U.S. patent application number 17/128926 was filed with the patent office on 2021-04-15 for method, apparatus, and system for determining a value of one or more parameters of a respiratory effort of a subject.
The applicant listed for this patent is NOX MEDICAL. Invention is credited to Jon Skirnir AGUSTSSON, Sveinbjorn HOSKULDSSON.
Application Number | 20210106258 17/128926 |
Document ID | / |
Family ID | 1000005291874 |
Filed Date | 2021-04-15 |
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United States Patent
Application |
20210106258 |
Kind Code |
A1 |
HOSKULDSSON; Sveinbjorn ; et
al. |
April 15, 2021 |
METHOD, APPARATUS, AND SYSTEM FOR DETERMINING A VALUE OF ONE OR
MORE PARAMETERS OF A RESPIRATORY EFFORT OF A SUBJECT
Abstract
A method, apparatus, and system for determining a value of one
or more parameters of a respiratory effort of a subject, including
obtaining a thoracic signal (T), the thoracic signal (T) being an
indicator of a thoracic component of the respiratory effort,
obtaining an abdomen signal (A), the abdomen signal (A) being an
indicator of an abdominal component of the respiratory effort, and
determining, without directly measuring, the value of the one or
more parameters of the respiratory effort by using constraints
and/or relationships of components of a model of a respiratory
system of the subject, and fitting the components of the model of
the respiratory system of the subject with data from the obtained
thoracic signal (T) and data from the obtained abdomen signal
(A).
Inventors: |
HOSKULDSSON; Sveinbjorn;
(Reykjavik, IS) ; AGUSTSSON; Jon Skirnir;
(Reykjavik, IS) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NOX MEDICAL |
Reykjavik |
|
IS |
|
|
Family ID: |
1000005291874 |
Appl. No.: |
17/128926 |
Filed: |
December 21, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15680910 |
Aug 18, 2017 |
10869619 |
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17128926 |
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62377258 |
Aug 19, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/087 20130101;
A61B 5/1135 20130101; A61B 5/6823 20130101; A61B 5/6831 20130101;
A61B 5/7278 20130101; A61B 5/0806 20130101 |
International
Class: |
A61B 5/113 20060101
A61B005/113; A61B 5/08 20060101 A61B005/08; A61B 5/087 20060101
A61B005/087; A61B 5/00 20060101 A61B005/00 |
Claims
1. A method of determining a value of one or more parameters of a
respiratory effort of a subject, the method comprising: obtaining a
thoracic signal (T), the thoracic signal (T) being an indicator of
a thoracic component of the respiratory effort; obtaining an
abdomen signal (A), the abdomen signal (A) being an indicator of an
abdominal component of the respiratory effort; and determining,
without directly measuring, the value of the one or more parameters
of the respiratory effort by using constraints and/or relationships
of components of a model of a respiratory system of the subject,
and fitting the components of the model of the respiratory system
of the subject with data from the obtained thoracic signal (T) and
data from the obtained abdomen signal (A).
2. The method according to claim 1, wherein the thoracic signal (T)
is a thoracic respiratory inductive plethysmograph (RIP) signal,
and the abdomen signal (A) is an abdomen respiratory inductive
plethysmograph (RIP) signal; and wherein each of the determined one
or more parameters of the respiratory effort is different than each
of the thoracic component of the respiratory effort, the abdominal
component of the respiratory effort, a weighted thoracic component
of the respiratory effort, a weighted abdominal component of the
respiratory effort, a paradox component of the respiratory effort,
and a respiratory movement.
3. The method according to claim 1, wherein the one or more
parameters include one or a combination of: an airway resistance Rr
of the subject; an intercostal muscle drive force FTm; a diaphragm
drive force FAm; a thorax counterforce FTb based on a force of
lifting the chest weight and/or stretching muscle and skin of the
chest; an abdomen counterforce FAb based on a force created by
organ hydraulic pressure from the abdomen of the subject; an
intrathoracic pressure PIt; a ratio of inhalation time over
exhalation time; a ratio of thoracic inhalation time over thoracic
exhalation time; a ratio of abdomen inhalation time over abdomen
exhalation time; a ratio of total inhalation time over total
exhalation time; a body pressure based on differing body positions
of the subject; a sleep stage of the subject; an abdomen inhalation
time; an abdomen exhalation time Aet; a thoracic inhalation time; a
thoracic exhalation time Tet; a thoracic negative pressure force
FTp; an abdomen negative pressure force FAp; respiratory drive;
changes in respiratory drive; a thorax contribution to respiration;
changes in the thorax contribution to respiration; an abdomen
exhalation time constant; changes in the abdomen exhalation time
constant; a thoracic exhalation time constant; changes in a
thoracic exhalation time constant; intra thoracic pressure; changes
in the intrathoracic pressure; a comparison of a thoracic
exhalation time Tet and an abdomen exhalation time Aet; a ratio of
inhalation time versus exhalation time; or a thoracic mass
component or abdomen mass component of the subject.
4. The method according to claim 3, wherein the one or more
parameters includes at least the thoracic negative pressure force
FTp.
5. The method according to claim 4, wherein the thoracic negative
pressure force FTp is based on an additional force caused by an
intrathoracic pressure PIt across a thoracic area (At), such that
FTp=PIt*At.
6. The method according to claim 3, wherein the one or more
parameters includes at least the abdomen negative pressure force
FAp.
7. The method according to claim 6, wherein the abdomen negative
pressure force FAp is based on an additional force caused by an
intrathoracic pressure PIt across an Abdomen area (Aa), such that
FAp=PIt*Aa.
8. The method according to claim 7, wherein low airway resistance
Rr of the subject results in low thoracic negative pressure force
FTp and/or low abdomen negative pressure force FAp, or high airway
resistance Rr of the subject results in high thoracic negative
pressure force FTp and/or high abdomen negative pressure force
FAp.
9. The method according to claim 7, wherein low airway resistance
Rr indicates that an upper airway resistance is low.
10. The method according to claim 1, wherein the thoracic signal
(T) and abdomen signal (A) are obtained by a Respiratory Inductive
Plethysmograph (RIP) system, including a first stretchable belt
including a first conductor formed therein, the first stretchable
belt being arranged at a thoracic region of the subject, and a
second stretchable belt including a second conductor formed
therein, the second stretchable belt being arranged at an abdomen
of the subject, and a processing unit configured to obtain the
thoracic signal (T) as a first inductive signal from the first
conductor and the processing unit is configured to obtain the
abdomen signal (A) as a second inductive signal from the second
conductor.
11. The method according to claim 1, wherein any one or more of
intercostal muscle drive force FTm, a thorax counterforce FTb, a
diaphragm drive force FAm, an abdomen counterforce FAb are
determined based on a Respiratory Inductive Plethysmograph (RIP)
system, including a first stretchable belt including a first
conductor formed therein, the first stretchable belt being arranged
at a thoracic region of the subject, and a second stretchable belt
including a second conductor formed therein, the second stretchable
belt being arranged at an abdomen of the subject, and a processing
unit configured to obtain the thoracic signal (T) as a first
inductive signal from the first conductor and the processing unit
is configured to obtain the abdomen signal (A) as a second
inductive signal from the second conductor.
12. The method according to claim 1, wherein any one or more of
intercostal muscle drive force FTm, a thorax counterforce FTb, a
diaphragm drive force FAm, an abdomen counterforce FAb are
determined based on an evaluation of one or more inhalations.
13. The method according to claim 1, wherein any one or more of
intercostal muscle drive force FTm, a thorax counterforce FTb, a
diaphragm drive force FAm, an abdomen counterforce FAb are
determined based on an evaluation of one or more exhalations.
14. The method according to claim 1, wherein any one or more of
intercostal muscle drive force FTm, a thorax counterforce FTb, a
diaphragm drive force FAm, an abdomen counterforce FAb are
determined based on an evaluation of one or more respiration
times.
15. The method according to claim 1, wherein any one or more of
intercostal muscle drive force FTm, a thorax counterforce FTb, a
diaphragm drive force FAm, an abdomen counterforce FAb are
determined based on an evaluation of one or more shape deviations
between the thorax signal (T) and the abdomen signal (A).
16. The method according to claim 1, wherein any one or more of
intercostal muscle drive force FTm, a thorax counterforce FTb, a
diaphragm drive force FAm, an abdomen counterforce FAb are
determined based on an evaluation of one or more phase shifts
between the thorax signal (T) and the abdomen signal (A).
17. The method according to claim 1, wherein the determining of the
value of the one or more parameters of the respiratory effort is
further based on a ratio of inhalation time versus exhalation time,
and wherein a determination of intrathoracic pressure is determined
based on a ratio of inhalation time versus exhalation time, where
it is assumed that PIt becomes more negative the longer it takes to
fill a respiratory capacity compared with an exhalation time.
18. The method according to claim 1, wherein determining, without
directly measuring the value of the one or more parameters of the
respiratory effort includes determining, without directly
measuring, a weighted sum (S) of the thoracic signal (T) and the
abdomen signal (A) that correctly represents the respiratory volume
by the relative contribution of the thoracic signal (T) and the
abdomen signal (A) to two or more harmonics of the weighted sum
(S).
19. A system for determining a value of one or more parameters of a
respiratory effort of a subject, the system comprising: a first
sensor device configured to obtain a thorax signal (T), the thorax
signal (T) being an indicator of a thoracic component of the
respiratory effort; a second sensor device configured to obtain an
abdomen signal (A), the abdomen signal (A) being an indicator of an
abdominal component of the respiratory effort; a processor
configured to receive the thorax signal (T) and the abdomen signal
(A); wherein the processor further is configured to receive the
thorax signal (T), receive an abdomen signal (A), and determine,
without directly measuring, the value of the one or more parameters
of the respiratory effort by using constraints and/or relationships
of components of a model of a respiratory system of the subject,
and fitting the components of the model of the respiratory system
of the subject with data from the obtained thoracic signal (T) and
data from the obtained abdomen signal (A).
20. A hardware storage device having stored thereon computer
executable instructions which, when executed by one or more
processors, implement a method of determining a value of one or
more parameters of a respiratory effort of a subject, the method
comprising: obtaining a thoracic signal (T), the thoracic signal
(T) being an indicator of a thoracic component of the respiratory
effort; obtaining an abdomen signal (A), the abdomen signal (A)
being an indicator of an abdominal component of the respiratory
effort; and determining, without directly measuring, the value of
the one or more parameters of the respiratory effort by using
constraints and/or relationships of components of a model of a
respiratory system of the subject, and fitting the components of
the model of the respiratory system of the subject with data from
the obtained thoracic signal (T) and data from the obtained abdomen
signal (A).
Description
FIELD OF THE DISCLOSURE
[0001] The present disclosure relates to a method, apparatus, and
system for measuring respiratory effort of a subject, and to a
method, apparatus, and system for calculating a calibration factor
for calibrating signals representative of the respiratory effort of
a subject.
BACKGROUND
[0002] Respiratory effort is a term used for indirect measures of
the power needed to drive the respiratory airflow. The gold
standard measure that has been used is an esophageal pressure
measurement, measuring the relative pressure difference over the
upper airway. To inhale, this pressure must be negative compared
with the atmospheric pressure. The diaphragm and rib muscles drive
the respiration and the lower that the relative esophageal pressure
gets, the more tension is provided by the muscles and more energy
is used for respiration.
[0003] As measuring of esophageal pressure requires the placement
of a catheter or sensor inside the esophageal area, it is an
invasive procedure and is not practical for general respiratory
measures. Non-invasive methods are useful and popular to measure
breathing movements, but these methods do not measure respiratory
effort directly. A non-invasive method to measure respiratory
effort would be particularly helpful.
[0004] For this reason, indirect methods have been developed to
evaluate the respiratory effort. One is to use respiratory effort
bands or belts, where a sensor belt capable of measuring either
changes in the band stretching or the area of and encircled body is
placed around the patients' body in one or more places. Typically
one belt is placed around the thorax and a second belt is placed
around the abdomen to capture respiratory movements caused by both
the diaphragm and the rib-muscles. When sensors measuring only the
stretching of the belts are used, the resulting signal is a
qualitative measure of the respiratory movement. This type of
measurement is used, for example, for measuring of sleep disordered
breathing and is intended to distinguish between reduced
respiration caused by obstruction in the upper airway (obstructive
apnea) where there can be considerable respiratory movement
measured, or if it is caused by reduced effort (central apnea)
where reduction in flow and reduction in the belt movement occur at
the same time.
[0005] Even if the methods (esophageal pressure measurement vs.
respiration movement measurement) differ significantly regarding
the signals and units being measured, both are linked indirectly to
the actual power used for respiration that is the desired
parameter.
[0006] Unlike the stretch-sensitive respiratory effort belts, the
areal sensitive respiratory effort belts provide detailed
information on the actual form, shape, and amplitude of the
respiration taking place. If the areal changes of both the thorax
and abdomen are known, by using a certain calibration technology,
the continuous respiratory volume can be measured from those
signals and therefore the respiratory flow can be derived.
[0007] The most practical and widely used method to measure
respiratory related areal changes is called Respiratory Inductive
Plethysmograph or RIP and is explained in details below. RIP
technology has been defined as the gold standard respiratory effort
signal for accredited Sleep Clinics in the United States for their
accuracy, reliability and ease of use.
[0008] Respiratory Inductive Plethysmography (RIP) includes the use
of respiratory bands to measure respiratory effort related areal
changes. RIP technology includes a measurement of an inductance of
a conductive belt or belts that encircles a respiratory region of a
subject.
[0009] The signal amplitude received from the respiratory effort
belts depends on both the shape of the subject and the placement of
the belts. To create a respiration volume signal by summing the
signal of the respiratory effort belts, one must use correct
weighting constants for the measured belt signals to transform each
signal correctly into a volume signal before summing them together.
Further, to perform a quantitative calibration, the signals of the
respiratory effort belts must be measured simultaneously with a
quantitative reference measure. Known methods therefore require
quantitative equipment for respiratory volume measure, such as a
spirometer, body-box, or similar ways to measure respiratory volume
accurately during the calibration.
[0010] Due to the complexity added with using reference respiratory
volume equipment and the fact that the weighting constants are
subject to change over time with belt and body movements, it would
simplify the measurement of respiratory efforts considerably if
there were a method available that would evaluate weighting
constants without the need of special quantitative equipment for
reference measures.
[0011] Statistical measures of RIP during normal breathing to
evaluate weighting constants may be used for respiratory analysis
and sleep diagnostics. However, the calculation of a calibration
factor will change if the belts move or the subject changes
position. To maintain accuracy, recalibration is needed after such
movements and changes, which requires a few minutes of normal,
non-obstructive breathing. This can be difficult with a sleeping
subject, especially with a subject's suffering from sleep
disordered breathing.
[0012] A method for calculating and calibrating the respiratory
signals in a more continuous fashion without the need for
quantitative equipment would be advantageous.
SUMMARY
[0013] The present disclosure concerns a method, apparatus, and
system for determining a value of one or more parameters of a
respiratory effort of a subject.
[0014] According to one example, the method includes determining a
value of one or more parameters of a respiratory effort of a
subject. The method comprises obtaining a thoracic signal (T), the
thoracic signal (T) being an indicator of a thoracic component of
the respiratory effort, obtaining an abdomen signal (A), the
abdomen signal (A) being an indicator of an abdominal component of
the respiratory effort, and determining, without directly
measuring, the value of the one or more parameters of the
respiratory effort by using constraints and/or relationships of
components of a model of a respiratory system of the subject, and
fitting the components of the model of the respiratory system of
the subject with data from the obtained thoracic signal (T) and
data from the obtained abdomen signal (A).
[0015] According to another example, a respiratory effort measuring
system is provided, which includes a first sensor device configured
to obtain a thorax effort signal (T), a second sensor device
configured to obtain an abdomen effort signal (A), and a processor
configured to receive the thorax effort signal (T) and the abdomen
effort signal (A). The thorax effort signal (T) is an indicator of
a thoracic component of the respiratory effort. The abdomen effort
signal (A) is an indicator of an abdominal component of the
respiratory effort. The processor is further configured to receive
the thorax signal (T), receive an abdomen signal (A), and
determine, without directly measuring, the value of the one or more
parameters of the respiratory effort by using constraints and/or
relationships of components of a model of a respiratory system of
the subject, and fitting the components of the model of the
respiratory system of the subject with data from the obtained
thoracic signal (T) and data from the obtained abdomen signal
(A).
[0016] According to another example, a hardware storage device is
provided having stored thereon computer executable instructions
which, when executed by one or more processors, implement a method
of determining a value of one or more parameters of a respiratory
effort of a subject. The method comprising obtaining a thoracic
signal (T), the thoracic signal (T) being an indicator of a
thoracic component of the respiratory effort, obtaining an abdomen
signal (A), the abdomen signal (A) being an indicator of an
abdominal component of the respiratory effort, and determining,
without directly measuring, the value of the one or more parameters
of the respiratory effort by using constraints and/or relationships
of components of a model of a respiratory system of the subject,
and fitting the components of the model of the respiratory system
of the subject with data from the obtained thoracic signal (T) and
data from the obtained abdomen signal (A).
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIGS. 1a and 1b illustrate an example of respiratory
inductance plethysmograph (RIP) belts, 1a shows an example of the
wave-shaped conductors in the belts, 1b shows the cross-sectional
area of each belt, which is proportional to the measured
inductance.
[0018] FIG. 2 illustrates an embodiment of an RIP belt.
[0019] FIG. 3 an example of RIP signals of many recorded signals
during polysomnography recording used in the field of sleep
medicine. The Chest (Thorax) and Abdomen signals above are typical
RIP signals.
[0020] FIG. 4 illustrates a reference flow signal (top, (A)), a
flow signal from calibrated RIP sum (middle, (B)), and flow signal
derived from uncalibrated RIP signals (bottom, (C)).
[0021] FIG. 5 shows a comparison of reference flow signal of type
pneumo flow (top, (A)) compared with flow signal derived from the
calibrated RIP sum (bottom, (B)).
[0022] FIG. 6 shows data of a whole night comparison between RIP
flow and pneumo flow.
[0023] FIG. 7 shows a comparison between measured esophageal
pressure (top, (A)) and a non-volume contributing effort signal
(bottom, (B)) derived from RIP signals.
[0024] FIG. 8 shows an example of a power loss ratio (annotated)
that evaluates in a quantitative manner the effort of
breathing.
[0025] FIGS. 9a, 9b, and 9c, respectively, show a cumulative and
relative histograms for power loss in 3 subjects with different
levels of upper airway obstruction, from left, (a) subject 1: AHI
0.2, (b) subject 2: AHI 9.8, and (c) subject 3: AHI 21.3.
[0026] FIG. 10 shows a cumulative histogram of power loss in RIP
flow over one night of a 9-year-old patient with AHI of 2.0.
[0027] FIG. 11 shows a multi-parameter respiratory system model for
sleep.
[0028] FIG. 12 graphically shows a first scenario according to the
model of FIG. 11.
[0029] FIG. 13 graphically shows a second scenario according to the
model of FIG. 11.
[0030] FIG. 14 graphically shows a third scenario according to the
model of FIG. 11.
DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS
[0031] Respiratory Inductive Plethysmography (RIP)
[0032] Non-invasive methods to measure breathing movements and
respiratory effort may include the use of respiratory effort bands
or belts placed around the respiratory region of a subject. The
sensor belt may be capable of measuring either changes in the band
stretching or the area of the body encircled by the belt when
placed around a subject's body. A first belt may be placed around
the thorax and second belt may be placed around the abdomen to
capture respiratory movements caused by both the diaphragm and the
intercostal-muscles. When sensors measuring only the stretching of
the belts are used, the resulting signal is a qualitative measure
of the respiratory movement. This type of measurement is used, for
example, for measurement of sleep disordered breathing and may
distinguish between reduced respiration caused by obstruction in
the upper airway (obstructive apnea), where there can be
considerable respiratory movement measured, or if it is caused by
reduced effort (central apnea), where reduction in flow and
reduction in the belt movement occur at the same time.
[0033] Unlike the stretch-sensitive respiratory effort belts, areal
sensitive respiratory effort belts provide detailed information on
the actual form, shape and amplitude of the respiration taking
place. If the areal changes of both the thorax and abdomen are
known, by using a certain calibration technology, the continuous
respiratory volume can be measured from those signals and therefore
the respiratory flow can be derived.
[0034] Respiratory Inductive Plethysmography (RIP) is a method to
measure respiratory related areal changes. As shown in FIGS. 1a and
1b, and 2, in RIP, stretchable belts 31, 32 may contain a conductor
34, 35 that when put on a subject 33, form a conductive loop that
creates an inductance that is directly proportional to the absolute
cross sectional area of the body part that is encircled by the
loop. When such a belt is placed around the abdomen or thorax, the
cross sectional area is modulated with the respiratory movements
and therefore also the inductance of the belt. Conductors 34, 35
may be connected to signal processor 38 by leads 36, 37. Processor
38 may include a memory storage. By measuring the belt inductance,
a value is obtained that is modulated directly proportional with
the respiratory movements. RIP technology includes therefore an
inductance measurement of conductive belts that encircle the thorax
and abdomen of a subject.
[0035] In another embodiment, conductors may be connected to a
transmission unit that transmits respiratory signals, for example
raw unprocessed respiratory signals, or semi-processed signals,
from conductors to processing unit. Respiratory signals or
respiratory signal data may be transmitted to the processor by
hardwire, wireless, or by other means of signal transmission.
[0036] Resonance circuitry may be used for measuring the inductance
and inductance change of the belt. In a resonance circuit, an
inductance L and capacitance C can be connected together in
parallel. With a fully charged capacitor C connected to the
inductance L, the signal measured over the circuitry would swing in
a damped harmonic oscillation with the following frequency:
f = 1 2 .pi. LC , ( 1 ) ##EQU00001##
until the energy of the capacitor is fully lost in the circuit's
electrical resistance. By adding to the circuit an inverting
amplifier, the oscillation can however be maintained at a frequency
close to the resonance frequency. With a known capacitance C, the
inductance L can be calculated by measuring the frequency f and
thereby an estimation of the cross-sectional area can be
derived.
[0037] FIG. 3 shows a sample of RIP signals obtained using an RIP
system described above. As can be seen in FIG. 3, the RIP signals
are only two of the many signals recorded during standard
polysomnography recording used in the field of sleep medicine. The
chest (thorax) and abdomen signals (so labeled) of FIG. 3 are
typical RIP signals.
[0038] Calibration of RIP Signals
[0039] The signal amplitude received from the respiratory effort
belts depends on both the shape of the subject and the placement of
the belts. The thorax respiration signal may be approximately the
same for the whole thorax region but the areal change may be
differently proportional to the thorax respiration signal,
depending on where on the thorax the belt is placed and how the
subject is shaped.
[0040] The same may be true for the abdomen region. The abdomen
respiration signal may be driven by the diaphragm alone and
therefore may be the same all over the abdomen region, but
depending on where the belt is located and the shape of the
abdomen, the areal change may be differently proportional to the
abdomen respiration signal.
[0041] To create a respiration volume signal by summing the thorax
respiration and abdomen respiration, one must use the correct
weights for the measured belt signals to transform each signal
correctly into a volume signal before summing them together. FIG. 4
shows a flow reference signal (top, (A)), a flow signal derived
from calibrated RIP sum (middle, (B)), and a flow signal derived
from un-calibrated RIP signals (bottom, (C)). FIG. 4 further shows
an obstruction of a duration of 26.5 seconds.
[0042] If the thorax RIP signal is T and the abdomen RIP signal is
A the total volume signal can be represented as:
V.sub.R=k.sub.v(k.sub.t.times.T+(1-k.sub.t).times.A) (2),
where k.sub.t is the weight of the thorax signal towards the
abdomen signal and the k.sub.v is the gain required to change the
weighted belt sum to the actual V.sub.R measured in liters or other
volume unit.
[0043] To perform a quantitative calibration, the signals T and A
must be measured simultaneously with a quantitative reference
measure of V.sub.R. Based on the result, the constants, k.sub.v and
k.sub.t can be derived using methods such as, for example, least
square fitting. This method does therefore require quantitative
equipment for respiratory volume measure, such as a spirometer,
body-box or similar ways to measure V.sub.R accurately during the
calibration.
[0044] Even if the quantitative measure of V.sub.R is of interest,
for example, for pulmonary function tests, it is sufficient for
many applications to derive a signal that is only proportional to
the actual volume. This is the case in sleep monitoring, where the
purpose of the measuring is to detect abnormal breathing patterns,
generally by determining if the amplitude of one breath deviates
from a reference breath, which would indicate an obstruction of
flow. In this case it is sufficient to evaluate the proportion
between the thorax and abdomen signals before summing, that is, for
a volume proportional sum V.sub.S1,
V.sub.S1=(k.sub.T.times.T+(1-k.sub.T).times.A) (3).
[0045] Due to the linearity of the system, as the absolute gain of
the signal may not be relevant, the weight factor for T can be
changed to any value as long as the weight factor for A is changed
proportionally. A simplified presentation of the above equation can
therefore be used for the sake of argument, where k.sub.T=1 and
k.sub.A=(1-k.sub.T)/k.sub.T resulting in the equation:
V.sub.S=(T+k.sub.A.times.A) (4.1).
[0046] Due to the complexity added with using reference respiratory
volume equipment and the fact that the k.sub.A is a subject to
change over time with belt and body movements, it would simplify
the measure considerably if there would be a method available that
would evaluate k.sub.A without the need of a special equipment for
reference measures.
[0047] A method may use statistical measures of RIP during normal
breathing to evaluate k.sub.A, using an algorithm referred to as
Qualitative Diagnostic Calibration (QDC), as described in
"Calibration of respiratory inductive plethysmograph during natural
breathing" by Marvin A. Sackner and his collages. (Sackner M A,
Watson H, Belsito A S. J Appl Physiol 1989; 66: 410-420).
[0048] The QDC algorithm allows a qualitative calibration of the
RIP signals during normal breathing to estimate the k.sub.A without
the use of a reference volume signal. The method is based on the
findings that during normal breathing (non-obstructive), the
variance in the amplitude of the thorax and abdomen RIP signals,
when correctly calibrated should be the same, given that the tidal
volume of the breaths are approximately the same. By measuring a
number of breaths (e.g. for a 5 minute period), selecting the
breaths that are close to being normal distributed around the
average tidal volume, and calculating the standard deviation of the
selected breaths for the thorax signal (Sd(T)) and abdomen signals
(Sd(A)), the gain factor can be evaluated as follows:
k A = - Sd ( T ) Sd ( A ) . ( 5 ) ##EQU00002##
[0049] This method may be useful for respiratory analysis and sleep
diagnostics. The drawback is however that the gain factor k.sub.A
will change if the belts move or the subject changes position. To
maintain accuracy, recalibration is needed after such movements and
changes, and that requires a few minutes of normal, non-obstructive
breathing. This can be difficult with a sleeping subject,
especially with a subject's suffering from sleep disordered
breathing. Preferably, a calibration factor for the respiratory
signals would be obtainable in a more continuous fashion.
[0050] As explained above, it is problematic to use QDC calibration
for sleeping patients due to the changes in the calibration
constant that can be expected through the night, due to movements
of the belts and changes of patient body position. The means that
have been used to minimize those effects are mainly to use belts
that are fixed to the patient by the use of adhesive/sticky
materials or woven into the patients clothing. The calibration is
then performed in different positions before the sleep onset time.
The position is constantly monitored and used to select the most
likely calibration gain factor for each period.
[0051] Even if this type of adaptive calibration is more accurate
than using RIP belts that can move and a single point calibration,
it would be beneficial if a method would allow calibration to take
place at any time during the night, not requiring normal and
non-obstructive breathing periods.
[0052] The thorax and abdomen RIP signals contain information on
the total areal change of the rib cage and abdomen area. During
open airway, the movements are close to synchronized and both
signals are similar in shape to the actual respiratory volume
signal. This is the condition required for QDC calibration as
described above. During total obstruction of the airway, the
respiratory volume can be approximated to be close to zero and the
sum of the belts should therefore be close to zero as well in a
calibrated system. This is the condition required for the Isocal
method, where A and T are forced to equal zero by asking the
patient to perform an iso-volume maneuver, as described by K.
Konno, J. Mead, Journal of Applied Physiology, 1968, Vol. 24, n. 4,
544-548. Natural breathing during sleep is however partially
obstructed most of the time, making the use of the previously
described methods, which are limited for most of the time.
[0053] The Components of Breathing
[0054] A different model is described herein that describes the
respiratory movements of the thorax and abdomen and their
relationship with the respiratory volume Vs inhaled at any given
time. The respiratory movement of thorax and abdomen may be divided
each into two signal components. One holds the movement that is
driving the respiration and contributing partially to the
respiratory volume. This component is referred to as "the
volume-contributing component" or the "real" component. The other
contains the rest of the movement that is not contributing any
respiratory volume and is referred to as the "paradoxical
component" or (P).
[0055] The underlying model and parameters of a preferable method
are herein described. For a calibrated system the following
applies:
[0056] From equation (3) above, the following can be derived:
i) V.sub.S=(T+k.sub.AA) (3.2),
As explained above, due to the linearity of the system, other
weight factors for k.sub.T and k.sub.A have the same effect, such
as choosing to set the weight factor k.sub.A to be equal to one
(k.sub.A=1), arriving at equation (3.3) below, and k.sub.T may be
determined as the weight factor, which may be defined as the ration
of weights for T towards A.
V.sub.S2=(k.sub.TT+A) (3.3),
Furthermore, both thorax and abdomen could have weights other than
1 resulting in:
V.sub.SW=(k.sub.TT+k.sub.AA) (3.4),
For ease in understanding the model, in the description below, the
weight factor k.sub.T is, however, chosen to be equal to one
(k.sub.T=1). Based on equation (3.2), the following may be further
defined:
T=k.sub.VT.times.V.sub.S+P (6),
k.sub.AA=(1-k.sub.VT).times.V.sub.S-P (7).
[0057] In the above formulas k.sub.VT is the contribution of the
thorax movement, in the range from 0 to 1 to the volume sum
V.sub.S, whereas the remaining contribution of (1-k.sub.VT) must
come from the abdomen movement. The product of
k.sub.VT.times.V.sub.S is therefore the flow-contributing component
of T while (1-k.sub.VT).times.V.sub.S is the flow-contributing
component of A. The residual movement of the thorax, T may be
termed herein as a paradox component P and is the exact opposite of
a paradox movement in k.sub.AA. Therefore by summing the two, the P
and -P cancel the effect of each other, as this movement is not
contributing any respiratory volume.
[0058] [62] In the extreme case of almost non-obstructive
breathing, the P is close to zero and the shape of both T and
k.sub.AA is close to being identical to V.sub.S,
T=k.sub.VT.times.V.sub.S and
k.sub.AA=(1-k.sub.VT).times.V.sub.S.
During the other extreme case of fully obstructive breathing,
V.sub.S drops to zero, T=P while k.sub.AA=-P.
[0059] The model may therefore successfully describe respiratory
movements of thorax and abdomen for differing levels of
obstruction, given that the coefficients, k.sub.A and k.sub.VT are
known. Further, a calibration factor for calibrating the thorax
effort signal T and the abdomen effort signal A may be obtained
based on the optimized weight factor k.sub.A. The weight factor
coefficients k.sub.A and k.sub.VT may then be stored, and the
calibrated thorax effort signal T and the abdomen effort signal A
and the volume sum V.sub.S may be stored and displayed on a display
device.
[0060] From equations (6) and (7) an equation for the parameter P
can be derived:
T - k A A = k VT .times. V S + P - ( ( 1 - k VT ) .times. V S - P )
T - k A A = 2 P + ( 2 k VT - 1 ) .times. V S P = T - k A A + ( 1 -
2 k VT ) .times. V S 2 P = T - k A A + ( 1 - 2 k VT ) .times. ( T +
k A A ) 2 P = 2 T - 2 k VT V S 2 ##EQU00003## P=T-k.sub.VTV.sub.S
(8).
As can be seen in equation (8) above, the paradox signal cannot be
determined from the T, k.sub.AA and V.sub.S only but is also a
function of the actual volume contribution from each belt
k.sub.VT.
[0061] In accordance with the model of this embodiment, there are
different ways to suitably determine the coefficients k.sub.A and
k.sub.VT, some examples of which are described herein.
[0062] Harmonic vectors calibration is based on the theory that
xAb=tcS+P and (1-x)Th=(1-tc) S-P, where S=xAb+(1-x)Th, and P is a
paradox and S is the flow. It is assumed that in the frequency
domain P and S are out of phase. For example, the frequency domain
P and S may be 90 degrees out of phase. The flow contributing parts
of xAb and (1-x)Th are in phase and the paradox, non-flow
contributing parts P cancel each other when xAb and (1-x)Th are
scaled with the correct value of x and added. When the Ab and Th
parts are scaled with the correct x value, the P parts are of equal
amplitude but opposite signs in the xAb and (1-x)Th. When xAb and
(1-x)Th are separated into their frequency components all flow
contributing frequency components of zAb and (1-x)Th are parallel
to S, it should be seen that when the correct calibration value is
chosen that the amplitudes of the parallel components should be the
same as in S multiplied by a constant, tc and (1-tc) in xAb and
(1-x)Th, respectively. Based on this theory, the respiratory flow
(F) of the subject may be obtained by derivation.
[0063] A different model is described herein that describes the
respiratory movements of the thorax and abdomen and the
relationship between the volume-contributing thoracic component
(VST) and a thoracic paradox component (PT) of the thoracic
respiratory movement, and the volume-contributing abdomen component
(VSA) and an abdomen paradox component (PA) of the abdomen
respiratory movement. In this model the time series describing the
volume-contributing thoracic component (VST) and the time series
describing thoracic paradox component (PT) have a 90 degree phase
difference between frequency components at identical frequencies.
In this model the time series describing volume-contributing
abdomen component (VSA) and the time series describing abdomen
paradox component (PT) have a 90 degree phase difference between
frequency components at identical frequencies. In this model the
time series describing thoracic paradox component (PT) and the time
series describing abdomen paradox component (PA) have a 180 degree
phase difference between frequency components at identical
frequencies.
[0064] A correct calibration value is found when the amplitude of
the thoracic paradox component (PT) equals the abdomen paradox
component (PA) at all identical frequencies.
[0065] The Characteristics of a Calibrated System
[0066] In a correctly calibrated system, the paradox component P is
equivalent to, but opposite in amplitude in both thorax and abdomen
signals. The paradox is caused by the negative pressure in the
thorax during inhalation and is therefore directly affected by the
respiratory effort and the level of flow resistance in the upper
airway. As the flow resistance is in most cases either caused by
soft tissue in the upper airway or enlarged tonsils, it is not
constant but is modulated by and during the respiration. The
pressure drop over the flow resistance causes the tissues involved
to narrow the airway even further during inhalation, but widens
again during exhalation.
[0067] For this reason the V.sub.S and P signal components may have
the following characteristics.
[0068] Amplitude and Power Loss Due to Summing of Channels
[0069] As V.sub.S is the sum of the volume-contributing components
of the thorax and abdomen signals, the paradox components present
in T and k.sub.AA disappear in the sum. As part of the signal is
lost, the amplitude of the summed signal V.sub.S is therefore less
than the sum of the amplitudes of T and k.sub.AA, and the same is
true for the power of the summed signal V.sub.S as compared to the
sum of the powers of T and k.sub.AA. (In this context, `power`
refers to mathematical power function, not electrical power.) Thus,
based on equation (4.1), V.sub.S=(T+k.sub.AA), it follows that for
the amplitude, the following applies:
|V.sub.S|.ltoreq.|T|+|k.sub.AA| (9),
and therefore its power is as follows:
PV.sub.S.ltoreq.PT+Pk.sub.AA. (10).
[0070] This power loss is minimal during normal breathing,
increases with increased partial obstruction until it is absolute
during complete obstruction. For any given timeframe, the amplitude
and power loss are at maximum when the sum is correctly calibrated.
Accordingly, a useful k.sub.A value can be obtained, which is a
value that maximizes the power loss and/or amplitude loss, compared
with the sum of the power or amplitudes respectively, of T and A
over a period of time. This is done in certain useful embodiments
of the disclosure and readily achieved with iterative
calculations.
[0071] Paradox Present in Higher Frequencies
[0072] Obstructive changes in the airway during a single breath are
by nature quicker events than the respiration itself and do
therefore contain higher frequency components compared with the
flow signal. The power of P is therefore more in the higher
frequencies compared with V.sub.S and the relative power of the
fundamental frequency is therefore higher in V.sub.S than in both T
and k.sub.AA.
[0073] Accordingly, in some embodiments a useful k.sub.A value is
obtained, by finding a k.sub.A value such that the proportional
power of lower frequencies in the respiratory signal is maximized
as compared with higher frequencies in the respiratory signal, over
a period of time. It is apparent that "lower" and "higher"
frequencies in this context are determined based on normal
breathing frequencies. Thus lower frequencies could be, in one
embodiment, frequencies lower than double the average frequency
being measured (which is generally the fundamental frequency of the
breathing signal, readily determined, e.g. by FT transforming of
the signal). Or in other embodiments frequencies lower than 1 Hz,
0.5 Hz or lower, such as lower than 0.2 Hz (12 breathes per minute)
or lower than 0.1 Hz. Higher frequencies would accordingly be those
frequencies that are higher than the cutoff between lower and
higher frequencies, or in some embodiments those frequencies that
are higher than the fundamental frequency of the breathing
signal.
[0074] Based on the above, the present disclosure further provides
for, in some embodiments, ways to use the magnitude of the loss of
amplitude or power, the magnitude of loss of the higher frequency
components or a combination of these to determine a k.sub.A value
that is considered optimal over a certain period of time.
[0075] Optimization Methods for k.sub.A
[0076] 1. Maximum Fundamental Frequency Optimization (MFF)
[0077] In another embodiment, a MFF method seeks by trial and error
over a certain period, the k.sub.A that results in the
V.sub.S=(T+k.sub.AA) that maximizes the power of the fundamental
frequency of the signal relative to the overall signal power. The
logic is that as P contains higher frequency components than
V.sub.S, maximizing the relative power of the fundamental frequency
is the same as minimizing the effects of P in the sum.
[0078] The MFF method may be applied in useful embodiments. The
fundamental frequency is generally the undisturbed breathing
rhythm, the period referred to can be relatively short, such as but
not limited to breath by breath (one breathing cycle), or a
predetermined period such as 1 minute or a few minutes. Different
time-frequency analysis methods can be used separately or together
for maximizing of the method efficiency, including but not limited
to, Wavelet transform, Fourier transformation, statistical
modeling, etc.
[0079] 2. Minimum Signal Amplitude Optimization (MSA)
[0080] A MSA method seeks by trial and error for a given period the
k.sub.A that results in the V.sub.S=(T+k.sub.AA) that minimizes the
resulting signal compared with the amplitude of T and k.sub.AA,
S l min = min RMS ( V S ) RMS ( T ) + RMS ( k A A ) . ( 11 )
##EQU00004##
S.sub.l can be understood as relative difference between the
V.sub.S amplitude versus the sum of the amplitudes of T and
k.sub.AA. The logic is that P contributes to the amplitude of the
measured signal but not to the amplitude of the respiration. By
minimizing the amplitude of the resulting signal S.sub.l, one is
minimizing the influence of P and the respiration part of the
signal is therefore maximized. The period in the MSA method can in
some embodiments be the period of a single breath (suitably
determined as described above), or a few breaths, or longer, such
as in the range from about 10 sec. to about 10 minutes, such as
e.g. about 0.5 minute, or about 1 min period, about 5 minutes or
about 10min period. In other embodiments longer periods are used,
such as 0.5 hour, 1 hour, or a period of a few hours (e.g. 2,3,4 or
5 hours), where a suitable period can be selected depending on the
application.
[0081] 3. Minimum Obstruction Amplitude (MOA) Optimization
[0082] The MOA method is useful for periods of time in embodiments
where there are quick changes in the signal amplitudes between
breaths and periods of obstruction. The obstructive periods provide
the opportunity to perform conventional isovolume calibration, by
selecting the k.sub.A that minimizes in the best way the V.sub.S
during obstruction. This can be done for a period of the signal
(e.g. 0.5 minute, or 1 min or a few minutes such as in the range
1-5 minutes), by dividing the period into a number of n shorter
time frames of few seconds each (such as e.g. in the range of 3-10
seconds, or in the range of 3-5 seconds; should fit within an
apnea). For a given timeframe i, the S.sub.lmini is found and the
relative k.sub.Ai is stored. The value of k.sub.A for the whole
period (longer period) is then selected by a weighted average over
the period by giving the timeframes that resulted in the lowest
S.sub.lmin values the maximum weight. More weight can be given to
the timeframes that performed in the best way by using a non-linear
weight transformation.
[0083] In some embodiments, combinations of two or more of the
above methods are applied to obtain a suitable optimal k.sub.A
value.
[0084] In other embodiments one or more method as described above
is applied to derive an intermediate k.sub.A value for a shorter
time span, such as e.g. in the range of 5-60 seconds, such as 5-30
seconds, or in the range 5-20 seconds, or in the range 10-30
seconds, and weighing the performance for each timespan and
choosing a k.sub.A for the longer timespan (e.g. in the range 1-15
minutes, or in the range 1-10 minutes, such as 5-10 minutes, or
longer time spans such as in the range 10-60 minutes, or in some
embodiments even longer periods, such as in the range 1-10 hours,
e.g. in the range 1-5 hours or in the range 5-10 hours), based on
selecting the method providing the most determinant result.
[0085] For evaluating and deriving a suitable determinant value of
the weighing ratio, various methods can be applied. In one
embodiment the performance of the intermediate periods and
intermediate values is evaluated by weighing the performance for
each intermediate time span (e.g. averaging or otherwise
statistically comparing), then the different methods can be
compared by comparing which method(s) gives a most consistent value
with minimal fluctuations while maintaining the minimal paradoxical
components.
[0086] Weighting in the Neighboring Periods
[0087] The method reliability is in some embodiments enhanced
further by selecting a set of periods and basing the selection of
k.sub.Aj on an average, weighted sum or other performance criteria
from the periods in the set. In this way, an accurate k.sub.Aj
value can be selected for periods that have low signal and
frequency magnitude losses, based on a more reliable estimation of
a neighboring period.
[0088] This is in some embodiments done, e.g., by splitting each
minute (or other chosen time span) into overlapping intermediate
periods (e.g. 5 seconds, or intermediate periods of other chosen
time length, such as but not limited to 3 seconds, 8, or 10
seconds), the k.sub.Aj value used for the whole time span can be
the weighted sum of the k.sub.A values for each intermediate period
where the weight of the periods that have the maximum
amplitude/power loss and/or frequency loss is higher than for
periods that show lower losses. This can then be applied for longer
periods, taking a set of time spans (e.g., minute time spans) and
for example calculating a weighted trend-curve for the changes of
k.sub.A minute by minute, giving the minutes with the strongest
losses the maximum weight and those with low losses the minimum
weight.
[0089] Selecting the Optimization Method
[0090] To adapt optimally to the information available at each
period in the signal, the method should preferably use the k.sub.A
that fits best for each condition. The measure of how well a method
performs can be based on the quantity of the amplitude, signal or
frequency loss, where the method providing the highest loss is
generally considered the optimal one.
[0091] A good criteria for selecting the k.sub.A also preferably
results in less switching between methods and optimally that
switching from one method to the other occurs when the two methods
predict nearly the same value of k.sub.A. This way a continuous
trend is achieved where sudden shifts in the resulting signal,
caused by switching methods, are avoided.
[0092] To further optimize the selection criteria, a good result
can be achieved by processing a series of periods instead of one by
one and then choosing the methods for each period that maximizes
the continuity of the k.sub.A between periods, minimizes the number
of switches between methods or a combination of both.
[0093] FIG. 5 shows a comparison of reference flow signal of type
pneumoflow (top, (A)) compared with flow signal derived from the
calibrated RIP sum (bottom, (B)).
[0094] FIG. 6 shows data of a whole night comparison between RIP
flow and Pneumoflow.
[0095] Signals Derived from the Calibrated Effort
[0096] A number of interesting and useful signals can be derived
with the embodiments of this disclosure, from the calibrated thorax
and abdomen signals.
[0097] The Paradox P and the Thorax Volume Contribution
k.sub.VT
[0098] As demonstrated in equation (8), even after optimally
calibrating the sum, the level of the paradox signal P has more
than one solution depending on the k.sub.VT.
[0099] To evaluate the P, a method must be created to seek for the
correct k.sub.VT. The physiological characteristics of P is that
the paradox is driven by the pressure difference in the thorax and
in the surrounding atmosphere, caused by the inhalation of air over
a partial obstruction in the upper airway. For a given pressure
difference, the paradox status generally has a balance to the
lowest energy level capable of creating that status. This
characteristic can be used to determine a useful and correct value
P by choosing the k.sub.VT that results in the lowest power
function/amplitude of P. Accordingly, in a further embodiment, this
disclosure provides a method for determining a useful value of the
paradox component P.
[0100] The paradox signal P is of special interest as it is
directly derived from the thorax internal pressure and is a strong
indication of the respiratory effort taking place for each breath.
This parameter is a candidate for being used as a substitute for a
very invasive method currently being used in sleep medicine. This
method is a direct measure of the esophageal pressure that is
currently performed by threading a catheter through the nose and
into the esophageal to monitor the respiratory pressure below the
upper airway obstruction.
[0101] The other signal is the thorax contribution, which is also
of interest as the physiology suggests that the ratio of thorax
contribution vs. abdomen contribution changes with the level of
sleep, the respiratory muscular activity being different during REM
sleep compared with the N1, N2, and N3 sleep stages. Sudden changes
in the contribution ratio are therefore strongly related with REM
onset and offset.
[0102] FIG. 7 shows a comparison between esophageal pressure (top,
(A)) compared with the derived P signal (bottom, (B)).
[0103] The Power Loss Ratio
[0104] As described in equations (9) and (10) the total amplitude
and thus the power of the sum V.sub.S is less than the sum of the
amplitude and power of the T and k.sub.AA due to the loss of the
paradox signal P.
[0105] The value S.sub.lmin defined in (11) describes the
efficiency of the respiration, that is, what portion of the
respiratory movement did result in respiratory flow and what
portion did not. This index is of great interest as it predicts in
a continuous manner the quality of the breathing, being 100% during
no obstruction and becoming 0% for total obstruction or, the other
way around, the power loss being 0% for no obstruction and 100% for
full obstruction.
[0106] As this index is the same as is used to seek the calibration
value k.sub.AA, it can also be calculated directly from the belt
signals by applying the model defined in (11), seeking the k.sub.A
that results in the minimum S.sub.lmin and use that value as a
power loss index. FIG. 8 shows a power loss ration evaluated in a
quantitative manner the effort of breathing.
[0107] FIGS. 9a, 9b, and 9c show cumulative and relative histograms
of power loss in three subjects with different breathing. As can be
seen the histograms are quite different, illustrating difference
between subjects with healthy breathing and subjects with
disordered breathing. Using 20% power loss as a threshold, only 5%
of the breaths of Subject 1 are above that threshold, 20% of the
breaths of Subject 2 and half of the breaths of Subject 3. The
power loss index is therefore a candidate to be used as a
quantitative measure of the level of partial obstruction.
[0108] FIG. 10 shows a histogram for a 9-year-old subject with AHI
of 2.0. As can be seen on the Histogram from the 9-year-old above
with scored level of apneas of AHI 2.0, 10% of his breaths are
below the 20% threshold and where as 99% of Patients 1 breaths are
below 30% signal loss, more than 5% of the breaths of this
9-year-old are above the 30% signal loss limit or more than
measured in Patient 2 with AHI of 9.8.
[0109] The power loss index is therefore a candidate to be used as
a quantitative measure of the level of partial obstruction.
[0110] Volume Calibration and Stabilization
[0111] As can be seen from equations (2) and (3), V.sub.R is an
absolute volume signal measured in liters, while V.sub.S is a
signal that is directly proportional to V.sub.R but is not the
absolute volume signal. If the sensitivity of T changes in (3) due
to belt movement or other changes in physiology, the gain between
V.sub.S and V.sub.R changes during the night. This causes the
problem for signal like the V.sub.S and P that even if their
initial values where known in a volume unit like liters, they would
change or drift through the night. This can be prevented to some
degree by fixing the belts as tightly to the subject as possible.
However, if there were a biomedical parameter that would allow the
belts to be regulated to show a constant ratio towards V.sub.R, the
results would allow the signals to be used with confidence to
compare amplitudes at different times over the night.
[0112] The present disclosure provides a further method that makes
use of the physiological characteristics that the human body
regulates the intake of oxygen to match the need of the cells at
all time, not building up or dropping oxygen levels during normal
breathing. The indication of increased metabolism is higher minute
ventilation (minute ventilation referring to the total volume
inspired or expired per minute) and heart rate and during aerobic
breathing the ratio between minute ventilation and heart rate is
close to linear. The method monitors the relative minute
ventilation from the V.sub.S signal and compares it to a measured
heart rate. The characteristic linearity between the minute
ventilation and heart rate is captured during periods where the
k.sub.A is not changing significantly but where there is a
variation in the minute ventilation and heart rate. The captured
value is then used to correct the V.sub.S when there is a change in
the k.sub.A values. This way the V.sub.R/V.sub.S ratio can be kept
nearly constant throughout the recording, allowing volume
calibration to take place at one or more points during the
recording and delivering reliable measure for all periods.
[0113] Time Variance
[0114] Even with the above-described model for calibration and
effort measurements within a certain timeframe, a complete model of
respiration during sleep may to take into account more variables
that affect both the accuracy of the calibration and the
respiratory effort measure. Parameters such as time-variability of
intercostal muscle activity and different body pressure on the
respiratory system with different body positions can have a
significant effect on the respiratory effort measure and
calibration values. Below is described the effect of those
variables on the measure and how it is possible to correct for the
influence by including more information on the respiration to the
model.
[0115] 1. Improved Accuracy Respiratory System Model for Sleep
[0116] In FIG. 11, a multi-parameter model can be seen showing some
key parameters of the respiratory movement. The airway goes from a
subject's mouth and nose, through airway resistance Rr into the
intra thoracic volume. The inner pressure of the intra thoracic
volume PIt is caused by the pressure drop over airway resistance Rr
during inhalation and exhalation. Airway resistance Rr may be
considered a time-dependent variable modulated by the respiratory
flow that depends on body and head position, muscle tonus of the
upper airway, basic diameter of airway and tongue position. The
thorax area (At) and abdomen area (Aa) movements are driven by the
intercostal muscle providing force FTm and the diaphragm providing
force FAm. For a respiratory movement to start, the thoracic and
diaphragm muscle force must overcome any counter forces from the
body. For the thorax, the counterforce FTb is caused by lifting the
chest weight and stretching muscle and skin of the chest. For the
abdomen, the diaphragm must overcome the counter force FAb created
by the organ hydraulic pressure from the abdomen.
[0117] As shown in a first scenario in FIG. 12, when inhalation
starts, the intrathoracic pressure PIt depends on the flowrate and
the flow-resistance in the airway Rr. PIt may be considered
negative during inhalation, causing air to flow into the lungs.
This negative pressure causes an additional force on the thorax and
abdomen (FTp and FAp), where:
F.sub.Tp=PIt*At, and
F.sub.Ap=PIt*Aa.
[0118] From the model above, according to a first scenario, low Rr
results in low PIt, and low PIt results in low FTp and low FAp.
High Rr results in high PIt, resulting in high FTp and high
FAp.
[0119] 2. Reflecting the Respiratory Effort Measure in the New
Respiratory Model
[0120] From the model above, it can be understood how the power
loss algorithms are related to the effort measured by the
intrathoracic pressure PIt. Referring to a second scenario shown in
FIG. 13, if it is assumed that for a certain amount of time, the
patient is not changing position giving constant FTb and FAb, and
the ratio of his thorax respiratory drive FTm towards his abdomen
respiratory drive FAm is close to constant.
[0121] Now if it is assumed that the Rr is very low, that is the
upper airway resistance is negligible. This means that there is no
PIt buildup--or close to no PIt buildup--and therefore FTp and FAp
are close to or equal to zero. In this system, the thorax and
abdomen move in synchrony but independently, that is, the movement
of one does not affect the other. The result is that there is no
power loss correctly predicting very low PIt and low respiratory
effort.
[0122] Now if it is assumed that Rr is very high during inhalation,
causing the upper airway to collapse. In this situation, any thorax
or abdomen movement causes a change in intrathoracic pressure PIt
that is proportional to the movement. If the total force of either
the thorax or abdomen gets to be negative during inhalation,
(FTm-FTb-FTp)<0 or (FAm-FAb-FAp)<0, the one with the negative
value being drawn inwards while the other moves outwards, creating
180.degree. phase shift between the belts or paradox breathing. In
this case, the power loss in a correctly calibrated system is 100%,
correctly describing the high intrathoracic pressure PIt.
[0123] According to the second scenario, FAm<FTm. When Rr is low
both FTm+FTp>0 and FAm+FAp>0. When Rr is large FTp and FAp
become large resulting in FTm+Ftp>0 and FAm+FAp<0.
[0124] The third scenario, as shown in FIG. 14, would be a partial
closure during inhalation with medium value of Rr. In this case,
considerable PIt could build up, but both (FTm-FTb-FTp)>0 and
(FAm-FAb-FAp)>0. This means that both parts move outwards during
exhalation, but the stronger one will lead the movement while the
other is held back. The one leading will delay the one following
from reaching his maximum value and the follower will first reach
maximum when the leader has started exhaling. This causes both a
phase shift and loss of power where the squared sum of the traces
is lower than the sum of the square sums of each trace. This will
result in a power loss value somewhere in the range of 0-100%
depending on the level of the loss. For fixed FTm, FTb, FAm and
FAb, the loss is only dependent on FTp and FAp, eventually only
dependent on the PIt. The power loss correctly delivers a value
directly proportional to the PIt as it is supposed to.
[0125] According to the third scenario, FTm>FAm, causes Thorax
to rise faster than Abdomen due to the fact that
FTm+FTp>FAm+FAp.
[0126] 3. Time Variables Derived from the Respiratory Model
[0127] Over longer periods of time, the FTm, FTb, FAm and FAb are
not constant but may vary. The force from the body pressure, FTb
and FAb is significantly dependent on the body position.
[0128] In supine position, the abdomen full weight causes pressure
on the diaphragm resulting in high FAb while the thorax may move
relatively freely causing lower FTb. With the body lying on either
side, there is a significant pressure on one side of the thorax,
increasing the FTb while the abdomen is now on level with the
diaphragm, causing lower FAb. Prone position of the body will cause
pressure on the thorax, causing high FTb while it depends on the
circumference of the Abdomen if the pressure FAb is increased or
not.
[0129] The respiratory drive is also changing during sleep. The
most dramatic change is between non-rapid eye movement NREM and
rapid eye movement REM sleep as the intercostal muscle activity
drops to close to none during the REM period's skeletal muscle
paralysis. During that time FTm is close to none while FAm remains
constant.
[0130] Those changes directly affect the power loss calculations,
so for the same PIt, the power loss will be different between
periods where FTm, FTb, FAm or FAb change. For accurate us of RIP
signals for evaluation of respiratory effort between patients or
between periods within the night, it is preferable to use a
modified power loss parameter that takes into account the forces
FTm, FTb, FAm and FAb.
[0131] 4. Measure of Multiple Time Variable Parameters Affecting
Respiratory Effort
[0132] The forces FTm, FTb, FAm and FAb can be evaluated based on
the RIP signals. A key parameter for such an evaluation are
inhalation, exhalation, and respiration time, shape deviation
between the thorax and abdomen signals along with the phase shift
between the two.
[0133] During inhalation, all the forces FTp, FTm, FTb, FAp, FAm
and FAb are present, but during exhalation, the muscle forces, FTm
and FAm drop to zero, the PIt is reversed from higher negative to
low positive values. The exhalation PIT values are relatively
constant from breath to breath as the flow now opens the airway and
the effect of the soft tissue causing the inhalation resistance is
minimized. This means that the exhalation rate is dominated by the
body pressure on the respiratory system only, which is driven by
the FTb and FAb. The body forces are as mentioned above caused by
the respiration thorax intercostal muscles stretching of the thorax
and diaphragm pressing the inner organs of the abdomen. The energy
used for this lifting is preserved, similar to compression of a
spring in a mechanical system, so when the exhalation starts, the
air is forced out by the energy stored in the stretched thorax and
lifted abdomen. The exhalation time of Thorax vs. Abdomen is
therefore directly relational to the amount of work that each
system puts into the inhalation, that is, with exhalation time
thorax=Tet and Aet for abdomen, the FTm/FAm is proportional to
Tet/Aet. It is therefore important to use a measure of Tet and Aet
in the calculation of respiratory effort to take into account the
time variation of FTm, FAm ratio such as between non-rapid eye
movement (NREM) and rapid eye movement (REM) sleep. Assuming the
exhalation flow behaves as an exponent the body forces FTm and FAm
are directly related to the exponent time constants for the
thoracic exhalation .tau.T and the abdomen exhalation .tau.A.
[0134] For a calibrated system, comparing the exhalation time at
one point in the study, such as Tet1 to another Tet2 is of
interest. As described above, the body force FTb and FAb changes
with different body positions. If a patient is for example at time
t1 in supine position and at t2 on left or right position, the FTb
force could be significantly higher per liter inhaled at t2
compared with t1. The reason is that in supine position, the chest
wall moves freely, while on the side, the bodyweight presses the
chest wall that is touching the bed. For the same muscle effort,
FTm at both times, the tidal volume inhaled by the chest is
therefore lower on the side compared with the supine position. As
the energy of inhalation going into thorax inhalation is conserved,
the exhalation flowrate of the thorax will be the same for both t1
and t2. Comparing the exhalation time and volume from one point of
the study to another provides an indicator that is proportional to
the changes in FTb and FAb between those two points. This indicator
can then be used to account for the changes in FTb and FAb when the
respiratory effort is evaluated from the belts.
[0135] With an indicator on both the changes in FTm, FAm, FTb and
FAb available, the inhalation time and the ratio of inhalation vs.
exhalation time becomes of interest. For the same FTm, FAm, FTb and
FAb, the ratio of inhalation vs. exhalation time is only variated
by the FTp and FAp. The more negative the PIT becomes, the longer
it takes to fill the lungs compared with exhaling. The inhalation
time and exhalation time are therefore also important parameters
for evaluating the actual respiratory effort.
[0136] As significant aspect of the present disclosure is that
respiratory effort may be determined by adjusting components of a
model of the respiratory system based on the thoracic effort signal
(T), the abdomen effort signal (A), or the respiratory flow (F) or
any combination of the thoracic effort signal (T), the abdomen
effort signal (A), and the respiratory flow (F). In such a
determination, the obtained thoracic effort signal (T) and the
abdomen effort signal (A), with an obtained respiratory flow (F)
are used, and one or more parameters in the model of the
respiratory system so that the model can predict or provide a
respiratory effort of the subject. The model dynamics set
constraints on the solution space, and by fitting the model
parameters using physical and temporal constraints the model can be
used to predict or derive the respiratory effort of the subject.
For example, fitting certain parameters during exhalation, fixing
their values and fitting other parameters during inhalation the
model can be fitted. Other factors such as setting the physical
constraint that body parameters do not change rapidly between
consecutive breaths, so a stable solution of the model can be found
by identifying the model parameters which produce a good fit for
multiple consecutive breaths. Similarly, a ratio may be derived of
thoracic inhalation time versus thoracic exhalation time, a ratio
of abdomen inhalation time versus abdomen exhalation time, and
total inhalation time versus total exhalation time may be derived
and used as parameters of the model.
[0137] Another parameter may be respiratory drive or changes in
respiratory drive. Another parameter may be the thorax contribution
to respiration or changes to thorax contribution. Another parameter
may be abdomen and thoracic exhalation time constant, or changes in
the exhalation time constant. Another parameter may be the intra
thoracic pressure or changes in the intrathoracic pressure.
[0138] The abdomen and thoracic muscles force respiratory drive,
and each element of the above model represents a part of the
respiratory system. The fitted values of these elements represent
some factor or parameter of the system. For example, the resistance
represents the airway resistance. The muscle forces FTm and FAm
represent the respiratory drive. And the relative partition of each
element in respiration, the body forces FTb and FAb represent the
force due to mass, elasticity, rigidity, and hydraulic pressure of
the system, the areas At and Aa represent the coupling between the
intrathoracic pressure and the thorax and abdomen, and the thoracic
pressure PIT represents the intrathoracic pressure.
[0139] Further, the mass components of the subject's body may be
parameters. If mass is not included in the model may cause the
model to oscillate and have infinitely short reaction times.
Accordingly, various mass components, such as thoracic or abdomen
masses can be helpful parameters.
[0140] Further, parameter surrogate to respiratory effort or
intrathoracic pressure can be measured using the RIP belts and
possibly flow. It is further significant that the parameters of the
model may be fit using the RIP measurements combined with a
directly measured or an alternatively derived respiratory flow to
determine the respiratory effort.
[0141] Other parameters that are directly affected by the
respiratory effort level are the divergence between the two belt
signals, the phase between the two belt signals, changes in
respiratory rate, and of cause the power loss of the sum.
[0142] 5. Patient to Patient Comparison
[0143] It is clear that patients are different in general and
patient to patient comparison must account for that difference when
comparing the respiratory effort calculations. For example children
have softer rib cages compared with adults, causing higher power
loss for a given PIt compared with the adult. A general method to
account for such differences is to use normalization towards a
normal breath of a particular patient. During sleep studies, a
patient will have a number of breaths that have low respiratory
effort, such as before the study start and during the night after
position changes etc. Those breaths can be easily identified as
they will have relatively similar inhalation and exhalation time.
The higher the power loss of the sum is measured compared to the
inhalation/exhalation time ratio, the softer is the rib-cage.
Selecting breaths and applying those parameters can therefore be
used to create an additional weight on the measured power loss to
make it comparable from one patient to the other.
[0144] 6. Multi-Parameter Modeling of Respiratory Effort
[0145] From the above description, we have the following.
[0146] A method is provided for evaluating the ratio of the signal
loss S.sub.l in the calibrated sum by first applying the methods of
1, 2, or 3 to evaluate k.sub.A, then using the k.sub.A to create a
weighted sum between signals based on T and A and then calculating
the ratio between the amplitude, power or any transformation f of
this sum towards the sum of the amplitudes, power or any other
transformation f of each of the signals derived from the T and A.
E.g.
f ( T + k A A ) f ( T ) + f ( k A A ) or f ( T ' + k A A ' ) f ( T
' ) + f ( k A A ' ) or f ( T ' + k A A ' ) f ( T ' ) + f ( k A A )
##EQU00005##
[0147] It is now clear that this formula does indeed describe
respiratory effort accurately for a given patient, body position
and sleep stage. It does however need to take the parameters
mentioned above into account to maximize the comparability between
patients, body positions and sleep stages. This can be done using
linear or non-linear multi-parameter modeling. The method measures
all of the above parameters and feeds them into a function that
transforms them into an output value indicating the respiratory
effort. Typically this would be done by "training" the linear or
non-linear model on the input parameters to best describe the known
output values for respiratory effort. The known output values would
be derived from invasive measurements, such as direct or indirect
measure of intra thoracic pressure PIT and/or diaphragm and
intercostal muscle EMG.
[0148] When a model has been trained, the model parameters can be
used to identify all forces, pressures and resistances depicted in
FIG. 11. Furthermore, the model can be used to identify other
parameters such as the thoracic and abdomen exhalation time
constants TT and TA, respectively. The respiratory drive or
intended volume, can be identified directly from the model or by
using the derived parameters and setting the airway resistance Rr
to a low value. The relative contribution of the thorax and abdomen
to breathing can be identified.
[0149] For RIP based respiratory parameters R={r1, r2, r3 . . . rn}
and the effort values E={e1, e2, e3 . . . em} the general formulas
to describe such a relationship would be
[0150] E=B.times.R for a linear transformation while it would go
E=F(R) for non-linear transformation.
[0151] There are multiple methods to find the optimal formulas
describing this relationship. For example, for linear
transformation, the use of Kalman filtering to describe the
transformation matrix B is a standard method in engineering, while
for seeking a non-linear relationship for describing the
transformation F(R) is commonly done by using artificial neural
networks with parameters optimized to with the R to E
relationship.
[0152] According to the present disclosure, the following
embodiments and combinations of embodiments are provided: In a
first embodiment, embodiment 1: A method for calibration of
respiratory effort signals is provided where: [0153] at least
Thorax effort signal T and Abdomen effort signal A are measured,
[0154] where T and A signals are composed of the sum of at least
two different signal components each, [0155] where at least one
signal component (V.sub.ST) in T and one signal, component
(V.sub.SA) in A both have the same shape as the actual respiratory
volume, that is they are positively proportional to the actual
respiratory volume, [0156] where at least one component P.sub.T in
T is negatively proportional to a component P.sub.A in A, [0157]
where the weighted sum of the T and A signals is used to derive a
Volume proportional signal V.sub.S, [0158] where the ratio of the
weights for A towards T is k.sub.A, and [0159] where k.sub.A is
selected by seeking a value of k.sub.A that minimizes the residues
of the P.sub.T and P.sub.A components in the resulting sum
V.sub.S
[0160] In embodiment 2, further to the embodiment 1 above or any of
the embodiments below, instead of processing the respiratory effort
signals T and A, the first derivative of T (T') and A (A') are used
instead of the T and A to minimize the residues of the P.sub.T and
P.sub.A resulting in a Respiratory Flow proportional signal
F.sub.S.
[0161] In embodiment 3, further to any of the embodiments above or
below, any level of integration or derivative of T and A are
used.
[0162] In embodiment 4, further to any of the embodiments above or
below, the proportional power of the lower frequencies are
maximized compared with the higher frequencies over a period of
time.
[0163] In embodiment 5, further to any of the embodiments above or
below, the amplitude loss and/or power loss of the resulting signal
is maximized compared with the sum of the amplitudes and/or power
of T and A over a period of time.
[0164] In embodiment 6, further to any of the embodiments above or
below, by first applying the methods described in embodiments 4
and/or 5 to derive the ratio for shorter timespans within the
longer period, the performance of the methods is weighted for each
timespan before choosing a k.sub.A for the longer period based on
selecting the methods providing the most optimal result.
[0165] In embodiment 7, further to any of the embodiments above or
below, the method further includes weighting in the k.sub.A values
of the other periods within the set to maximize the continuity and
level of determination over the whole set.
[0166] In embodiment 8, further to any of the embodiments above or
below, the method further includes deriving the residual signal
P.sub.T, or any derivative or integer of P.sub.T from T and A
signals by first applying the methods described in 1, 2, or 3, and
then use the resulting signal V.sub.S to seek for the thorax and
abdomen contribution ratio that results in minimizing of the
amplitude and/or power of P.sub.T.
[0167] In embodiment 9, a method is provided for evaluating flow
resistance from the ratio of P.sub.T defined in equation (9) with
the first derivative of V.sub.S.
[0168] In embodiment 10, a method is provided for evaluating
respiration energy by integrating the multiply of P.sub.T defined
in equation (9) with the first derivative of V.sub.S
[0169] In embodiment 11, a method is provided for evaluating the
ratio of the signal loss S.sub.l in the calibrated sum by first
applying the methods of embodiments 1, 2, or 3, above, to evaluate
k.sub.A, then using the k.sub.A to create a weighted sum between
signals based on T and A and then calculating the ratio between the
amplitude, power or any transformation f of this sum towards the
sum of the amplitudes, power or any other transformation f of each
of the signals derived from the T and A:
f ( T + k A A ) f ( T ) + f ( k A A ) or f ( T ' + k A A ' ) f ( T
' ) + f ( k A A ' ) . ##EQU00006##
[0170] In embodiment 12, a method is provided for further
stabilizing the amplitude of the signals derived by embodiments 1,
2, or 3, by first calculating the V.sub.S, then based on the
V.sub.S, calculate an integrated volume (V.sub.l) by summing the
tidal volumes from the V.sub.S over a period of time, and for the
same period of time monitor the average heart rate (HR) signal.
Then adjust the absolute amplitude of V.sub.S, by using the
approximation that the ratio of V.sub.l towards HR is close to
constant for all periods.
[0171] In embodiment 13, the method described in embodiment 12 is
applied by using HR signal derived by isolating the cardiac
component from the T or A signals.
[0172] In embodiment 14, the method described in embodiment 12 is
applied by using HR signal derived from pulse Oximeter or ECG
electrodes.
[0173] Additionally, according to the present disclosure, the
following embodiments , the following embodiments and combinations
of embodiments are provided:
[0174] In embodiment 15, a method is provided for evaluating the
ratio of the signal loss S.sub.l in the calibrated sum by first
applying the methods of embodiments 1, 2, or 3, above, to evaluate
k.sub.A, then using the k.sub.A to create a weighted sum between
signals based on T and A and then calculating the ratio between the
amplitude, power or any transformation f of this sum towards the
sum of the amplitudes, power or any other transformation f of each
of the signals derived from the T and A:
f ( T ' + k A A ' ) f ( T ) + f ( k A A ) . ##EQU00007##
[0175] In embodiment 16, further to any of the embodiments above or
below, a model is used based on plurality of parameters derived
from RIP signals to predict respiratory effort in a patient.
[0176] In embodiment 17, further to any of the embodiments above or
below, any or all of the following parameters are used to build a
respiratory effort model, measured from the RIP signals, Power
Loss, Inhalation time, Exhalation time, Inhalation volume,
Exhalation volume, Exhalation flow, Inhalation flow, Respiratory
Rate, Total volume over certain time, such as minute volume, shape
divergence between belts, phase between belts and/or time shift
between belts.
[0177] In embodiment 18, further to any of the embodiments above or
below, breaths are selected for patient normalization and using a
subset or all of the above mentioned parameters to create a
respiratory effort scale comparable between patients.
[0178] In embodiment 19, further to any of the embodiments above or
below, general patient information is used, such age, weight,
height or other body measures and/or conditions to further improve
the respiratory effort measure.
[0179] In embodiment 20, further to any of the embodiments above or
below, time periods of constant calibration are identified by
looking for sudden changes in any or all of the above mentioned
parameters.
[0180] Each of the methods described herein, or any combination of
the above-described methods may be implemented by a respiratory
effort measuring system that includes a first sensor device
configured to obtain a thorax effort signal (T) and a second sensor
device configured to obtain an abdomen effort signal (A). As above,
the thorax effort signal (T) being an indicator of a thoracic
component of the respiratory effort. And as above, the abdomen
effort signal (A) being an indicator of an abdominal component of
the respiratory effort. The system may include a memory storage
configured to store data of each of the thorax effort signal (T)
and the abdomen effort signal (A). The system may further include a
processor, such as one of more hardware processor devices. For
example, the processor may be one or more processors of a computer
or a terminal device. The processor of the system is configured to
receive the thorax effort signal (T) and the abdomen effort signal
(A).
[0181] Further, the processor is configured to obtain a respiratory
flow (F) of the subject. The respiratory flow (F) of the subject
may be measured directly by a sensor or device of the system, such
as respiratory flow sensor included in the respiratory effort
measuring system. For example, the respiratory effort measuring
system may include a cannula flow sensor that directly measures the
respiratory flow (F) of the subject. For example, the system may
include one or more pneumo flow sensors. Or alternatively, the
respiratory flow (F) may be determined or derived based on other
obtained signals, such as effort bands or belts placed on the
subject. For example, the respiratory flow (F) may be determined
from RIP signals, as described herein. For example, a respiratory
flow (F) may be obtained by deriving the respiratory flow (F) from
a calibrated RIP signal sum. Or alternatively, a respiratory flow
(F) may be obtained by deriving the respiratory flow (F) from
non-calibrated RIP signals. Such derivation of the respiratory flow
(F) may further be performed by the processor of the system.
[0182] In another embodiment, a hardware storage device is
provided. Such a storage device may be any hardware device that is
used for storing, porting and extracting data files and objects,
which can hold and store information both temporarily and
permanently. The storage device may be internal or may be external
to a computer, server or any similar computing device. The hardware
storage device has stored thereon computer executable instructions
which, when executed by one or more processors, implement a method
of measuring respiratory effort of a subject. The method includes
obtaining a thoracic effort signal (T), obtaining an abdomen effort
signal (A), and obtaining a respiratory flow (F). The thoracic
effort signal (T) is an indicator of a thoracic component of the
respiratory effort. The abdomen effort signal (A) is an indicator
of an abdominal component of the respiratory effort. The method
further includes determining the respiratory effort by adjusting
the components of a model of the respiratory system based on the
thoracic effort signal (T), the abdomen effort signal (A), or the
respiratory flow (F), or any combination of the thoracic effort
signal (T), the abdomen effort signal (A), and the respiratory flow
(F).
[0183] Certain terms are used throughout the description and claims
to refer to particular methods, features, or components. As those
having ordinary skill in the art will appreciate, different persons
may refer to the same methods, features, or components by different
names. This disclosure does not intend to distinguish between
methods, features, or components that differ in name but not
function. The figures are not necessarily to scale. Certain
features and components herein may be shown in exaggerated scale or
in somewhat schematic form and some details of conventional
elements may not be shown or described in interest of clarity and
conciseness.
[0184] Although various example embodiments have been described in
detail herein, those skilled in the art will readily appreciate in
view of the present disclosure that many modifications are possible
in the example embodiments without materially departing from the
concepts of present disclosure. Accordingly, any such modifications
are intended to be included in the scope of this disclosure.
Likewise, while the disclosure herein contains many specifics,
these specifics should not be construed as limiting the scope of
the disclosure or of any of the appended claims, but merely as
providing information pertinent to one or more specific embodiments
that may fall within the scope of the disclosure and the appended
claims. Any described features from the various embodiments
disclosed may be employed in combination. In addition, other
embodiments of the present disclosure may also be devised which lie
within the scopes of the disclosure and the appended claims. Each
addition, deletion, and modification to the embodiments that falls
within the meaning and scope of the claims is to be embraced by the
claims.
[0185] Certain embodiments and features may have been described
using a set of numerical upper limits and a set of numerical lower
limits. It should be appreciated that ranges including the
combination of any two values, e.g., the combination of any lower
value with any upper value, the combination of any two lower
values, and/or the combination of any two upper values are
contemplated unless otherwise indicated. Certain lower limits,
upper limits and ranges may appear in one or more claims below. Any
numerical value is "about" or "approximately" the indicated value,
and takes into account experimental error and variations that would
be expected by a person having ordinary skill in the art.
* * * * *