U.S. patent application number 13/874975 was filed with the patent office on 2013-09-19 for signal processing techniques for aiding the interpretation of respiration signals.
This patent application is currently assigned to Nellcor Puritan Bennett Ireland. The applicant listed for this patent is NELLCOR PURITAN BENNETT IRELAND. Invention is credited to Paul Stanley Addison, Scott McGonigle, James Nicholas Watson.
Application Number | 20130245482 13/874975 |
Document ID | / |
Family ID | 42312910 |
Filed Date | 2013-09-19 |
United States Patent
Application |
20130245482 |
Kind Code |
A1 |
McGonigle; Scott ; et
al. |
September 19, 2013 |
SIGNAL PROCESSING TECHNIQUES FOR AIDING THE INTERPRETATION OF
RESPIRATION SIGNALS
Abstract
According to embodiments, a respiration signal may be processed
to normalize respiratory feature values in order to improve and/or
simplify the interpretation and subsequent analysis of the signal.
Data indicative of a signal may be received at a sensor and may be
used to generate a respiration signal. Signal peaks in the
respiration signal may be identified and signal peak thresholds may
be determined. The identified signal peaks may be adjusted based on
the signal peak threshold values to normalize the respiration
signal.
Inventors: |
McGonigle; Scott; (East
Lothian, GB) ; Addison; Paul Stanley; (Edinburgh,
GB) ; Watson; James Nicholas; (Dunfermline,
GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NELLCOR PURITAN BENNETT IRELAND |
Mervue |
|
IE |
|
|
Assignee: |
Nellcor Puritan Bennett
Ireland
Mervue
IE
|
Family ID: |
42312910 |
Appl. No.: |
13/874975 |
Filed: |
May 1, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12481045 |
Jun 9, 2009 |
8444570 |
|
|
13874975 |
|
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Current U.S.
Class: |
600/529 |
Current CPC
Class: |
A61B 5/0816 20130101;
A61B 5/7278 20130101; A61B 5/7203 20130101; A61B 5/7207 20130101;
A61B 5/0002 20130101; A61B 5/726 20130101; A61B 5/14551 20130101;
A61B 5/02416 20130101; A61B 5/08 20130101; A61B 2562/08
20130101 |
Class at
Publication: |
600/529 |
International
Class: |
A61B 5/08 20060101
A61B005/08 |
Claims
1. A method for processing a physiological signal, comprising:
receiving, using a processor, the physiological signal; generating,
using the processor, a wavelet transform based at least in part on
the physiological signal, wherein the wavelet transform comprises
phase information; identifying, using the processor, phase values
corresponding to respiration based at least in part on the wavelet
transform; and generating, using the processor, a substantially
sinusoidal function based at least in part on the phase values,
wherein the substantially sinusoidal function is indicative of
respiration phase.
2. The method of claim 1, wherein the substantially sinusoidal
function comprises normalized height values.
3. The method of claim 1, wherein identifying the phase values
comprises identifying a respiration ridge based at least in part on
the wavelet transform.
4. The method of claim 3, wherein the respiration ridge comprises
local phase values as a function of time.
5. The method of claim 1, wherein generating the sinusoidal
function comprises determining the cosine or sine of the phase
values.
6. The method of claim 1, wherein generating the sinusoidal
function comprises determining an inverse wavelet transform of the
phase values as a function of time.
7. The method of claim 1, further comprising determining a
respiration parameter based at least in part on the substantially
sinusoidal function.
8. The method of claim 1, further comprising generating a display
based at least in part on the substantially sinusoidal
function.
9. The method of claim 8, wherein the display comprises a bar
graph.
10. The method of claim 1, wherein the physiological signal is a
photoplethysmograph signal.
11. A system for processing a physiological signal, comprising: a
processor configured to perform operations comprising: receiving
the physiological signal; generating a wavelet transform based at
least in part on the physiological signal, wherein the wavelet
transform comprises phase information; identifying phase values
corresponding to respiration based at least in part on the wavelet
transform; and generating a substantially sinusoidal function based
at least in part on the phase values, wherein the substantially
sinusoidal function is indicative of respiration phase.
12. The system of claim 11, wherein the substantially sinusoidal
function comprises normalized height values.
13. The system of claim 11, wherein identifying the phase values
comprises identifying a respiration ridge based at least in part on
the wavelet transform.
14. The system of claim 13, wherein the respiration ridge comprises
local phase values as a function of time.
15. The system of claim 11, wherein generating the sinusoidal
function comprises determining the cosine or sine of the phase
values.
16. The system of claim 11, wherein generating the sinusoidal
function comprises determining an inverse wavelet transform of the
phase values as a function of time.
17. The system of claim 11, wherein the processor is configured to
perform operations further comprising determining a respiration
parameter based at least in part on the substantially sinusoidal
function.
18. The system of claim 11, wherein the processor is configured to
perform operations further comprising generating a display based at
least in part on the substantially sinusoidal function.
19. The system of claim 18, wherein the display comprises a bar
graph.
20. The system of claim 11, wherein the physiological signal is a
photoplethysmograph signal.
21. A method for processing a respiration signal, comprising:
obtaining, using a processor, the respiration signal; determining,
using the processor, one or more signal thresholds for the
respiration signal based at least in part on the respiration
signal; identifying, using the processor, one or more portions of
the respiration signal that exceed at least one of the one or more
signal thresholds; selectively adjusting, using the processor, the
amplitude of the respiration signal based at least in part on the
identified one or more portions to generate an adjusted respiration
signal, wherein the amplitude of at least one peak in the
respiration signal is adjusted; and determining, using the
processor, a respiration parameter based at least in part on the
adjusted respiration signal.
22. The method of claim 21, wherein the one or more signal
thresholds comprise an upper threshold and a lower threshold,
wherein selectively adjusting comprises reducing the amplitude of
the at least one peak, and wherein at least one other peak below
the lower threshold remains unadjusted.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/481,045, filed Jun. 9, 2009, which is
incorporated by reference herein in its entirety.
SUMMARY
[0002] The present disclosure is related to signal processing
systems and methods, and more particularly, to systems and methods
for processing respiration signals.
[0003] In an embodiment, a respiration signal may be processed to
normalize respiratory feature values of the signal. Respiration
signals may indicate the breathing patterns of a patient over time.
Respiratory features (e.g., signal peaks) within the respiration
signal may reflect the breathing of the patient. Respiratory
features within the respiration signal may also reflect noise or
other artifacts. The respiration signal may be normalized by
reducing variations in the respiratory feature values within the
respiration signal. Normalizing the respiration signal may reduce
the effect of noise or other artifacts on the respiration signal
and may aid in the interpretation and/or analysis of the
respiration signal. For example, normalizing the respiration signal
may aid in the determination of respiration parameters such as
respiration rate.
[0004] In an embodiment, a respiration signal may be obtained using
a sensor capable of measuring the respiration of a patient or by
deriving the respiration signal from another suitable biosignal.
Respiratory features such as signal peaks (e.g., local maxima
and/or minima in the signal amplitude versus time) in the
respiration signal may be identified and signal peak thresholds may
be determined. In an embodiment, signal peak threshold values may
be determined based on the values of the identified signal peaks.
For example, signal peak threshold values may be related to a mean
value, a weighted mean value, a median value, a value at a certain
percentile of distribution of values, or any other suitable value.
An upper signal peak threshold value may be used to identify signal
peaks having values that exceed a particular value. A lower signal
peak threshold value may be used to identify signal peaks having
values that are below a particular value. The identified signal
peaks may then be adjusted based on the determined signal peak
threshold values to normalize the respiration signal.
[0005] In an embodiment, a portion of the respiration signal
surrounding an identified signal peak may be selected and the
entire selected portion of the signal may be adjusted. For example,
a signal segment may be a portion of the signal that begins at a
zero crossing before an identified signal peak and ends at a zero
crossing that after the signal peak. As another example, a signal
segment may be the a portion of a signal that exceeds a threshold
value.
[0006] In an embodiment, selected signal segments may be rescaled
by a constant value. In an embodiment, selected signal segments may
be nonlinearly rescaled based at least in part on a distance
between a signal peak another suitable values (e.g., a
characteristic value of the signal or a threshold value).
[0007] For the purposes of illustration, and not by way of
limitation, in an embodiment disclosed herein the respiration
signal may be derived from a photoplethysmograph (PPG) signal drawn
from any suitable source, such as a pulse oximeter. The PPG signal
may be filtered, processed or otherwise transformed before the
techniques described herein are applied to the signal. A scalogram
may be generated from the PPG signal data. Respiratory features may
be identified within the scalogram and/or within a secondary
wavelet decomposition of the scalogram. A respiration signal may be
generated from these identified respiratory features.
[0008] In an embodiment, a normalized respiration signal may be
generated from a scalogram of wavelet phase information calculated
from a PPG signal. A respiration ridge representing local phase
values relating to respiratory features as a function of time may
be identified within the scalogram. A sinusoidal function
indicative of respiration phase and having normalized height values
may then be generated from these local phase values.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The above and other features of the present disclosure, its
nature and various advantages will be more apparent upon
consideration of the following detailed description, taken in
conjunction with the accompanying drawings in which:
[0010] FIG. 1 shows an illustrative pulse oximetry system in
accordance with an embodiment;
[0011] FIG. 2 is a block diagram of the illustrative pulse oximetry
system of FIG. 1 coupled to a patient in accordance with an
embodiment;
[0012] FIGS. 3(a) and 3(b) show illustrative views of a scalogram
derived from a PPG signal in accordance with an embodiment;
[0013] FIG. 3(c) shows an illustrative scalogram derived from a
signal containing two pertinent components in accordance with an
embodiment;
[0014] FIG. 3(d) shows an illustrative schematic of signals
associated with a ridge in FIG. 3(c) and illustrative schematics of
a further wavelet decomposition of these newly derived signals in
accordance with an embodiment;
[0015] FIGS. 3(e) and 3(f) are flow charts of illustrative steps
involved in performing an inverse continuous wavelet transform in
accordance with embodiments;
[0016] FIG. 4 is a block diagram of an illustrative continuous
wavelet processing system in accordance with some embodiments;
[0017] FIG. 5 is an illustrative plot of a respiration signal in
accordance with an embodiment;
[0018] FIG. 6 is another illustrative plot of a respiration signal
in accordance with an embodiment;
[0019] FIG. 7 depicts an illustrative process for normalizing
respiratory feature values of a respiration signal in accordance
with an embodiment;
[0020] FIG. 8 depicts an illustrative process for adjusting one or
more respiration signal peaks in accordance with an embodiment;
and
[0021] FIG. 9 depicts an additional illustrative process for
generating a normalized respiration signal from a scalogram in
accordance with an embodiment.
DETAILED DESCRIPTION
[0022] An oximeter is a medical device that may determine the
oxygen saturation of the blood. One common type of oximeter is a
pulse oximeter, which may indirectly measure the oxygen saturation
of a patient's blood (as opposed to measuring oxygen saturation
directly by analyzing a blood sample taken from the patient) and
changes in blood volume in the skin. Ancillary to the blood oxygen
saturation measurement, pulse oximeters may also be used to measure
the pulse rate of the patient. Pulse oximeters typically measure
and display various blood flow characteristics including, but not
limited to, the oxygen saturation of hemoglobin in arterial
blood.
[0023] An oximeter may include a light sensor that is placed at a
site on a patient, typically a fingertip, toe, forehead or earlobe,
or in the case of a neonate, across a foot. The oximeter may pass
light using a light source through blood perfused tissue and
photoelectrically sense the absorption of light in the tissue. For
example, the oximeter may measure the intensity of light that is
received at the light sensor as a function of time. A signal
representing light intensity versus time or a mathematical
manipulation of this signal (e.g., a scaled version thereof, a log
taken thereof, a scaled version of a log taken thereof, etc.) may
be referred to as the photoplethysmograph (PPG) signal. In
addition, the term "PPG signal," as used herein, may also refer to
an absorption signal (i.e., representing the amount of light
absorbed by the tissue) or any suitable mathematical manipulation
thereof. The light intensity or the amount of light absorbed may
then be used to calculate the amount of the blood constituent
(e.g., oxyhemoglobin) being measured as well as the pulse rate and
when each individual pulse occurs.
[0024] The light passed through the tissue is selected to be of one
or more wavelengths that are absorbed by the blood in an amount
representative of the amount of the blood constituent present in
the blood. The amount of light passed through the tissue varies in
accordance with the changing amount of blood constituent in the
tissue and the related light absorption. Red and infrared
wavelengths may be used because it has been observed that highly
oxygenated blood will absorb relatively less red light and more
infrared light than blood with a lower oxygen saturation. By
comparing the intensities of two wavelengths at different points in
the pulse cycle, it is possible to estimate the blood oxygen
saturation of hemoglobin in arterial blood.
[0025] When the measured blood parameter is the oxygen saturation
of hemoglobin, a convenient starting point assumes a saturation
calculation based on Lambert-Beer's law. The following notation
will be used herein:
I(.lamda., t)=I.sub.0(.lamda.)
exp(-(s.beta..sub.0(.lamda.)+(1-s).beta..sub.r(.lamda.))l(t))
(1)
where: [0026] .lamda.=wavelength; [0027] t=time; [0028] I=intensity
of light detected; [0029] I.sub.o=intensity of light transmitted;
[0030] s=oxygen saturation; [0031] .beta..sub.o,
.beta..sub.t=empirically derived absorption coefficients; and
[0032] l(t)=a combination of concentration and path length from
emitter to detector as a function of time.
[0033] The traditional approach measures light absorption at two
wavelengths (e.g., red and infrared (IR)), and then calculates
saturation by solving for the "ratio of ratios" as follows. [0034]
1. First, the natural logarithm of (l) is taken ("log" will be used
to represent the natural logarithm) for IR and Red
[0034] log l=log I.sub.0-(s.beta..sub.0+(1-s).beta..sub.r)l (2)
[0035] 2. (2) is then differentiated with respect to time
[0035] log I t = - ( s .beta. 0 + ( 1 - s ) .beta. r ) l t ( 3 )
##EQU00001## [0036] 3. Red (3) is divided by IR (3)
[0036] log I ( .lamda. R ) / t log I ( .lamda. IR ) / t = s .beta.
0 ( .lamda. R ) + ( 1 - s ) .beta. r ( .lamda. R ) s .beta. 0 (
.lamda. IR ) + ( 1 - s ) .beta. r ( .lamda. IR ) ( 4 ) ##EQU00002##
[0037] 4. Solving for s
[0037] s = log I ( .lamda. IR ) t .beta. r ( .lamda. R ) - log I (
.lamda. R ) t .beta. r ( .lamda. IR ) log I ( .lamda. R ) t (
.beta. 0 ( .lamda. IR ) - .beta. r ( .lamda. IR ) ) - log I (
.lamda. IR ) t ( .beta. 0 ( .lamda. R ) - .beta. r ( .lamda. R ) )
##EQU00003## [0038] Note in discrete time
[0038] log I ( .lamda. , t ) t log I ( .lamda. , t 2 ) - log I (
.lamda. , t 1 ) ##EQU00004## [0039] Using log A-log B=log A/B,
[0039] log I ( .lamda. , t ) t log ( I ( t 2 , .lamda. ) I ( t 1 ,
.lamda. ) ) ##EQU00005## [0040] So, (4) can be rewritten as
[0040] log I ( .lamda. R ) t log I ( .lamda. IR ) t log ( I ( t 1 ,
.lamda. R ) I ( t 2 , .lamda. R ) ) log ( I ( t 1 , .lamda. IR ) I
( t 2 , .lamda. IR ) ) = R ( 5 ) ##EQU00006##
where R represents the "ratio of ratios." Solving (4) for s using
(5) gives
s = .beta. r ( .lamda. R ) - R .beta. r ( .lamda. IR ) R ( .beta. 0
( .lamda. IR ) - .beta. r ( .lamda. IR ) ) - .beta. 0 ( .lamda. R )
+ .beta. r ( .lamda. IR ) . ##EQU00007## [0041] From (5), R can be
calculated using two points (e.g., PPG maximum and minimum), or a
family of points. One method using a family of points uses a
modified version of (5). Using the relationship
[0041] log I t = I / t I ( 6 ) ##EQU00008##
now (5) becomes
log I ( .lamda. R ) t log I ( .lamda. IR ) t I ( t 2 , .lamda. R )
- I ( t 1 , .lamda. R ) I ( t 1 , .lamda. R ) I ( t 2 , .lamda. IR
) - I ( t 1 , .lamda. IR ) I ( t 1 , .lamda. IR ) = [ I ( t 2 ,
.lamda. R ) - I ( t 1 , .lamda. R ) ] I ( t 1 , .lamda. IR ) [ I (
t 2 , .lamda. IR ) - I ( t 1 , .lamda. IR ) ] I ( t 1 , .lamda. R )
= R ( 7 ) ##EQU00009##
which defines a cluster of points whose slope of y versus x will
give R where
x(t)=[I(t.sub.2, .lamda..sub.IR)-I(t.sub.1,
.lamda..sub.IR)]I(t.sub.1, .lamda..sub.R)
y(t)=[I(t.sub.2, .lamda..sub.R)-I(t.sub.1,
.lamda..sub.R)]I(t.sub.1, .lamda..sub.IR)
y(t)=Rx(t) (8)
[0042] FIG. 1 is a perspective view of an embodiment of a pulse
oximetry system 10. System 10 may include a sensor 12 and a pulse
oximetry monitor 14. Sensor 12 may include an emitter 16 for
emitting light at two or more wavelengths into a patient's tissue.
A detector 18 may also be provided in sensor 12 for detecting the
light originally from emitter 16 that emanates from the patient's
tissue after passing through the tissue.
[0043] According to another embodiment and as will be described,
system 10 may include a plurality of sensors forming a sensor array
in lieu of single sensor 12. Each of the sensors of the sensor
array may be a complementary metal oxide semiconductor (CMOS)
sensor. Alternatively, each sensor of the array may be charged
coupled device (CCD) sensor. In another embodiment, the sensor
array may be made up of a combination of CMOS and CCD sensors. The
CCD sensor may comprise a photoactive region and a transmission
region for receiving and transmitting data whereas the CMOS sensor
may be made up of an integrated circuit having an array of pixel
sensors. Each pixel may have a photodetector and an active
amplifier.
[0044] According to an embodiment, emitter 16 and detector 18 may
be on opposite sides of a digit such as a finger or toe, in which
case the light that is emanating from the tissue has passed
completely through the digit. In an embodiment, emitter 16 and
detector 18 may be arranged so that light from emitter 16
penetrates the tissue and is reflected by the tissue into detector
18, such as a sensor designed to obtain pulse oximetry data from a
patient's forehead.
[0045] In an embodiment, the sensor or sensor array may be
connected to and draw its power from monitor 14 as shown. In
another embodiment, the sensor may be wirelessly connected to
monitor 14 and include its own battery or similar power supply (not
shown). Monitor 14 may be configured to calculate physiological
parameters based at least in part on data received from sensor 12
relating to light emission and detection. In an alternative
embodiment, the calculations may be performed on the monitoring
device itself and the result of the oximetry reading may be passed
to monitor 14. Further, monitor 14 may include a display 20
configured to display the physiological parameters or other
information about the system. In the embodiment shown, monitor 14
may also include a speaker 22 to provide an audible sound that may
be used in various other embodiments, such as for example, sounding
an audible alarm in the event that a patient's physiological
parameters are not within a predefined normal range.
[0046] In an embodiment, sensor 12, or the sensor array, may be
communicatively coupled to monitor 14 via a cable 24. However, in
other embodiments, a wireless transmission device (not shown) or
the like may be used instead of or in addition to cable 24.
[0047] In the illustrated embodiment, pulse oximetry system 10 may
also include a multi-parameter patient monitor 26. The monitor may
be cathode ray tube type, a flat panel display (as shown) such as a
liquid crystal display (LCD) or a plasma display, or any other type
of monitor now known or later developed. Multi-parameter patient
monitor 26 may be configured to calculate physiological parameters
and to provide a display 28 for information from monitor 14 and
from other medical monitoring devices or systems (not shown). For
example, multiparameter patient monitor 26 may be configured to
display an estimate of a patient's blood oxygen saturation
generated by pulse oximetry monitor 14 (referred to as an
"SpO.sub.2" measurement), pulse rate information from monitor 14
and blood pressure from a blood pressure monitor (not shown) on
display 28.
[0048] Monitor 14 may be communicatively coupled to multi-parameter
patient monitor 26 via a cable 32 or 34 that is coupled to a sensor
input port or a digital communications port, respectively and/or
may communicate wirelessly (not shown). In addition, monitor 14
and/or multi-parameter patient monitor 26 may be coupled to a
network to enable the sharing of information with servers or other
workstations (not shown). Monitor 14 may be powered by a battery
(not shown) or by a conventional power source such as a wall
outlet.
[0049] FIG. 2 is a block diagram of a pulse oximetry system, such
as pulse oximetry system 10 of FIG. 1, which may be coupled to a
patient 40 in accordance with an embodiment. Certain illustrative
components of sensor 12 and monitor 14 are illustrated in FIG. 2.
Sensor 12 may include emitter 16, detector 18, and encoder 42. In
the embodiment shown, emitter 16 may be configured to emit at least
two wavelengths of light (e.g., RED and IR) into a patient's tissue
40. Hence, emitter 16 may include a RED light emitting light source
such as RED light emitting diode (LED) 44 and an IR light emitting
light source such as IR LED 46 for emitting light into the
patient's tissue 40 at the wavelengths used to calculate the
patient's physiological parameters. In one embodiment, the RED
wavelength may be between about 600 nm and about 700 nm, and the IR
wavelength may be between about 800 nm and about 1000 nm. In
embodiments where a sensor array is used in place of single sensor,
each sensor may be configured to emit a single wavelength. For
example, a first sensor emits only a RED light while a second only
emits an IR light.
[0050] It will be understood that, as used herein, the term "light"
may refer to energy produced by radiative sources and may include
one or more of ultrasound, radio, microwave, millimeter wave,
infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic
radiation. As used herein, light may also include any wavelength
within the radio, microwave, infrared, visible, ultraviolet, or
X-ray spectra, and that any suitable wavelength of electromagnetic
radiation may be appropriate for use with the present techniques.
Detector 18 may be chosen to be specifically sensitive to the
chosen targeted energy spectrum of the emitter 16.
[0051] In an embodiment, detector 18 may be configured to detect
the intensity of light at the RED and IR wavelengths.
Alternatively, each sensor in the array may be configured to detect
an intensity of a single wavelength. In operation, light may enter
detector 18 after passing through the patient's tissue 40. Detector
18 may convert the intensity of the received light into an
electrical signal. The light intensity is directly related to the
absorbance and/or reflectance of light in the tissue 40. That is,
when more light at a certain wavelength is absorbed or reflected,
less light of that wavelength is received from the tissue by the
detector 18. After converting the received light to an electrical
signal, detector 18 may send the signal to monitor 14, where
physiological parameters may be calculated based on the absorption
of the RED and IR wavelengths in the patient's tissue 40.
[0052] In an embodiment, encoder 42 may contain information about
sensor 12, such as what type of sensor it is (e.g., whether the
sensor is intended for placement on a forehead or digit) and the
wavelengths of light emitted by emitter 16. This information may be
used by monitor 14 to select appropriate algorithms, lookup tables
and/or calibration coefficients stored in monitor 14 for
calculating the patient's physiological parameters.
[0053] Encoder 42 may contain information specific to patient 40,
such as, for example, the patient's age, weight, and diagnosis.
This information may allow monitor 14 to determine, for example,
patient-specific threshold ranges in which the patient's
physiological parameter measurements should fall and to enable or
disable additional physiological parameter algorithms. Encoder 42
may, for instance, be a coded resistor which stores values
corresponding to the type of sensor 12 or the type of each sensor
in the sensor array, the wavelengths of light emitted by emitter 16
on each sensor of the sensor array, and/or the patient's
characteristics. In another embodiment, encoder 42 may include a
memory on which one or more of the following information may be
stored for communication to monitor 14: the type of the sensor 12;
the wavelengths of light emitted by emitter 16; the particular
wavelength each sensor in the sensor array is monitoring; a signal
threshold for each sensor in the sensor array; any other suitable
information; or any combination thereof.
[0054] In an embodiment, signals from detector 18 and encoder 42
may be transmitted to monitor 14. In the embodiment shown, monitor
14 may include a general-purpose microprocessor 48 connected to an
internal bus 50. Microprocessor 48 may be adapted to execute
software, which may include an operating system and one or more
applications, as part of performing the functions described herein.
Also connected to bus 50 may be a read-only memory (ROM) 52, a
random access memory (RAM) 54, user inputs 56, display 20, and
speaker 22.
[0055] RAM 54 and ROM 52 are illustrated by way of example, and not
limitation. Any suitable computer-readable media may be used in the
system for data storage. Computer-readable media are capable of
storing information that can be interpreted by microprocessor 48.
This information may be data or may take the form of
computer-executable instructions, such as software applications,
that cause the microprocessor to perform certain functions and/or
computer-implemented methods. Depending on the embodiment, such
computer-readable media may include computer storage media and
communication media. Computer storage media may include volatile
and non-volatile, removable and non-removable media implemented in
any method or technology for storage of information such as
computer-readable instructions, data structures, program modules or
other data. Computer storage media may include, but is not limited
to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state
memory technology, CD-ROM, DVD, or other optical storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic
storage devices, or any other medium which can be used to store the
desired information and which can be accessed by components of the
system.
[0056] In the embodiment shown, a time processing unit (TPU) 58 may
provide timing control signals to a light drive circuitry 60, which
may control when emitter 16 is illuminated and multiplexed timing
for the RED LED 44 and the IR LED 46. TPU 58 may also control the
gating-in of signals from detector 18 through an amplifier 62 and a
switching circuit 64. These signals are sampled at the proper time,
depending upon which light source is illuminated. The received
signal from detector 18 may be passed through an amplifier 66, a
low pass filter 68, and an analog-to-digital converter 70. The
digital data may then be stored in a queued serial module (QSM) 72
(or buffer) for later downloading to RAM 54 as QSM 72 fills up. In
one embodiment, there may be multiple separate parallel paths
having amplifier 66, filter 68, and A/D converter 70 for multiple
light wavelengths or spectra received.
[0057] In an embodiment, microprocessor 48 may determine the
patient's physiological parameters, such as SpO.sub.2 and pulse
rate, using various algorithms and/or look-up tables based on the
value of the received signals and/or data corresponding to the
light received by detector 18. Signals corresponding to information
about patient 40, and particularly about the intensity of light
emanating from a patient's tissue over time, may be transmitted
from encoder 42 to a decoder 74. These signals may include, for
example, encoded information relating to patient characteristics.
Decoder 74 may translate these signals to enable the microprocessor
to determine the thresholds based on algorithms or look-up tables
stored in ROM 52. User inputs 56 may be used to enter information
about the patient, such as age, weight, height, diagnosis,
medications, treatments, and so forth. In an embodiment, display 20
may exhibit a list of values which may generally apply to the
patient, such as, for example, age ranges or medication families,
which the user may select using user inputs 56.
[0058] The optical signal through the tissue can be degraded by
noise, among other sources. One source of noise is ambient light
that reaches the light detector. Another source of noise is
electromagnetic coupling from other electronic instruments.
Movement of the patient also introduces noise and affects the
signal. For example, the contact between the detector and the skin,
or the emitter and the skin, can be temporarily disrupted when
movement causes either to move away from the skin. In addition,
because blood is a fluid, it responds differently than the
surrounding tissue to inertial effects, thus resulting in momentary
changes in volume at the point to which the oximeter probe is
attached.
[0059] Noise (e.g., from patient movement) can degrade a pulse
oximetry signal relied upon by a physician, without the physician's
awareness. This is especially true if the monitoring of the patient
is remote, the motion is too small to be observed, or the doctor is
watching the instrument or other parts of the patient, and not the
sensor site. Processing pulse oximetry (i.e., PPG) signals may
involve operations that reduce the amount of noise present in the
signals or otherwise identify noise components in order to prevent
them from affecting measurements of physiological parameters
derived from the PPG signals.
[0060] It will be understood that the present disclosure is
applicable to any suitable signals and that PPG signals are used
merely for illustrative purposes. Those skilled in the art will
recognize that the present disclosure has wide applicability to
other signals including, but not limited to other biosignals (e.g.,
electrocardiogram, electroencephalogram, electrogastrogram,
electromyogram, heart rate signals, pathological sounds,
ultrasound, or any other suitable biosignal), dynamic signals,
non-destructive testing signals, condition monitoring signals,
fluid signals, geophysical signals, astronomical signals,
electrical signals, financial signals including financial indices,
sound and speech signals, chemical signals, meteorological signals
including climate signals, and/or any other suitable signal, and/or
any combination thereof
[0061] In one embodiment, a PPG signal may be transformed using a
continuous wavelet transform. Information derived from the
transform of the PPG signal (i.e., in wavelet space) may be used to
provide measurements of one or more physiological parameters.
[0062] The continuous wavelet transform of a signal x(t) in
accordance with the present disclosure may be defined as
T ( a , b ) = 1 a .intg. - .infin. + .infin. x ( t ) .psi. * ( t -
b a ) t ( 9 ) ##EQU00010##
where .psi.*(t) is the complex conjugate of the wavelet function
.psi.(t), a is the dilation parameter of the wavelet and b is the
location parameter of the wavelet. The transform given by equation
(9) may be used to construct a representation of a signal on a
transform surface. The transform may be regarded as a time-scale
representation. Wavelets are composed of a range of frequencies,
one of which may be denoted as the characteristic frequency of the
wavelet, where the characteristic frequency associated with the
wavelet is inversely proportional to the scale a. One example of a
characteristic frequency is the dominant frequency. Each scale of a
particular wavelet may have a different characteristic frequency.
The underlying mathematical detail required for the implementation
within a time-scale can be found, for example, in Paul S. Addison,
The Illustrated Wavelet Transform Handbook (Taylor & Francis
Group 2002), which is hereby incorporated by reference herein in
its entirety.
[0063] The continuous wavelet transform decomposes a signal using
wavelets, which are generally highly localized in time. The
continuous wavelet transform may provide a higher resolution
relative to discrete transforms, thus providing the ability to
garner more information from signals than typical frequency
transforms such as Fourier transforms (or any other spectral
techniques) or discrete wavelet transforms. Continuous wavelet
transforms allow for the use of a range of wavelets with scales
spanning the scales of interest of a signal such that small scale
signal components correlate well with the smaller scale wavelets
and thus manifest at high energies at smaller scales in the
transform. Likewise, large scale signal components correlate well
with the larger scale wavelets and thus manifest at high energies
at larger scales in the transform. Thus, components at different
scales may be separated and extracted in the wavelet transform
domain. Moreover, the use of a continuous range of wavelets in
scale and time position allows for a higher resolution transform
than is possible relative to discrete techniques.
[0064] In addition, transforms and operations that convert a signal
or any other type of data into a spectral (i.e., frequency) domain
necessarily create a series of frequency transform values in a
two-dimensional coordinate system where the two dimensions may be
frequency and, for example, amplitude. For example, any type of
Fourier transform would generate such a two-dimensional spectrum.
In contrast, wavelet transforms, such as continuous wavelet
transforms, are required to be defined in a three-dimensional
coordinate system and generate a surface with dimensions of time,
scale and, for example, amplitude. Hence, operations performed in a
spectral domain cannot be performed in the wavelet domain; instead
the wavelet surface must be transformed into a spectrum (i.e., by
performing an inverse wavelet transform to convert the wavelet
surface into the time domain and then performing a spectral
transform from the time domain). Conversely, operations performed
in the wavelet domain cannot be performed in the spectral domain;
instead a spectrum must first be transformed into a wavelet surface
(i.e., by performing an inverse spectral transform to convert the
spectral domain into the time domain and then performing a wavelet
transform from the time domain). Nor does a cross-section of the
three-dimensional wavelet surface along, for example, a particular
point in time equate to a frequency spectrum upon which
spectral-based techniques may be used. At least because wavelet
space includes a time dimension, spectral techniques and wavelet
techniques are not interchangeable. It will be understood that
converting a system that relies on spectral domain processing to
one that relies on wavelet space processing would require
significant and fundamental modifications to the system in order to
accommodate the wavelet space processing (e.g., to derive a
representative energy value for a signal or part of a signal
requires integrating twice, across time and scale, in the wavelet
domain while, conversely, one integration across frequency is
required to derive a representative energy value from a spectral
domain). As a further example, to reconstruct a temporal signal
requires integrating twice, across time and scale, in the wavelet
domain while, conversely, one integration across frequency is
required to derive a temporal signal from a spectral domain. It is
well known in the art that, in addition to or as an alternative to
amplitude, parameters such as energy density, modulus, phase, among
others may all be generated using such transforms and that these
parameters have distinctly different contexts and meanings when
defined in a two-dimensional frequency coordinate system rather
than a three-dimensional wavelet coordinate system. For example,
the phase of a Fourier system is calculated with respect to a
single origin for all frequencies while the phase for a wavelet
system is unfolded into two dimensions with respect to a wavelet's
location (often in time) and scale.
[0065] The energy density function of the wavelet transform, the
scalogram, is defined as
S(a,b)=|T(a,b)|.sup.2 (10)
where `.parallel.` is the modulus operator. The scalogram may be
rescaled for useful purposes.
[0066] One common rescaling is defined as
S R ( a , b ) = T ( a , b ) 2 a ( 11 ) ##EQU00011##
and is useful for defining ridges in wavelet space when, for
example, the Morlet wavelet is used. Ridges are defined as the
locus of points of local maxima in the plane. Any reasonable
definition of a ridge may be employed in the method. Also included
as a definition of a ridge herein are paths displaced from the
locus of the local maxima. A ridge associated with only the locus
of points of local maxima in the plane are labeled a "maxima
ridge".
[0067] For implementations requiring fast numerical computation,
the wavelet transform may be expressed as an approximation using
Fourier transforms. Pursuant to the convolution theorem, because
the wavelet transform is the cross-correlation of the signal with
the wavelet function, the wavelet transform may be approximated in
terms of an inverse FFT of the product of the Fourier transform of
the signal and the Fourier transform of the wavelet for each
required a scale and then multiplying the result by {square root
over (a)}.
[0068] In the discussion of the technology which follows herein,
the "scalogram" may be taken to include all suitable forms of
rescaling including, but not limited to, the original unscaled
wavelet representation, linear rescaling, any power of the modulus
of the wavelet transform, or any other suitable rescaling. In
addition, for purposes of clarity and conciseness, the term
"scalogram" shall be taken to mean the wavelet transform, T(a,b)
itself, or any part thereof. For example, the real part of the
wavelet transform, the imaginary part of the wavelet transform, the
phase of the wavelet transform, any other suitable part of the
wavelet transform, or any combination thereof is intended to be
conveyed by the term "scalogram".
[0069] A scale, which may be interpreted as a representative
temporal period, may be converted to a characteristic frequency of
the wavelet function. The characteristic frequency associated with
a wavelet of arbitrary a scale is given by
f = f c a ( 12 ) ##EQU00012##
where f.sub.C, the characteristic frequency of the mother wavelet
(i.e., at a=1), becomes a scaling constant and f is the
representative or characteristic frequency for the wavelet at
arbitrary scale a.
[0070] Any suitable wavelet function may be used in connection with
the present disclosure. One of the most commonly used complex
wavelets, the Morlet wavelet, is defined as:
.psi.(t)=.pi..sup.-1/4(e.sup.i2.pi.f.sup.0.sup.t-e.sup.-(2.pi.f.sup.0.su-
p.).sup.2.sup./2)e.sup.-t.sup.2.sup./2 (13)
where f.sub.0 is the central frequency of the mother wavelet. The
second term in the parenthesis is known as the correction term, as
it corrects for the non-zero mean of the complex sinusoid within
the Gaussian window. In practice, it becomes negligible for values
of f.sub.0>>0 and can be ignored, in which case, the Morlet
wavelet can be written in a simpler form as
.psi. ( t ) = 1 .pi. 1 / 4 2 .pi. f 0 t - t 2 / 2 ( 14 )
##EQU00013##
[0071] This wavelet is a complex wave within a scaled Gaussian
envelope. While both definitions of the Morlet wavelet are included
herein, the function of equation (14) is not strictly a wavelet as
it has a non-zero mean (i.e., the zero frequency term of its
corresponding energy spectrum is non-zero). However, it will be
recognized by those skilled in the art that equation (14) may be
used in practice with f.sub.0>>0 with minimal error and is
included (as well as other similar near wavelet functions) in the
definition of a wavelet herein. A more detailed overview of the
underlying wavelet theory, including the definition of a wavelet
function, can be found in the general literature. Discussed herein
is how wavelet transform features may be extracted from the wavelet
decomposition of signals. For example, wavelet decomposition of PPG
signals may be used to provide clinically useful information within
a medical device.
[0072] Pertinent repeating features in a signal give rise to a
time-scale band in wavelet space or a rescaled wavelet space. For
example, the pulse component of a PPG signal produces a dominant
band in wavelet space at or around the pulse frequency. FIGS. 3(a)
and (b) show two views of an illustrative scalogram derived from a
PPG signal, according to an embodiment. The figures show an example
of the band caused by the pulse component in such a signal. The
pulse band is located between the dashed lines in the plot of FIG.
3(a). The band is formed from a series of dominant coalescing
features across the scalogram. This can be clearly seen as a raised
band across the transform surface in FIG. 3(b) located within the
region of scales indicated by the arrow in the plot (corresponding
to 60 beats per minute). The maxima of this band with respect to
scale is the ridge. The locus of the ridge is shown as a black
curve on top of the band in FIG. 3(b). By employing a suitable
rescaling of the scalogram, such as that given in equation (11),
the ridges found in wavelet space may be related to the
instantaneous frequency of the signal. In this way, the pulse rate
may be obtained from the PPG signal. Instead of rescaling the
scalogram, a suitable predefined relationship between the scale
obtained from the ridge on the wavelet surface and the actual pulse
rate may also be used to determine the pulse rate.
[0073] By mapping the time-scale coordinates of the pulse ridge
onto the wavelet phase information gained through the wavelet
transform, individual pulses may be captured. In this way, both
times between individual pulses and the timing of components within
each pulse may be monitored and used to detect heart beat
anomalies, measure arterial system compliance, or perform any other
suitable calculations or diagnostics. Alternative definitions of a
ridge may be employed. Alternative relationships between the ridge
and the pulse frequency of occurrence may be employed.
[0074] As discussed above, pertinent repeating features in the
signal give rise to a time-scale band in wavelet space or a
rescaled wavelet space. For a periodic signal, this band remains at
a constant scale in the time-scale plane. For many real signals,
especially biological signals, the band may be non-stationary;
varying in scale, amplitude, or both over time. FIG. 3(c) shows an
illustrative schematic of a wavelet transform of a signal
containing two pertinent components leading to two bands in the
transform space, according to an embodiment. These bands are
labeled band A and band B on the three-dimensional schematic of the
wavelet surface. In this embodiment, the band ridge is defined as
the locus of the peak values of these bands with respect to scale.
For purposes of discussion, it may be assumed that band B contains
the signal information of interest. This will be referred to as the
"primary band". In addition, it may be assumed that the system from
which the signal originates, and from which the transform is
subsequently derived, exhibits some form of coupling between the
signal components in band A and band B. When noise or other
erroneous features are present in the signal with similar spectral
characteristics of the features of band B then the information
within band B can become ambiguous (i.e., obscured, fragmented or
missing). In this case, the ridge of band A may be followed in
wavelet space and extracted either as an amplitude signal or a
scale signal which will be referred to as the "ridge amplitude
perturbation" (RAP) signal and the "ridge scale perturbation" (RSP)
signal, respectively. The RAP and RSP signals may be extracted by
projecting the ridge onto the time-amplitude or time-scale planes,
respectively. The top plots of FIG. 3(d) show a schematic of the
RAP and RSP signals associated with ridge A in FIG. 3(c). Below
these RAP and RSP signals are schematics of a further wavelet
decomposition of these newly derived signals. This secondary
wavelet decomposition allows for information in the region of band
B in FIG. 3(c) to be made available as band C and band D. The
ridges of bands C and D may serve as instantaneous time-scale
characteristic measures of the signal components causing bands C
and D. This technique, which will be referred to herein as
secondary wavelet feature decoupling (SWFD), may allow information
concerning the nature of the signal components associated with the
underlying physical process causing the primary band B (FIG. 3(c))
to be extracted when band B itself is obscured in the presence of
noise or other erroneous signal features.
[0075] In some instances, an inverse continuous wavelet transform
may be desired, such as when modifications to a scalogram (or
modifications to the coefficients of a transformed signal) have
been made in order to, for example, remove artifacts. In one
embodiment, there is an inverse continuous wavelet transform which
allows the original signal to be recovered from its wavelet
transform by integrating over all scales and locations, a and
b:
x ( t ) = 1 C g .intg. - .infin. .infin. .intg. 0 .infin. T ( a , b
) 1 a .psi. ( t - b a ) a b a 2 ( 15 ) ##EQU00014##
which may also be written as:
x ( t ) = 1 C g .intg. - .infin. .infin. .intg. 0 .infin. T ( a , b
) .psi. a , b ( t ) a b a 2 ( 16 ) ##EQU00015##
where C.sub.g is a scalar value known as the admissibility
constant. It is wavelet type dependent and may be calculated
from:
C g = .intg. 0 .infin. .psi. ^ ( f ) 2 f f ( 17 ) ##EQU00016##
FIG. 3(e) is a flow chart of illustrative steps that may be taken
to perform an inverse continuous wavelet transform in accordance
with the above discussion. An approximation to the inverse
transform may be made by considering equation (15) to be a series
of convolutions across scales. It shall be understood that there is
no complex conjugate here, unlike for the cross correlations of the
forward transform. As well as integrating over all of a and b for
each time t, this equation may also take advantage of the
convolution theorem which allows the inverse wavelet transform to
be executed using a series of multiplications. FIG. 3(f) is a flow
chart of illustrative steps that may be taken to perform an
approximation of an inverse continuous wavelet transform. It will
be understood that any other suitable technique for performing an
inverse continuous wavelet transform may be used in accordance with
the present disclosure.
[0076] FIG. 4 is an illustrative continuous wavelet processing
system in accordance with an embodiment. In this embodiment, input
signal generator 410 generates an input signal 416. As illustrated,
input signal generator 410 may include oximeter 420 coupled to
sensor 418, which may provide as input signal 416, a PPG signal. It
will be understood that input signal generator 410 may include any
suitable signal source, signal generating data, signal generating
equipment, or any combination thereof to produce signal 416. Signal
416 may be any suitable signal or signals, such as, for example,
biosignals (e.g., electrocardiogram, electroencephalogram,
electrogastrogram, electromyogram, heart rate signals, pathological
sounds, ultrasound, or any other suitable biosignal), dynamic
signals, non-destructive testing signals, condition monitoring
signals, fluid signals, geophysical signals, astronomical signals,
electrical signals, financial signals including financial indices,
sound and speech signals, chemical signals, meteorological signals
including climate signals, and/or any other suitable signal, and/or
any combination thereof
[0077] In this embodiment, signal 416 may be coupled to processor
412. Processor 412 may be any suitable software, firmware, and/or
hardware, and/or combinations thereof for processing signal 416.
For example, processor 412 may include one or more hardware
processors (e.g., integrated circuits), one or more software
modules, computer-readable media such as memory, firmware, or any
combination thereof. Processor 412 may, for example, be a computer
or may be one or more chips (i.e., integrated circuits). Processor
412 may perform the calculations associated with the continuous
wavelet transforms of the present disclosure as well as the
calculations associated with any suitable interrogations of the
transforms. Processor 412 may perform any suitable signal
processing of signal 416 to filter signal 416, such as any suitable
band-pass filtering, adaptive filtering, closed-loop filtering,
and/or any other suitable filtering, and/or any combination
thereof.
[0078] Processor 412 may be coupled to one or more memory devices
(not shown) or incorporate one or more memory devices such as any
suitable volatile memory device (e.g., RAM, registers, etc.),
non-volatile memory device (e.g., ROM, EPROM, magnetic storage
device, optical storage device, flash memory, etc.), or both. The
memory may be used by processor 412 to, for example, store data
corresponding to a continuous wavelet transform of input signal
416, such as data representing a scalogram. In one embodiment, data
representing a scalogram may be stored in RAM or memory internal to
processor 412 as any suitable three-dimensional data structure such
as a three-dimensional array that represents the scalogram as
energy levels in a time-scale plane. Any other suitable data
structure may be used to store data representing a scalogram.
[0079] Processor 412 may be coupled to output 414. Output 414 may
be any suitable output device such as, for example, one or more
medical devices (e.g., a medical monitor that displays various
physiological parameters, a medical alarm, or any other suitable
medical device that either displays physiological parameters or
uses the output of processor 412 as an input), one or more display
devices (e.g., monitor, PDA, mobile phone, any other suitable
display device, or any combination thereof), one or more audio
devices, one or more memory devices (e.g., hard disk drive, flash
memory, RAM, optical disk, any other suitable memory device, or any
combination thereof), one or more printing devices, any other
suitable output device, or any combination thereof.
[0080] It will be understood that system 400 may be incorporated
into system 10 (FIGS. 1 and 2) in which, for example, input signal
generator 410 may be implemented as parts of sensor 12 and monitor
14 and processor 412 may be implemented as part of monitor 14.
[0081] FIG. 5 is an illustrative plot 500 of a respiration signal
505. Respiration signal 505 may indicate the breathing patterns of
a patient over time. Plot 500 displays time on the x-axis and
signal amplitude values of respiration signal 505 on the y-axis.
Plot 500 may be displayed using any suitable display device such
as, for example, monitor 20 (FIG. 1), display 28 (FIG. 1), a PDA, a
mobile device, or any other suitable display device. Additionally,
plot 500 may be displayed on multiple display devices.
[0082] Respiration signal 505 may be obtained using a sensor
capable of measuring the respiration of a patient, such as patient
40 (FIG. 2). For example, the respiration of a patient may be
measured using a flow meter or a chest band sensor. Respiration
signal 505 may also be derived from other biological signals (i.e.,
biosignals) captured by one or more sensors of a suitable biosignal
measurement system. For example, respiration signal 505 may be
derived from PPG signal data received from a pulse oximetry system
such as pulse oximetry system 10 (FIG. 1). Respiration signal 505
may also be derived from other biosignals including transthoracic
impedance signals, capnograph signals, nasal thermistor signals,
and/or electrocardiogram (EKG) signals. The derivation of
respiration signal 505 from a PPG signal or other suitable
biosignal will be described in more detail below. Although, the
techniques disclosed herein are described in terms of a respiration
signal derived from a PPG signal, the disclosed techniques may be
applied to any respiration signal or any other biosignals where
cyclic phenomena are captured by the measurement system.
[0083] Respiration signal 505 may exhibit an oscillatory behavior
versus time. The size, shape, and frequency of respiration signal
505 may be indicative of the breaths or breathing cycle of a
patient, such as patient 40 (FIG. 2), and/or may be used determine
the respiration rate of the patient. Respiration signal 505 may be
a processed version of a preliminary respiration signal obtained
from a sensor or derived from a suitable biosignal. The preliminary
respiration signal may contain erroneous or otherwise undesirable
artifacts due to, for example, patient movement, equipment failure,
and/or various noise sources. For example, cable 24, cable 32,
and/or cable 34 (all of FIG. 1) may malfunction or become loosened
from the equipment to which it is connected. Further, sensor 12
(FIG. 1), or any constituent component of sensor 12 (FIG. 1) (for
example, emitter 16 (FIG. 1) and/or detector 18 (FIG. 1)) may
malfunction and/or become loosened. Additionally, noise sources may
produce inconsistent features in a PPG signal or other biosignal
from which respiration signal 505 was derived. Possible sources of
noise include thermal noise, shot noise, flicker noise, burst
noise, and/or electrical noise caused by light pollution. These and
other noise sources may be introduced, for example, through sensor
12 (FIG. 1), and/or cables 24, 32, and 34 (all of FIG. 1). These
and/or other phenomena may be present in a system such as pulse
oximetry system 10 (FIG. 1), and thus may introduce inconsistent
features into the measured PPG signal and in turn may introduce
inconsistent features into respiration signal 505.
[0084] As shown in plot 500, respiration signal 505 may be
substantially free of these erroneous and otherwise undesirable
artifacts. The effect of these artifacts on a respiration signal
may be reduced or eliminated by processing the underlying biosignal
(e.g., a PPG signal) from which respiration signal 505 is derived,
by the processing techniques used to derive respiration signal 505
from the biosignal and/or by processing a preliminary respiration
signal to obtain respiration signal 505. Each of these processing
steps may be implemented in a pulse oximetry system such as pulse
oximetry system 10 (FIG. 1) and may be carried out using a
processor such as processor 412 (FIG. 4) or microprocessor 48 (FIG.
2). However, even when these artifacts are reduced or eliminated,
respiration signal 505 may still contain respiratory features
(e.g., signal peaks) having a wide range of amplitude values. It
may be advantageous to reduce the range of these amplitude values
in respiration signal 505 in order to improve the interpretation
and subsequent analysis of this signal and/or to obtain additional
respiration parameters such as respiration rate. For example, one
or more large signal peaks in respiration signal 505 may adversely
effect the respiration rate determined from the signal.
[0085] Plot 500 of FIG. 5 includes upper threshold 510 to reduce
the amplitude variations in respiration signal 505. Signal peaks
having amplitude values that are above upper threshold 510 may be
reduced. These signal peaks may be reduced to amplitude values that
are closer to the threshold value, closer to a mean or median
signal peak value for respiration signal 505 or closer to another
predetermined value. For example, signal peaks 511, 512, 513, and
514 all have amplitude values that exceed upper threshold 510.
These signal peaks may therefore be reduced to the values of
adjusted signal peaks 511a, 512a, 513a, and 514a, which may be
substantially equal to the value of upper threshold 510.
[0086] FIG. 6 is an illustrative plot 600 of a respiration signal
605 which is similar to plot 500 of FIG. 5 and includes additional,
lower threshold 620. Signal peaks 611, 612, 613, and 614 all have
amplitude values that exceed upper threshold 610 and may therefore
be reduced to the values of adjusted signal peaks 611a, 612a, 613a,
and 614a. Additionally or alternatively, signal peaks 621, 622,
623, and 624 all have amplitude values that are less than upper
threshold 610 and that exceed lower threshold 620. The amplitude
values of these signal peaks may be increased to the values of
adjusted signal peaks 621a, 622a, 623a, and 624a. According to this
example, amplitude values of signal peaks that exceed upper
threshold 610 are reduced and signal peaks having values between
lower threshold 620 and upper threshold 610 are be increased. In
this manner, signal peaks having values both greater than and less
than the value of upper threshold 610 may be adjusted closer to a
single amplitude value, i.e., the value of upper threshold 610.
Signal peaks having amplitude values that are less than lower
threshold 620 may remain unchanged to prevent erroneously small
features from being increased in amplitude. In another example,
upper and lower signal threshold values may be set such that signal
peak values that exceed an upper threshold value or that are less
than a lower threshold value may be adjusted closer to a value
between the two threshold values (e.g., a mean value). Signals peak
values that are between these two threshold values may remain
unchanged. Additionally, a third, minimum threshold value may
prevent erroneously small features from being increased in
amplitude.
[0087] Process 700 (depicted in FIG. 7) illustrates exemplary
techniques for reducing amplitude variations in respiration signals
505 and 605 by normalizing the peak values of these signals based
on one or more threshold values. Normalizing signal peaks within
respiration signals 505 and 605 may reduce the amplitude variations
of these respiration signal may improve and/or simplify the
subsequent processing of these respiration signals. For example,
normalizing signal peaks within respiration signals 505 and 605 may
aid in the determination of respiration rate information from these
signals.
[0088] FIG. 7 depicts an illustrative process 700 for normalizing
respiratory feature amplitude values of a respiration signal (or
parts of a respiration signal), e.g., respiration signal 505 (FIG.
5) or respiration signal 605 (FIG. 6). Process 700 may be
implemented in a pulse oximetry system such as pulse oximetry
system 10 (FIG. 1), and the steps of process 700 may be carried out
using a processor such as processor 412 (FIG. 4) or microprocessor
48 (FIG. 2).
[0089] Process 700 may start at step 710. At step 720, process 700
may obtain a respiration signal. The respiration signal obtained in
step 720 may be obtained using a sensor capable of measuring the
respiration of a patient, such as patient 40 (FIG. 2). For example,
the respiration of a patient may be measured using a flow meter or
a chest band sensor. The respiration signal obtained in step 720
may also be derived from other biological signals (i.e.,
biosignals) captured by one or more sensors of a suitable biosignal
measurement system. For example, respiration signal 505 may be
derived from PPG signal data received from a pulse oximetry system
such as pulse oximetry system 10 (FIG. 1) using a sensor such as
sensor 12 (FIG. 1) to measure biological characteristics of a
patient such as patient 40 (FIG. 2). Respiration signal 505 may
also be derived from other biosignals including transthoracic
impedance signals, capnograph signals, nasal thermistor signals,
and/or electrocardiogram (EKG) signals. The respiration signal
and/or one or more signals that may be used to derive the
respiration signal may be real-time signals or may be signals
previously received and stored in memory, for example, ROM 52 (FIG.
2) or RAM 54 (FIG. 2).
[0090] In an embodiment, the respiration signal obtained at step
720 may be derived from a PPG signal. The PPG signal may be
obtained by processing another, preliminary PPG signal. For
example, a preliminary PPG signals may be obtained using, e.g.,
sensor 12 (FIG. 1) and processed using a processor such as
processor 412 (FIG. 4) or microprocessor 48 (FIG. 2) in a system
similar or identical to pulse oximetry system 10 (FIG. 1). For
example, the preliminary signal may be processed using low-pass
filters, noise-component removal techniques, and/or interpolation
methods, that may remove various undesirable artifacts that may be
present in the preliminary signal. As another example, one or more
preliminary PPG signals may be selected and mirrored to create the
PPG signal used to derive a reparation signal using techniques
similar or identical to those described in Watson, U.S. Provisional
Application No. 61/077,092, filed Jun. 30, 2008, entitled "Systems
and Method for Detecting Pulses," and McGonigle et al., U.S.
application Ser. No. 12/437,317, filed May 7, 2009, entitled
"Concatenated Scalograms," which are incorporated by reference
herein in their entirety. As yet another example, a preliminary PPG
signal may be analyzed to calculate regions having at least a
threshold level of stability and/or consistency using techniques
similar or identical to those described in Watson et al., U.S.
application Ser. No. 12/437,326, filed May 7, 2009--entitled "
Consistent Signal Selection By Signal Segment Selection
Techniques," which is incorporated by reference herein in its
entirety.
[0091] The respiration signal obtained in step 720 may be derived
from a PPG signal by generating a scalogram from a received PPG
signal. For example, a scalogram may be derived using the same
method (e.g., using continuous wavelet transforms) that was used to
derive the scalograms shown in FIGS. 3(a), 3(b), and 3(c). The
scalogram of the wavelet transform may be generated or otherwise
obtained using, for example a processor such as processor 412 (FIG.
4) or microprocessor 48 (FIG. 2). In addition to the scalogram,
other parts of the wavelet transform may be determined. For
example, the transform modulus, phase, real, and/or imaginary parts
may be generated in addition to the scalogram.
[0092] The resultant scalogram may include bands and ridges
corresponding to at least one area of increased energy. A
respiration band of the scalogram may generally reflect the
breathing pattern of a patient, e.g., patient 40 (FIG. 2). These
bands may be extracted from the scalogram using, for example, a
processor such as processor 412 (FIG. 4) or microprocessor 48 (FIG.
2), using any suitable method. The respiration band of the
scalogram may be identified using characteristics of the scalogram
including the energy and structure of the scalogram, and the
signal-to-noise levels in various regions of scalogram. In one
embodiment, this information may be calculated one or more times
using different time-window sizes. The number and type of
time-window sizes that are used may depend on the anticipated
respiration rate, the available computational resources (e.g., the
amount of ROM 52 (FIG. 2) and/or RAM 54 (FIG. 2) and the speed of
processor 412 (FIG. 4) and/or microprocessor 48 (FIG. 2)), as well
as on possible input derived from user inputs 56 (FIG. 2).
[0093] The respiration signal may be derived from the amplitude
and/or scale modulation observed in the respiration band (e.g.,
respiration band B in FIG. 3(c)). The respiration signal may also
may be derived after further analysis of the scalogram including,
for example, secondary wavelet feature decoupling. This secondary
wavelet feature decoupling of a ridge allows for information
concerning the band of interest (e.g., respiration band B in FIG.
3(c)) to be made available as secondary bands (e.g., band C and
band D in FIG. 3(d)). The ridges of the secondary bands may serve
as instantaneous time-scale characteristic measures of the
underlying signal components causing the secondary bands, which may
be useful in analyzing the signal component associated with the
underlying physical process causing the primary band of interest
(e.g., the respiration band B) when band B itself may be obscured.
By extracting and further analyzing a respiration band in the
scalogram, a respiration signal may be extracted from the scalogram
when the respiration band itself is, for example, obscured in the
presence of noise or other erroneous signal features.
[0094] At step 730 signal peaks may be identified from the
respiration signal obtained in step 720. Signal peaks may be found,
e.g., using any suitable signal processing technique, including a
zero-crossing technique, a root-finding technique, an analytic
curve-fitting technique, and/or a numerical analysis of the
derivatives of the selected portion of the signal. These and other
techniques may be implemented in pulse oximetry system 10 (FIG. 1)
by processor 412 (FIG. 4), microprocessor 48 (FIG. 2), ROM 52 (FIG.
2), and/or RAM 54 (FIG. 2). Additionally, the parameters that may
be used by suitable signal processing techniques, e.g,. tolerance
values and sensitivity levels, may be controlled by a user or
patient using, e.g., using user inputs 56 (FIG. 2). Signal peaks
that are identified may be displayed, for example, on monitor 26
(FIG. 1) or display 20 or 28 (both of FIG. 1). Alternatively, a
portion of the respiration signal generated at step 730 may be
displayed on a monitor, and a user may choose or otherwise
influence which peaks are selected using, for example, user inputs
56 (FIG. 2).
[0095] At step 740 one or more signal peak thresholds may be
selected or determined. Signal peak thresholds may calculated using
any suitable signal processing and analysis techniques. For
example, signal peak thresholds may be related to a mean, median,
mode, range, standard deviation, or percentile of the signal peaks
identified at step 730. Signal peak threshold values may be
determined based on an initial set of signal peak values. Signal
peak thresholds may then be replaced or updated periodically or
continuously based on newer incoming signal peak values.
Alternatively, signal peak thresholds may be set to predetermined
values based on historical or idealized respiration signal data or
based on any other suitable data. These and other techniques may be
implemented in pulse oximetry system 10 (FIG. 1) by processor 412
(FIG. 4), microprocessor 48 (FIG. 2), ROM 52 (FIG. 2), and/or RAM
54 (FIG. 2). Additionally, the parameters that may be used by
suitable signal processing techniques, e.g,. tolerance values and
sensitivity levels, may be controlled by a user or patient using,
e.g., using user inputs 56 (FIG. 2). Signal peak thresholds may be
displayed, for example, on monitor 26 (FIG. 1) or display 20 or 28
(both of FIG. 1). Alternatively, the portion of the respiration
signal obtained in step 720 may be displayed on a monitor, and a
user may choose or otherwise influence signal peak thresholds
using, for example, user inputs 56 (FIG. 2).
[0096] Illustrative plot 500 (FIG. 5) includes a single, upper
threshold 510. Signal peaks that exceed the upper threshold value
may be reduced. Illustrative plot 600 (FIG. 6) includes an
additional, lower threshold 620. Signal peaks that exceed the lower
threshold value may be increased, but signal peaks that have
amplitudes below the lower threshold may be left unchanged. A
minimum threshold (not illustrated) may reduce or eliminate signal
peaks that have amplitudes below the minimum threshold values or
may prevent signal peaks below this minimum threshold from being
modified. Other threshold types may also be provided. The number
and type of signal peak thresholds used to normalize respiration
features within a respiration signal may be determined by processor
412 (FIG. 4), microprocessor 48 (FIG. 2) based on any suitable
signal processing and analysis techniques. For example, the
particular type of signal peak thresholds to be used may be
determined based on the respiration signal to be processed.
Additionally, the number and type of signal peak thresholds used to
process a respiration signal may be controlled by a user or patient
using, e.g., using user inputs 56 (FIG. 2). One or more signal peak
thresholds may be displayed, for example, on monitor 26 (FIG. 1) or
display 20 or 28 (both of FIG. 1) and the user may choose or
otherwise the number and type of signal peak thresholds using, for
example, user inputs 56 (FIG. 2).
[0097] At step 750, one or more the signal peaks identified in step
730 may be adjusted based on the signal peak thresholds determined
in step 740. The signal adjustment may be performed by a processor
such as processor 412 (FIG. 4) or microprocessor 48 (FIG. 2). For
example, signal peaks that exceed an upper threshold value may be
reduced in value and/or signal peaks that exceed a lower threshold
may be increased in value. These adjustments may be used to provide
a normalized respiration signal. Alternatively or additionally, the
adjustments may be made within the one or more scalograms used to
generate the original respiration signal. The adjusted scalograms
may be processed further to determine or estimate additional
information. For example, two or more scalograms having adjusted
respiratory features may be concatenated together and processed to
improve the computation of information such as respiration
information using techniques similar or identical to those
described in McGonigle et al., U.S. application Ser. No.
12/437,317, filed May 7, 2009, entitled "Concatenated Scalograms,"
which was previously incorporated by reference herein.
[0098] One approach for modifying the value of an identified
respiration signal peak is to linearly rescale a signal segment
associated with the signal peak. Referring to respiratory signal
505 (FIG. 5), signal peak 511 exceeds upper threshold 510.
Therefore, a signal segment defined by the zero crossing (or any
other suitable points) before and after signal peak 511, i.e.,
points 511b and 511c, may be rescaled by a constant factor (less
than unity). In an embodiment, the constant factor may be set to a
value such that the adjusted signal peak (e.g., 511a) for a given
signal peak (e.g., 511a) is less than or equal to the signal peak
threshold value or any other suitable value (e.g., a mean value).
This value may be set such that all adjusted peak values will be
similar. Alternatively, the same constant factor may be used
irrespective of the actual signal peak value. In an embodiment,
only the portion of the signal that crosses a threshold may be
rescaled. For example, referring to respiratory signal 505 (FIG. 5)
and according to this embodiment, only the respiration signal
segment between signal points 511d and 511e may be adjusted. In an
embodiment, a nonlinear rescaling value may be used whereby the
change in value of a respiration signal segment associated with a
signal peak may be related in some way to the distance between the
signal peak value and an average value of the signal. Nonlinear
rescaling values may also be related to a distance between a signal
peak and, for example, the threshold value, a desired value, or
another predetermined value. The nonlinear relationship may be
smoothly nonlinear or may be made up of discreet linear scaling
factor values.
[0099] At step 760 a respiration parameter may be generated based
on the normalized respiration signal adjusted in step 750. For
example, a respiration rate may be determined or estimated from the
adjusted respiration signal using any suitable approach. The
respiration rate may be represented by a number from 1 to 100,
where a larger number indicates a larger respiration rate (any
other suitable number range could be used instead). The
determination of the respiration rate may be performed, for
example, by processor 412 (FIG. 4) or microprocessor 48 (FIG. 2),
and may additionally depend on parameters entered by a user through
user inputs 56 (FIG. 2). To estimate a respiration rate the
processor may use, for example, maximum-likelihood techniques to
combine data when the prior probability of a given respiration rate
is known, and Neyman-Pearson combining techniques may be used when
the prior probability of a given respiration rate is unknown.
[0100] At step 770 the respiration parameter determined or
estimated from the respiration signal in step 760 may be reported.
For example, a respiration rate may be reported by generating an
audible alert or, for example, using speaker 22 (FIG. 2) as well as
possibly through other audio devices, generating an on-screen
message, for example, on display 20 (FIG. 1) or display 28 (FIG.
1), generating a pager message, a text message, or a telephone
call, for example, using a wireless connection embedded or attached
to a system such as system 10 (FIG. 1), activating a secondary or
backup sensor or sensor array, for example, connected through a
wire or wirelessly to monitor 14 (FIG. 1), or regulating the
automatic administration medicine, for example, which is controlled
in part or fully through a system such as system 10 (FIG. 1).
Additionally, the respiration rate may be reported on a display
such as display 20 (FIG. 1) or display 28 (FIG. 1) in graphical
form using, for example, a bar graph or histogram. The respiration
parameter may also be reported to one or more other processes, for
example, to be used as part of or to improve the reliability of
other measurements or calculations within a system such as pulse
oximetry system 10 (FIG. 1).
[0101] FIG. 8 depicts an illustrative process for adjusting one or
more signal peaks in a signal, e.g., respiration signal 505 (FIG.
5), in accordance with some embodiments. Process 800 may be
implemented in a pulse oximetry system such as pulse oximetry
system 10 (FIG. 1), and the steps of process 800 may be carried out
using a processor such as processor 412 (FIG. 4) or microprocessor
48 (FIG. 2). Process 800 may correspond to a further embodiment of
process 700, and more particularly, may correspond to a further
embodiment of step 750 of FIG. 7. Process 800 may start at step
810. At step 810, a first signal peak is selected. For example, at
step 810, process 800 may select one of the signal peaks of a
respiration signal identified by process 700 (FIG. 7) at step 730.
The first signal peak may correspond to the first-occurring signal
peak in time, e.g. signal peak 511 (FIG. 5) of respiration signal
505, and/or it may correspond to the first signal peak found
through a suitable signal processing algorithm, such as an
extrema-finding algorithm. Once the location of a first peak has
been found, at step 810 an amplitude value of the first signal peak
may be determined.
[0102] At step 830, it is determined whether the signal peak
crosses a threshold value. The value of the signal peak may be
compared to one or more signal peak threshold values determined by
process 700 (FIG. 7) at step 750. For example, for respiration
signal 505 (FIG. 5) it may be determined that signal peak 511
exceeds threshold 510. As another example, for respiration signal
605 (FIG. 6) it may be determined that signal peak 621 exceeds
threshold 620. Signal peak 611 exceeds both thresholds 610 and 620.
In this instance, only the higher threshold (i.e., threshold 610)
is considered. If the signal peak does not cross any threshold
values, the next signal peak is selected at step 840 and process
800 continues until there are no more signal peaks.
[0103] If it is determined that the signal peak crosses a threshold
value, at step 850 a portion of the signal surrounding the signal
peak may be selected. For example, signal peak 511 (FIG. 5) of
respiration signal 505 exceeds the value of signal peak threshold
510. As described above, the signal segment defined by the zero
crossing before and after signal peak 511, i.e., points 511b and
511c, may be selected. Alternatively, the signal segment defined by
the threshold crossing before and after signal peak 511, i.e.,
points 511d and 511e, may be selected. Any other suitable portion
of the signal between the selected signal peak and adjacent signal
peaks may also be selected. At step 860 the selected portion of the
signal may be adjusted using linear or nonlinear scaling
techniques, as described above. Finally, the next signal peak is
selected at step 840 and process 800 continues until there are no
more signal peaks.
[0104] FIG. 9 depicts an additional illustrative process for
generating a normalized respiration signal from a scalogram 910.
Scalogram 910 of the wavelet transform may be generated or
otherwise obtained at least in part from a received PPG signal
using, for example a processor such as processor 412 (FIG. 4) or
microprocessor 48 (FIG. 2). Scalogram 910 includes wavelet phase
information from a received PPG signal in the region of the feature
scales in wavelet space. Similar to the respiration ridge within
respiration band B in the scalogram illustrated FIG. 3(c) and the
respiration ridge within a secondary bands C and D in FIG. 3(d),
which represent amplitude and/or scale modulation relating to
respiration features as a function of time, ridge location 920
includes local phase values relating to respiration features as a
function of time. A sinusoidal function indicative of respiration
phase and having normalized height values may be generated from
these local phase values by taking the sine or cosine of these
values. Plot 930 is an illustrative cosine signal of wavelet phase
values along ridge location 920. Alternatively, an inverse wavelet
transform may be performed on the local transform phase values
along ridge location 920 to generate a normalized respiration
signal.
[0105] It will also be understood that the above method may be
implemented using any human-readable or machine-readable
instructions on any suitable system or apparatus, such as those
described herein.
[0106] The foregoing is merely illustrative of the principles of
this disclosure and various modifications may be made by those
skilled in the art without departing from the scope and spirit of
the disclosure. The following claims may also describe various
aspects of this disclosure.
* * * * *