coefficients, a non-linear function of Z can be used to select a
coefficient or coefficients for the filter. In operation, the
selection algorithm may first be tuned before it is fully
implemented in a particular diagnostics system. The tuning of the
selection algorithm(s) may be done manually using heuristic
approaches. Alternately, the selection algorithm may be tuned
statistically, in a manner similar to training a neural
network.
[0032] Embodiments offer several advantages over conventional
adaptive filtering. It is known that conventional adaptive
filtering seeks to optimize some output criterion by continuously
tuning the coefficients in a linear filter. The approach is
advantageous over conventional adaptive filtering for the following
reasons. First, filter parameters in accordance with embodiments
are selected by switching among several preset or fixed values,
rather than being varied or tuned continuously. By switching the
parameters of the digital filter among fixed, preset values, the
embodiments provide for computational savings and simplicity of
implementation. Second, the parameters of the digital filter are
selected based upon an assessment of the input signal received by
the filter rather than by comparing the filter's output with its
input. This too, provides for computational savings and simplicity
of implementation. Third, the filter need not be a linear filter,
that is the filter is not required to be linear in its input-output
relationship. Since the filter in accordance with embodiments is
not constrained to be linear, the filter's design can correspond
more to physiological than to mathematical requirements, as is the
case with most conventional adaptive filtering schemes. This
physiological-based filter parameter selection may be used to, for
example, attenuate pulse amplitudes above a threshold, or respond
more quickly to decreases than to increases in blood oxygen
saturation.
[0033] Accordingly, as will be understood by those of skill in the
art, embodiments related to reducing noise effects in a system for
measuring a physiological parameter, may be embodied in other
specific forms without departing from the essential characteristics
thereof For example, signals indicative of any physiological
parameter other than oxygen saturation, such as pulse rate, blood
pressure, temperature, or any other physiological variable could be
filtered using the techniques above. Moreover, many other
indicators of the quality of the input signal can be used as a
basis for the selection of the filter's coefficients. Further,
while the present embodiments have been described in the
time-domain, frequency-base methods are equally relevant to the
embodiments. Accordingly, the foregoing disclosure is intended to
be illustrative, but not limiting.
* * * * *