U.S. patent application number 15/934289 was filed with the patent office on 2018-07-26 for systems and methods for determining patient effort and/or respiratory parameters in a ventilation system.
The applicant listed for this patent is Covidien LP. Invention is credited to Milenko Masic.
Application Number | 20180207379 15/934289 |
Document ID | / |
Family ID | 40983539 |
Filed Date | 2018-07-26 |
United States Patent
Application |
20180207379 |
Kind Code |
A1 |
Masic; Milenko |
July 26, 2018 |
SYSTEMS AND METHODS FOR DETERMINING PATIENT EFFORT AND/OR
RESPIRATORY PARAMETERS IN A VENTILATION SYSTEM
Abstract
Various embodiments of the present disclosure provide systems,
methods and devices for respiratory support. As one example, a
method for respiratory support is described that includes measuring
a pressure, providing a measured pressure, measuring an inlet flow
and an outlet flow, and providing a measured net flow. A
relationship between a first value related to the measured
pressure, a second value related to the measured net flow and a
third value related to patient effort is used to provide a
prediction of patient effort. An interim value is updated based at
least in part on the prediction of the patient effort.
Inventors: |
Masic; Milenko; (Carlsbad,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Covidien LP |
Mansfield |
MA |
US |
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|
Family ID: |
40983539 |
Appl. No.: |
15/934289 |
Filed: |
March 23, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14455472 |
Aug 8, 2014 |
9925345 |
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15934289 |
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12479230 |
Jun 5, 2009 |
8826907 |
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14455472 |
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61059599 |
Jun 6, 2008 |
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61101575 |
Sep 30, 2008 |
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61101578 |
Sep 30, 2008 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61M 16/0057 20130101;
A61M 16/026 20170801; A61M 2205/50 20130101; A61B 5/08 20130101;
G01F 1/34 20130101; A61M 16/0003 20140204; A61M 2205/3303 20130101;
G01F 1/00 20130101; A61M 2016/0036 20130101; A61M 2016/0027
20130101; A61M 16/0051 20130101; A61M 2016/0018 20130101; A61M
2016/0039 20130101; A61M 16/12 20130101; A61M 2016/0015 20130101;
A61M 2016/0042 20130101 |
International
Class: |
A61M 16/00 20060101
A61M016/00; A61M 16/12 20060101 A61M016/12; A61B 5/08 20060101
A61B005/08; G01F 1/00 20060101 G01F001/00; G01F 1/34 20060101
G01F001/34 |
Claims
1. (canceled)
2. A method for respiratory support, the method comprising:
measuring a pressure; measuring an inlet flow and an outlet flow;
based on the inlet flow and the outlet flow, calculating a net
flow; predicting patient effort based at least in part on a
relationship between a first value related to the measured
pressure, a second value related to the net flow, and a third value
related to patient effort, wherein the third value related to
patient effort represents at least one unknown value, and wherein
the estimate of patient effort is determined based on the unknown
value being characterized by a smooth signal; and based at least in
part on the prediction of patient effort, updating an interim value
that was previously used to calculate patient effort.
3. The method of claim 2, wherein the third value related to
patient effort is an actual patient effort.
4. The method of claim 2, wherein the third value related to
patient effort is a derivative of patient effort.
5. The method of claim 2, wherein the method further comprises:
calculating patient effort based at least in part on the interim
value.
6. The method of claim 5, wherein calculating the patient effort
includes estimating patient effort based on a combination of one or
more values selected from a group consisting of: an estimated
normalized prediction error, a filtered pressure value, a
regression vector, and a current estimated value of a parameter
vector.
7. The method of claim 2, wherein the interim value is a time
dependent signal.
8. The method of claim 2, wherein the interim value includes a
respiratory parameter set, and wherein the respiratory parameter
set is a vector including a combination of one or more respiratory
parameters selected from: lung compliance, patient resistance,
tubing compliance, and leakage.
9. The method of claim 2, wherein the interim value includes a time
dependent estimator of patient effort.
10. The method of claim 9, wherein the time dependent estimator of
the patient effort is an estimate of patient effort.
11. The method of claim 10, wherein the estimate of patient effort
is based on a combination of one or more values selected from a
group consisting of: an estimated normalized prediction error, a
filtered pressure value, a regression vector, and a current
estimated value of a parameter vector.
12. The method of claim 2, wherein the relationship is a
parameterized system input to output relationship.
13. The method of claim 12, wherein the parameterized system input
to output relationship is a regression form:
z=.THETA..phi.+.phi..sub.d, wherein z is a filtered pressure value,
.THETA. is a current estimated value of a parameter vector, .phi.
is at least a part of a regression vector, and .phi..sub.d is a
filtered patient effort.
14. The method of claim 12, wherein the parameterized system input
to output relationship is derived from a transfer function.
15. The method of claim 14, wherein the transfer function is
derived from a model comprising: [ ? ? ] = [ - 1 C T R P 1 C T R P
1 C L R P - 1 C L R P ] [ p Y p L ] + [ 1 C T 1 C T - 1 C T 0 0 0 ]
[ q AIR q O 2 q E ] + [ 0 - 1 C T 0 1 0 - 1 C T ] [ ? q Tleak q
Pleak ] ? indicates text missing or illegible when filed
##EQU00033## wherein p.sub.y is a value for a wye gas pressure,
p.sub.L is a value of a pressure in a patient's lungs, {dot over
(p)}.sub.y is a value for a first derivative of a wye gas pressure,
{dot over (p)}.sub.L is a value for a first derivative of pressure
in the patient's lungs, C.sub.T is a value for a tubing compliance,
C.sub.L is a value for a lung compliance, R.sub.P is a value for a
patient resistance, q.sub.air is a value for an inlet flow of air,
q.sub.02 is a value for an inlet flow of oxygen, q.sub.E is a value
for an outlet gas flow, q.sub.Tleak is a value for gas flow
associated with tubing leakage, q.sub.Pleak is a value for gas flow
associated with leakage in the patient, and {dot over (p)}.sub.p is
a value for the derivative of patient effort.
16. The method of claim 2, wherein an estimate of the prediction
patient effort is calculated using the approximation:
.phi..sub.d.apprxeq.e.sup.-sdt(z-.THETA..sup.T.phi.) wherein
.phi..sub.d is a filtered patient effort, z is a filtered pressure
value, .THETA..sup.T is a current estimated parameter vector, .phi.
is at least a part of regression vector, t is a variable of time,
and s is a spatial variable.
17. A ventilation system, the ventilation system comprising: a
processor communicably coupled to a computer readable medium,
wherein the computer readable medium includes instructions
executable by the processor to: receive a pressure; calculate a net
flow based at least in part on an inlet flow and an outlet flow;
predict patient effort based at least in part on a relationship
between a first value related to the pressure, a second value
related to the net flow, and a third value related to patient
effort, wherein the third value related to patient effort
represents at least one unknown value, and wherein the estimate of
patient effort is determined based on the unknown value being
characterized by a smooth signal; and based at least in part on the
prediction of patient effort, update an interim value previously
used to calculate patient effort.
18. The ventilation system of claim 17, wherein the computer
readable medium further includes instructions executable by the
processor to: calculate a patient effort value based at least in
part on the interim value.
19. A method for respiratory support, the method comprising:
receiving a parameter set of one or more parameters; measuring a
pressure; determining a net flow; predicting patient effort based
at least in part on a relationship between a first value related to
the measured pressure, a second value related to the net flow, and
a third value related to patient effort, wherein the third value
related to patient effort represents at least one unknown value,
and wherein the estimate of patient effort is determined based on
the unknown value being characterized by a smooth signal; and based
at least in part on the prediction of patient effort, updating an
interim value previously used to calculate patient effort.
20. The method of claim 19, wherein the parameter set comprises a
parameter related to a circuit resistance.
21. The method of claim 19, wherein the parameter set comprises a
parameter related to a circuit compliance.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 61/059,599, filed Jun. 6, 2008, titled "Systems and
Methods for Determining Patient Effort and/or Respiratory
Parameters in a Ventilation System," the benefit of U.S.
Provisional Application No. 61/101,575, filed Sep. 30, 2008, titled
"Systems and Methods for Monitoring and Displaying Respiratory
Information," and the benefit of U.S. Provisional Application No.
61/101,578, filed Sep. 30, 2008, also titled "Systems and Methods
for Monitoring and Displaying Respiratory Information," and whereby
the complete disclosure of each such application is hereby
incorporated by reference.
BACKGROUND
[0002] The present invention is related to ventilators, and more
particularly to systems and methods for identification of time
dependent signals and/or respiratory parameters in a dynamic
ventilation system.
[0003] Ventilators are designed to ventilate a patient's lungs with
gas, and to thereby assist the patient when the patient's ability
to breathe on their own is somehow impaired. Ventilation is
achieved by providing a defined gas mixture to the patient
according to a prescribed ventilation modality. As each patient may
require a different ventilation strategy, modern ventilators can be
customized for the particular needs of an individual patient.
[0004] Modern ventilators are dynamic systems whose dynamic
behavior and outputs, such as pressures and flows delivered to the
patient, are driven by input signals, such as gas flows. Proper
operation of such ventilators relies on some understanding of a
variety of respiratory parameters including the resistance of the
patient airways and the compliance of the lung. These parameters
may vary significantly from one ventilation system to another, and
from one patient to another. In many cases, proper operation of a
ventilation system is limited by the accuracy at which such
parameters are defined or estimated.
[0005] Methods for identifying the ventilation parameters for a
particular individual or a particular ventilation situation have
been developed. Such methods can be divided into two different
categories: static methods and dynamic methods. In static methods,
respiratory parameters are typically estimated during short periods
of induced equilibrium states (i.e., maneuvers) of the system using
only a few measurements of quantities that are related to the
estimated parameters. In contrast, dynamic methods operate to
describe the dynamic behavior of the patient under ventilation, and
are typically based on continuous or segmented continuous
measurement of ventilator conditions. Historically, identifying
respiratory parameters posed a challenge in the case of the
ventilation system driven by unknown input signals. This is the
case with the ventilation systems involving actively breathing
patients and leaks, and many existing approaches fail to provide
sufficiently accurate results because these signals driving the
system typically cannot be measured but they must be accounted for
in the identification algorithms. For example, various approaches
for estimating patient breathing effort are inaccurate, and as such
dynamic methods relying on an estimated patient effort are often
inadequate.
[0006] In some cases, patient breathing effort has been estimated
using the equation of motion, and relying exclusively on the
measurement of gas flow in and out of the patient's lungs along
with a pressure measurement. The reliability of such an approach is
limited by the accuracy at which gas flow in and out of the
patient's lungs may be measured. Such a measurement, however, is
inherently inaccurate as it relies on a flow sensor at or near a
tube inserted in the patient's trachea. The accuracy of the flow
sensor is substantially reduced due to the humidity of gas exhaled
from the lung. Further, such a flow sensor near the patient's
trachea is often not available in existing ventilation systems.
[0007] Hence, there exists a need in the art for advanced
ventilation systems, and methods for using such.
BRIEF SUMMARY
[0008] The present disclosure is related to ventilators, and more
particularly to systems and methods for identification of time
dependent signals and/or respiratory parameters in a dynamic
ventilation system.
[0009] Various embodiments of the present disclosure provide
methods for respiratory support. Some of such methods include
measuring a pressure, providing a measured pressure, measuring an
inlet flow and an outlet flow, and providing a measured net flow. A
relationship between a first value related to the measured
pressure, a second value related to the measured net flow and a
third value related to patient effort is used to provide a
prediction of patient effort. An interim value is updated based at
least in part on the prediction of the patient effort. In some
instances of the aforementioned embodiments, the methods further
include calculating the patient effort based at least in part on
the interim value. In some cases, calculating the third value
related to patient effort includes estimating patient effort based
on a combination of one or more of an estimated normalized
prediction error ( ) a filtered pressure value (z), and/or a
regression vector (.phi..sup.T). The first value may be, but is not
limited to, a filtered version of the measured pressure or the
actual measured pressure. The second value may be, but is not
limited to, a filtered version of the measured net flow or the
actual measured net flow. The third value may be, but is not
limited to, an actual patient effort or a derivative of patient
effort.
[0010] In some cases, the interim value includes a time dependent
signal, and/or a respiratory parameter. The respiratory parameter
may be, but is not limited to, lung compliance (C.sub.L), patient
resistance (R.sub.P), tubing compliance (C.sub.T), and leakage
(.lamda..sub.LEAK). In particular instances of the aforementioned
embodiments, the interim value includes a time dependent estimator
of the patient effort. The time dependent estimator of the patient
effort may be an estimate of the patient effort. Such an estimate
of the patient effort may be based on any of an estimated
normalized prediction error ( ), a filtered pressure value (z), a
regression vector (.phi..sup.T), and/or some combination of the
aforementioned variables.
[0011] The above mentioned relationship may be a linear regression,
z=.THETA..sup.T.phi.+.phi..sub.d, derived from a transfer function.
In one case, the transfer function is derived from the following
dynamic model of the system:
[ p . Y p . L ] = [ - 1 C T R P 1 C T R P 1 C L R P - 1 C L R P ] [
p Y p L ] + [ 1 C T 1 C T - 1 C T 0 0 0 ] [ q AIR q O 2 q E ] + [ 0
- 1 C T 0 1 0 - 1 C T ] [ p . p q Tleak q Pleak ] ##EQU00001##
In some cases, the prediction of the filtered patient effort is
calculated using the approximation:
.phi..sub.d.apprxeq.e.sup.-sdt(z-.THETA..sup.T.phi.).
[0012] Other embodiments of the present invention provide
ventilation systems that include a processor communicably coupled
to a computer readable medium. The computer readable medium
includes software or firmware that is executable by the processor
to receive a pressure, receive an inlet flow, and receive an outlet
flow. The instructions are further executable to calculate a net
flow based at least in part on the inlet flow and the outlet flow,
and to use a relationship between a first value related to the
pressure, a second value related to the net flow, and a third value
related to patient effort to provide a prediction of patient
effort. An interim value based at least in part on the prediction
of the patient effort is updated. In some embodiments, a patient
effort is calculated based at least in part on the interim
value.
[0013] Yet other embodiments provide methods for respiratory
support that include receiving a parameter set of one or more
parameters, measuring a pressure and providing a measured pressure,
measuring an inlet flow and an outlet flow, and providing a
measured net flow. A relationship between a first value related to
the measured pressure, a second value related to the measured net
flow and the parameter set is used to provide a prediction of
patient effort. An interim value is updated based at least in part
on the prediction of the patient effort. In some embodiments, the
parameter set includes a circuit resistance, a circuit compliance,
or other patient or circuit-related parameters.
[0014] Yet a further embodiment of the present disclosure provides
a patient ventilator including a gas inlet, a gas outlet, a tube
coupling the gas inlet and the gas outlet, a pressure sensor that
is operable to provide a measured pressure value indicating a
pressure in the tube, and two flow sensors. One of the flow sensors
is operable to provide an inlet flow value indicating a flow
associated with the gas inlet, and the other flow sensor is
operable to provide an outlet flow value indicating a flow
associated with the gas outlet. The patient ventilator further
includes a processor communicably coupled to a computer readable
medium. The computer readable medium includes software or firmware
that is executable by the processor to receive a pressure, receive
an inlet flow, and receive an outlet flow. The instructions are
further executable to calculate a net flow based at least in part
on the inlet flow and the outlet flow, and to use a relationship
between a first value related to the pressure, a second value
related to the net flow, and a third value related to patient
effort to provide a prediction of patient effort. An interim value
based at least in part on the prediction of the patient effort may
be updated.
[0015] Additional embodiments of the present invention provide
methods for respiratory support that include measuring a pressure
to generate a measured pressure, measuring a flow to generate a
measured flow, and using the dynamic model:
[ p . Y p . L ] = [ - 1 C T R P 1 C T R P 1 C L R P - 1 C L R P ] [
p Y p L ] + [ 1 C T 1 C T - 1 C T 0 0 0 ] [ q AIR q O 2 q E ] + [ 0
- 1 C T 0 1 0 - 1 C T ] [ p . p q Tleak q Pleak ] ##EQU00002##
to provide a prediction of a patient effort.
[0016] Yet additional embodiments of the present invention provide
methods for respiratory support that include measuring a pressure
to generate a measured pressure, measuring a flow to generate a
measured flow, using the linear regression
z=.THETA..sup.T.phi.+.phi..sub.d to provide a prediction of a
patient effort, and updating a patient effort estimate based at
least in part on the prediction of the patient effort.
[0017] This summary provides only a general outline of some
embodiments of the invention. Many other objects, features,
advantages and other embodiments of the invention will become more
fully apparent from the following detailed description, the
appended claims and the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] A further understanding of the various embodiments of the
present disclosure may be realized by reference to the figures
which are described in remaining portions of the specification. In
the figures, like reference numerals may be used throughout several
of the figures to refer to similar components. In some instances, a
sub-label consisting of a lower case letter is associated with a
reference numeral to denote one of multiple similar components.
When reference is made to a reference numeral without specification
to an existing sub-label, it is intended to refer to all such
multiple similar components.
[0019] FIG. 1 depicts a ventilation system including, among other
things, an adaptive calculation module capable of providing
adaptively estimated respiratory parameters and patient effort in
accordance with various embodiments of the present invention;
[0020] FIG. 2 shows a patient ventilator system and associated
parameterized model that may be used for determining patient effort
in accordance with some embodiments of the present invention;
[0021] FIG. 3 provides a graphical example of patient effort
correlated to other signals that is achievable through
implementation of a particular embodiment of the present
invention;
[0022] FIG. 4 is a flow diagram depicting a method in accordance
with some embodiments of the present invention for determining
patient effort;
[0023] FIG. 5 shows a microprocessor based system for determining
patient effort in accordance with various embodiments of the
present invention;
[0024] FIG. 6 is a flow diagram depicting a method in accordance
with some embodiments of the present invention for triggering a
ventilation cycle;
[0025] FIG. 7 is a timing diagram showing triggering a ventilation
cycle based upon an estimated patient effort signal in accordance
with various embodiments of the present invention;
[0026] FIG. 8 are timing diagrams comparing a process triggering
off of a pressure sensor verses triggering off of an estimated
patient effort signal in accordance with one or more embodiments of
the present invention;
[0027] FIG. 9 is a flow diagram showing a method for providing
ventilation in proportion to patient effort in accordance with
various embodiments of the present invention;
[0028] FIG. 10 illustrates a group of timing diagrams that
graphically depict providing ventilation in proportion to patient
effort in accordance with one or more embodiments of the present
invention;
[0029] FIG. 11 shows an exemplary graphical interface showing the
display of patient effort corresponding to an actively breathing
patient in accordance with some embodiments of the present
invention; and
[0030] FIG. 12 shows an exemplary graphical interface showing the
display of a respiratory parameter corresponding to an actively
breathing patient in accordance with some embodiments of the
present invention.
DETAILED DESCRIPTION
[0031] The present disclosure is related to ventilators, and more
particularly to systems and methods for controlling the delivery of
gas based on a patient's effort to breathe.
[0032] It is desirable to synchronize the onset and end of a
ventilation cycle to effort a patient may be making to breathe on
their own {i.e., patient effort). For example, it is desirable to
have an accurate ventilator trigger, whereby the ventilator
initiates a breath as soon as the patient attempts to inhale. Some
ventilators use a pressure trigger which senses a change in
ventilation circuit pressure caused by the patient attempting to
inhale, while other ventilators use a flow trigger which senses a
change in flow caused by the patient attempting to inhale. In
either case, delays between the patient's effort and the ventilator
response can occur due to a variety of reasons. For example, a leak
in the ventilation circuit may allow air to enter the circuit when
the patient inhales. Since the entirety of the patient breath is
not measured by a ventilator flow sensor, and the ventilator may be
monitoring a change in flow to detect an inhalation (flow trigger),
the ventilator may be delayed in initiating the breath. Some
embodiments of the present invention facilitate improved
synchronization through providing a reasonably accurate estimate of
patient effort that may be used either alone or in relation to
other signals to trigger the onset and end of a ventilation cycle.
In one or more embodiments of the present invention, the estimated
patient effort may be additionally used in relation to controlling
proportional ventilation of a patient. Such proportional
ventilation operates to deliver a gas to a patient in proportion to
the patient's effort to receive such gas. In various embodiments of
the present invention, the estimated patient effort and/or
respiratory parameters may be used to drive a graphical display
that may be used by a clinician for patient monitoring and/or
diagnostic purposes.
[0033] Various embodiments of the present disclosure provide
systems and methods for estimating of one or more respiratory
parameters and at least one un-measured input signal driving a
ventilation system with a reasonable degree of accuracy. In some
embodiments, at least one un-measured input signal may be derived
from measured input signals, such as measured pressure and measured
flow, and used to estimate the respiratory parameters. The
un-measured input signal may be, but is not limited to, patient
effort and/or a derivative of patient effort, a ventilation system
gas leak (i.e., a leak occurring in the tubing or patient interface
connecting a ventilator to a patient), a patient gas leak (e.g., a
leak in the patient's lung), and/or flow and pressure sensing
errors. The respiratory parameters may include, but are not limited
to, lung compliance (C.sub.L), patient resistance (R.sub.P), and
tubing compliance (C.sub.T). In some cases, estimation of both
respiratory parameters and the un-measured input signal(s) is
simultaneous. In some embodiments, the un-measured input signal has
a strong correlation to patient effort, and therefore can be used
as a surrogate for patient effort in subsequent ventilator actions.
In other embodiments, methods of the present invention allow the
respiratory parameters to be continuously provided. In this manner,
patient effort may be determined, as well as respiratory or
ventilation system parameters such as lung compliance, patient
resistance, leak, etc.
[0034] In some embodiments of the present invention, a relationship
between measurable pressure, measurable flow and an unknown patient
effort is exploited to provide a continuous estimate of patient
effort along with a variety of respiratory parameters. In
particular instances, the relationship is defined as a transfer
function relating, inter cilia, measured pressure, measured flow
and patient effort. In such cases, the transfer function may be
reduced using linear regression techniques to yield one or more
interim values that may in turn be used to estimate patient effort.
In an embodiment, ongoing inputs of measured pressure and measured
flow are plugged into the transfer function to estimate patient
effort and, as needed, one or more respiratory parameters. In
another embodiment, the estimate of patient effort may be used
recursively to derive a more accurate estimate of patient effort
during succeeding calculation periods. Thus, through use of
recursion, the accuracy of an estimated patient effort value may be
continuously improved.
[0035] In some cases, the measured flow is a net flow value that
combines a net flow of gas out of the system with a net flow of gas
into the system. In one particular case, the net flow of gas into
the system includes a flow of Oxygen combined with a flow of Air
into the system. Such flows are reasonably easy to measure, and are
not subject to the inaccuracies that often attend the measurement
of gas flow near the lung.
[0036] In some cases, a patient effort signal or some proxy thereof
calculated as described above may be used to trigger a ventilation
cycle. Use of such signals can allow a ventilation system to more
accurately synchronize mechanical ventilation with the efforts
being made by a patient to breathe on their own.
[0037] Of note, the respiratory parameters and the derivative of
patient effort may be inputs to the same model, and may be
calculated using interdependent equations derived from that same
model. As the values calculated from some of the interdependent
equations are used as inputs to other interdependent equations,
they may be generically referred to as interim values. As used
herein, the phrase "interim value" is used in its broadest sense to
mean a value derived from one equation that is used as an input to
another equation. It will be noted based on reading this disclosure
that a variety of interim values may be utilized in relation to the
various embodiments of the present invention.
[0038] Turning to FIG. 1, a ventilation system 1 is shown in
accordance with various embodiments of the present invention.
Ventilation system I includes a ventilator 10, an adaptive
calculation module 20, a graphical user interface 40, and a
proportional and triggering control module 30. Ventilator 10 may be
any ventilator known in the art that is capable of providing a
measured pressure 65, a measured inlet flow 70 and a measured
outlet flow 75. Adaptive calculation module 20 receives pressure
65, inlet flow 70 and outlet flow 75 and calculates an estimated
patient effort 55 and estimated respiratory parameters 60. Patient
effort 55 may be patient effort itself or some signal that is
strongly correlated to patient effort. Signals correlated to
patient effort are more fully discussed below. Respiratory
parameters 60 may include a variety of parameters that are more
fully described below. In an embodiment, the calculations performed
by adaptive calculation module 20 may be adaptive in nature relying
on previous interim values to generate updated respiratory
parameters 60 and patient effort 55 estimates. In some embodiments,
such interim values may include the patient effort 55 and/or the
respiratory parameter estimates 60 as shown by dashed lines in FIG.
1. Alternatively (not shown), the previous interim values used by
adaptive calculation module 20 may be composite parameters that do
not directly correspond to any identifiable respiratory parameter
(such as, for example, the covariance matrix and parameter vector
discussed in greater detail below).
[0039] In the embodiment illustrated, patient effort 55 is provided
to proportional and triggering control module 30. Based on patient
effort 55, proportional and triggering control module 30 generates
one or more control signals 80 that are provided to ventilator 10.
In some embodiments, control signals 80 control the timing of gas
delivery to a patient. In various embodiments, control signals 80
control the amount of gas to be delivered to a patient, where the
amount of gas is in proportion to patient effort 55.
[0040] Ventilator 10 provides control signals 90 that drive
graphical user interface 40. Graphical user interface 40 may be
included as part of ventilator 10 to allow for interaction with a
user including, but not limited to, receiving user commands and/or
displaying data relevant to ventilator operation. In some
embodiments, ventilator 10 may direct graphical user interface 40
to display information 85 provided by adaptive calculation module
20. Such information may include, but is not limited to,
respiratory parameters 60 and/or patient effort 55 as is more fully
discussed below.
[0041] Various embodiments of the present invention utilize a
parameterized dynamic model of a patient ventilator system to
determine patient effort. A model of a ventilator system 100 is
depicted in FIG. 2. Ventilator system 100 includes an inlet air
flow 105 (q.sub.AIR), an inlet Oxygen flow 110 (q.sub.O2), and an
outlet gas flow 115 (q.sub.E). It should be noted that while
ventilator system 100 shows two gas sources, Air and Oxygen, more
or fewer inlet gas sources may be used in relation to different
embodiments of the present invention. For example, it may be that
only an Air source is used, or that in addition to the inlet Air
source and the inlet Oxygen source, a Helium and/or Heliox source
may be included. Based on the disclosure provided herein, one of
ordinary skill in the art will recognize a variety of other gas
sources that may be used in relation to different embodiments of
the present invention.
[0042] Tubing, flow valves, and/or pressure monitors included in
the system introduce some resistance to gas flow in ventilator
system 100. In particular, an air resistance 120 (R.sub.air), an
Oxygen resistance 125 (R.sub.O2), an exhalation resistance 130
(R.sub.EV), and a patient resistance 135 (R.sub.P) (i.e., some
combination of trachea resistance and resistance in an endotracheal
tube) are possible. A pressure sensor 150 measures the pressure
(p.sub.I) at the inlet at a location where the air flow and Oxygen
flow is combined, and a pressure sensor 155 measures the pressure
(p.sub.E) in an exhalation output. It should be noted that pressure
sensor 150 may be replaced by individual pressure sensors
associated with respective inlet lines. The pressure (p.sub.Y) at a
location where inlet and outlet gases combine is represented as a
baffles 140 (e.g., wye gas pressure), and the pressure (p.sub.L) in
the patient's lungs is represented by another baffles. In some
embodiments of the present invention, p.sub.Y is determined though
use of a pressure measurement device mounted at or near the
particular location corresponding to the pressure. In other
embodiments of the present invention, p.sub.Y is set equal to
either p.sub.I or P.sub.E, while in other embodiments of the
present invention, p.sub.Y is set to the average of p.sub.I and
p.sub.E. In any of the aforementioned three cases, p.sub.Y is
considered to be "directly measured" as it is either a measurement
or is an average of other direct measurements. A gas flow
associated with a leakage 160 (q.sub.Tleak) in the tubing, and a
gas flow associated with a leakage 165 (q.sub.Pleak) in the patient
are also identified. A patient effort value 195 (p.sub.P) is shown
as a force interacting with the force of moving gas in and out of a
patient's lung.
[0043] Various equations may be used to describe the operation of
ventilator system 100. For example, using the principle of
conservation of mass, the various flow values (i.e., q.sub.AIR,
q.sub.O2, q.sub.T, q.sub.Tleak, q.sub.P, q.sub.Pleak, q.sub.LUNG,
q.sub.1) may be combined to yield the following three
equations:
q.sub.LUNG=q.sub.p-q.sub.Pleak;
q.sub.I-q.sub.P-q.sub.E=0; and
q.sub.AIR+q.sub.O2=q.sub.1+q.sub.Tleak+q.sub.T.
[0044] Further, using the principle of equilibrium of forces, the
pressures p.sub.Y, P.sub.L and p.sub.p, and flows q.sub.T and
q.sub.L can be combined in the following relationships:
p Y = 1 C T .intg. q T dt , or p . Y = 1 C T q T ; and ##EQU00003##
p p - p L = 1 C L .intg. q L dt , or p . L = p . p - 1 C L q L .
##EQU00003.2##
Finally, the relationship between pressure and flow can be used to
derive the following equation based on ventilator system 100:
p.sub.Y-p.sub.L=R.sub.Pq.sub.P.
[0045] By algebraically manipulating the aforementioned equations
derived from ventilator system 100 and recasting the equations in a
matrix form, the following parameterized model 190 is developed to
characterize the operation of ventilator system 100 of FIG. 2:
[ p . Y p . L ] = [ - 1 C T R P 1 C T R P 1 C L R P - 1 C L R P ] [
p Y p L ] + [ 1 C T 1 C T - 1 C T 0 0 0 ] [ q AIR q O 2 q E ] + [ 0
- 1 C T 0 1 0 - 1 C T ] [ p . P q Tleak q Pleak ] ,
##EQU00004##
where {dot over (p)}.sub.Y is the first derivative of the pressure
measured at the tubing branch, {dot over (p)}.sub.L is the first
derivative of the pressure in the patient's lung, {dot over
(p)}.sub.P is the first derivative of the patient effort, C.sub.T
represents tubing compliance, and C.sub.L represents lung
compliance. It should be noted that where more or fewer inlet gases
are utilized, that parameterized model 190 may be modified to
account for the different gases in accordance with other
embodiments of the present invention.
[0046] Various embodiments of the present invention utilize
parameterized model 190 to determine patient effort, p.sub.P. In
different embodiments of the present invention, assumptions may be
made to simplify the calculation. In one particular embodiment of
the present invention, leakage 160 may be assumed to exhibit the
following linear relationship between the tubing leak flow and the
pressure drop across an opening:
q Tleak = 1 R LEAK p y = .lamda. LEAK p y . ##EQU00005##
[0047] It should be noted that in other embodiments of the present
invention, other assumptions about the relationship between the
tubing leak flow and the pressure drop across an opening may be
used. Relying on the aforementioned linear assumption for the
tubing leak flow, parameterized model 190 may be reduced to the
following model:
[ p . Y p . L ] = [ - 1 C T R P - .lamda. Tleak C T 1 C T R P 1 C L
R P - 1 C L R P ] [ p Y p L ] + [ 1 C T 1 C T - 1 C T 0 0 0 ] [ q
AIR q O 2 q E ] + [ 0 0 1 - 1 C L ] [ p . P q Pleak ] .
##EQU00006##
Based on the aforementioned parameterized model, the transfer
function for p.sub.Y is defined as follows:
p Y ( s ) = b q ( s ) a ( s ) ( q AIR ( s ) + q O 2 ( s ) - q E ( s
) ) + b P p ( s ) a ( s ) p . P ( s ) + b Pleak ( s ) a ( s ) q
Pleak ( s ) = b q ( s ) a ( s ) q N ( s ) + b P p ( s ) a ( s ) p .
P ( s ) + b Pleak ( s ) a ( s ) q Pleak ( s ) , ##EQU00007##
where the instantaneous sum of each of the measured flows (e.g.,
q.sub.AIR+q.sub.O2-q.sub.E) is denoted q.sub.N for net flow.
b q ( s ) a ( s ) q N ( s ) ##EQU00008##
represents a transfer function from the net flow (q.sub.N) to the
output (p.sub.Y),
b P p ( s ) a ( s ) p . P ( s ) ##EQU00009##
represents a transfer function from the derivative of patient
effort ({dot over (p)}.sub.P) to the output (p.sub.Y), and
b Pleak ( s ) a ( s ) q Pleak ( s ) ##EQU00010##
represents a transfer function from patient leakage (q.sub.Pleak)
to the output (p.sub.Y). It should be noted that the first term in
the preceding transfer function (i.e., the q.sub.N term) is a
transfer function related to a known, measured value, and the
second term in the preceding transfer function (i.e., the p.sub.P
term) is a transfer function related to an unknown, adaptively
estimated value. In some embodiments of the present invention, the
third term (i.e., the q.sub.Pleak term is assumed to be zero for
the sake of simplification. Again, using the above mentioned
parameterized model, the relationships between the transfer
function coefficients and the system parameters are as follow:
a ( s ) = s 2 + C L + C T + C L R P .lamda. Tleak C L C T R P s +
.lamda. Tleak C L C T R P = s 2 + a 1 s + a 0 , with ##EQU00011## a
1 = C L + C T + C L R P .lamda. Tleak C L C T R P , a 0 = .lamda.
Tleak C L C T R P ##EQU00011.2## b q ( s ) = 1 C T s + 1 C L C T R
P = b q 1 s + b q 0 , with ##EQU00011.3## b q 1 = 1 C T , b q 0 = 1
C L C T R P ##EQU00011.4## b P p ( s ) = 1 C T R P = b P p 0
##EQU00011.5## b Pleak ( s ) = - 1 C L C T R = b Pleak 0
##EQU00011.6##
[0048] From the forgoing, it is possible to derive a parameterized
output model in a linear regression form. A first step in defining
the parameterized linear regression output model includes defining
an unknown parameter vector such as the following:
.THETA..sup.T=[a.sub.0 a.sub.1 b.sub.q0 b.sub.q1].
[0049] From the unknown parameter model, once estimated, all lumped
parameters of ventilator system 100 (e.g., C.sub.T, C.sub.L,
R.sub.P, and .lamda..sub.LEAK) may be recovered. Through algebraic
manipulation of the transfer function for p.sub.Y may be
represented as:
p Y ( s ) s 2 .LAMBDA. ( s ) = - p Y ( s ) ( a 1 s + a 0 ) .LAMBDA.
( s ) + b q ( s ) q N ( s ) .LAMBDA. ( s ) + b P p ( s ) p , P ( s
) .LAMBDA. ( s ) + b Pleak ( s ) q Pleak ( s ) .LAMBDA. ( s ) .
##EQU00012##
[0050] In this case, the pressure
p Y ( s ) = s 2 .LAMBDA. ( s ) ##EQU00013##
represents pressure p.sub.Y(s) after filtering through a proper
filter,
s 2 .LAMBDA. ( s ) . ##EQU00014##
Such a proper filter relies on a polynomial .LAMBDA.(s) that is the
same or of higher order than s.sup.2 (e.g., s.sup.2, s.sup.3,
s.sup.4 . . . ). By assuming that patient leakage (q.sub.Pleak) is
zero, a compact linear regression form of the input to output
relationship corresponding to parameterized model 190 of
ventilation system 100 is represented as:
z = .THETA. T .PHI. + .PHI. d ##EQU00015## z = p Y ( s ) s 2
.LAMBDA. ( s ) ##EQU00015.2## .THETA. T = [ a 0 a 1 b q 0 b q 1 ]
##EQU00015.3## .PHI. T = [ - p Y ( s ) .LAMBDA. ( s ) - p Y ( s ) s
.LAMBDA. ( s ) q N ( s ) .LAMBDA. ( s ) q N ( s ) s .LAMBDA. ( s )
] ##EQU00015.4## .PHI. d = b P p ( s ) p . P ( s ) .LAMBDA. ( s )
##EQU00015.5##
where z is the output pressure value, .phi..sup.T is the regression
vector representing a collection of known signals, and .phi..sub.d
is filtered patient effort.
[0051] In this case, use of standard linear regression to estimate
the system parameters .THETA..sup.T=[a.sub.0 a.sub.1 b.sub.q0
b.sub.q1] is not possible as .phi..sub.d is unknown. By inspecting
the unknown term
.PHI. d = b P p ( s ) p . P ( s ) .LAMBDA. ( s ) , ##EQU00016##
and understanding that the derivative of patient effort ({dot over
(p)}.sub.P) is a bounded signal, that the filter (.LAMBDA.(s)) is a
stable polynomial, and
b P p ( s ) .LAMBDA. ( s ) ##EQU00017##
is a proper linear filter, it is apparent that the unknown filtered
patient effort (i.e., .phi..sub.d) is a smooth signal. Based on
this understanding, the value of the unknown filtered patient
effort at any time t can be approximated by its value at the time
t-dt, where dt represents an infinitesimal or finite, but small
amount of time:
.phi..sub.d.apprxeq..phi..sub.de.sup.-sdt=e.sup.-sdt(z-.THETA..sup.T.phi-
.).
[0052] In some embodiments of the present invention, dt is five
milliseconds or less. The aforementioned approximation represents a
reasonable guess, or prediction, of the unknown filtered patient
effort signal at time t that may be used in calculating respiratory
parameters, and thereafter in calculating patient effort. This
reasonable guess can be used to determine the predicted value
({circumflex over (z)}) of the system output (z) can be defined in
accordance with the following equation:
{circumflex over
(z)}=.THETA..sup.T.phi.+e.sup.-sdt(z-.THETA..sup.T.phi.)=.THETA..sup.T(.p-
hi.-e.sup.-sdt.phi.)+e.sup.-sdtz.
From this definition, the parametric identification problem can be
solved through formulation of the following problem: Given
.phi.(t),z(t),
find .THETA. = arg min .THETA. J ( z - z ^ ) , ##EQU00018##
where J( ) is a convex (e.g., ( ).sup.2) function of .THETA.. From
this point, one of a number of mathematical solutions may be
applied to resolve the problem. As one example, a modified
recursive least squares method may be used. More detail related to
a non-modified mathematical implementation of such an approach is
more fully described in one or both of (1) Lennart Ljung, "System
Identification, Theory for the User", Second Edition, Prentice
Hall, 1999 (ISBN 0-13-656695-2) and (2) Petros Ioannou and Jing
Sun, Robust Adaptive Control, Prentice Hall, 1995 (ISBN
9780134391007). Both of the aforementioned references are
incorporated herein by reference for all purposes.
[0053] In implementing a modified recursive least squares method, a
prediction error ( ) is first normalized and signals are adopted
for the normalized signals as set forth in the following
equation:
= z - z ^ ( t ) m 2 = z - .THETA. T ( .PHI. - e - s dt .PHI. ) - e
- s dt z m 2 = z ( 1 - e - s dt ) - .THETA. T .PHI. ( 1 - e - s dt
) m 2 = z ~ - .THETA. T .PHI. ~ m 2 ##EQU00019## z ~ = z ( 1 - e -
s dt ) ##EQU00019.2## .PHI. ~ = .PHI. ( 1 - e - s dt )
##EQU00019.3## m 2 = 1 + .PHI. ~ T .PHI. ~ ##EQU00019.4##
where is the normalized prediction error, {tilde over (z)} and
{tilde over (.phi.)} are the differences of the output and
regressor respectively corresponding to the time interval dt, and m
is the normalization signal. In addition, a modified function J(
)(referred to as a cost function) is adopted in accordance with the
following equation:
J ( .THETA. ( t ) ) = 1 2 .intg. 0 t e - .beta. ( t - .tau. ) ( z ~
- .THETA. T .PHI. ~ ) 2 m 2 d .tau. + 1 2 e - .beta. t ( .THETA. -
.THETA. 0 ) T Q 0 ( .THETA. - .THETA. 0 ) , ##EQU00020##
where .beta.>0 and Q.sub.0.gtoreq.0 are referred to as a
forgetting factor and a penalty matrix. Based on this, the
following stationary conditions must be met at the solution
.THETA.:
.differential. .differential. .THETA. J ( .THETA. ( t ) ) = e -
.beta. t Q 0 ( .THETA. - .THETA. 0 ) - .intg. 0 t e - .beta. ( t -
.tau. ) z ~ .PHI. ~ m 2 d .tau. + .intg. 0 t e - .beta. ( t - .tau.
) .PHI. ~ .PHI. ~ T m 2 d .tau..THETA. = = [ e - .beta. t Q 0 +
.intg. 0 t e - .beta. ( t - .tau. ) .PHI. ~ .PHI. ~ T m 2 d .tau. ]
.THETA. - [ e - .beta. t Q 0 .THETA. 0 + .intg. 0 t e - .beta. ( t
- .tau. ) z ~ .PHI. ~ m 2 d .tau. ] = = P - 1 .THETA. - [ e -
.beta. t Q 0 .THETA. 0 + .intg. 0 t e - .beta. ( t - .tau. ) z ~
.PHI. ~ m 2 d .tau. ] = 0 ##EQU00021##
[0054] Thus, .THETA. can be found non-recursively as:
.THETA. = P [ e - .beta. t Q 0 ( .THETA. - .THETA. 0 ) + .intg. 0 t
e - .beta. ( t - .tau. ) z ~ .PHI. ~ m 2 d .tau. ] , where
##EQU00022## P = [ e - .beta. t Q 0 + .intg. 0 t e - .beta. ( t -
.tau. ) .PHI. ~ .PHI. ~ T m 2 d .tau. ] - 1 . ##EQU00022.2##
Matrix P and vector .THETA. satisfy the following two differential
equations which complete the definition of the recursive algorithm
that can be used to solve the parameter identification problem:
P . = .beta. P - P .PHI. ~ .PHI. ~ T m 2 P , P ( 0 ) = P 0 = Q 0 -
1 , .THETA. . = P .PHI. ^ , ##EQU00023##
where is the normalized error or difference between the last
measured values and current measured values.
[0055] In the following discussion, methods are described that can
be used to indirectly estimate a current value of patient effort in
real time. In addition, it is demonstrated how various combinations
of the above mentioned interim values (e.g., signals internal the
transfer function) explained above possess a significant level of
correlation with the unmeasured patient effort. Because of the
correlation, the interim values may be used to characterize patient
effort with a reasonable degree of accuracy.
[0056] From the relationships established above, it is clear
that:
.PHI. d = ( z - .THETA. T .PHI. ) = b P p ( s ) p . P ( s )
.LAMBDA. ( s ) . ##EQU00024##
[0057] By choosing an appropriate filter,
1 .PI. ( s ) , ##EQU00025##
that yields
.LAMBDA. ( s ) b P p ( s ) 1 .PI. ( s ) , ##EQU00026##
an estimate of the derivative of patient effort ({dot over
({circumflex over (p)})}.sub.p) of the real derivative of patient
effort ({dot over (p)}.sub.P) can be computed as follows:
p . ^ P ( s ) = ( z - .THETA. T .PHI. ) .LAMBDA. ( s ) b P p ( s )
1 .PI. ( s ) . ##EQU00027##
[0058] Based on the following equation, it is apparent that a
prediction error signal, z- is correlated with the patient effort
signal, {dot over (p)}.sub.P, and the filtered version thereof,
.phi..sub.d:
z - z ^ = .THETA. T .PHI. + .PHI. d - ( .THETA. T .PHI. + e - s dt
( z - .THETA. T .PHI. ) ) = .PHI. d - e - s dt .PHI. d = d .PHI. d
( t - dt ) dt dt . ##EQU00028##
[0059] Using the transfer function defined above and the current
estimate of the parameter vector .THETA., a prediction ({circumflex
over (p)}.sub.y) of the current pressure in the tubing (p.sub.y) is
represented by the following equation:
p ^ Y ( s , .THETA. ) = b q ( s , .THETA. ) a ( s , .THETA. ) q N (
s ) . ##EQU00029##
[0060] From this, the prediction error may be described by the
following equation:
p y - p ^ y = b P p ( s ) a ( s ) p . P ( s ) , ##EQU00030##
which is a filtered version of the derivative of patient effort
({dot over (p)}.sub.P). Moreover, if the ventilation system is
characterized by the absence of tubing leaks (i.e., assume
.lamda..sub.LEAK=0), then the prediction error, p.sub.y-{circumflex
over (p)}.sub.y, resembles the patient effort signal (p.sub.P) as
the transfer function
b P p ( s ) a ( s ) ##EQU00031##
is an integration function.
[0061] The aforementioned equations describe relationships between
patient effort (i.e., p.sub.P and/or {dot over (p)}.sub.P), and
accurately obtainable flow and pressure measurements. FIG. 3
graphically depicts the exemplary correlation between patient
effort (i.e., p.sub.P and/or {dot over (p)}.sub.P) and exemplary
signals internal to the previously described algorithm. As shown, a
timing diagram 210 depicts patient effort (p.sub.P) as a function
of time. A timing diagram 205 depicts the first derivative of
patient effort ({dot over (p)}.sub.P) as a function of time. A
timing diagram 215 depicts p.sub.y-{circumflex over (p)}.sub.y and
a timing diagram 220 depicts z-{circumflex over (z)}. The magnitude
of each of p.sub.P, {dot over (p)}.sub.P, p.sub.y-{circumflex over
(p)}.sub.y and z-{circumflex over (z)} is represented in
centimeters of H.sub.2O. As would be expected based on the analysis
provided above, there is a strong correlation between patient
effort (p.sub.P) depicted in diagram 210 and the signal
p.sub.y-{circumflex over (p)}.sub.y depicted in diagram 215.
Similarly, diagrams 205 and 220 demonstrate a strong correlation
between the first derivative of the patient effort, {dot over
(p)}.sub.P, and the signal z-{circumflex over (z)}. Thus, the
reconstructed signals can be used to predict the otherwise unknown
signals {dot over (p)}.sub.P and p.sub.P. It should be noted that
the results are merely exemplary, and that based on the disclosure
provided herein, one of ordinary skill in the art will recognize a
variety of different signals and their delayed versions that may be
achieved through use of different embodiments of the present
invention to characterize the unknown patient effort signals and
the derivatives thereof.
[0062] Turning to FIG. 4, a flow diagram 300 depicts a method in
accordance with some embodiments of the present invention for
determining patient effort. A ventilator system is provided that
includes a ventilator that is coupled to a subject using various
tubing. The ventilator receives one or more inlet gas flows and
includes an outlet gas flow in addition to an inlet/outlet to the
subject. Following flow diagram 300, pressure in the tubing
(p.sub.y) is measured along with the inlet flow(s) and the outlet
flow to generate a net flow (q.sub.n) (block 305). The pressure
value (p.sub.y) is filtered and provided as an output (z) (block
310), and the pressure (p.sub.y) and net flow value (q.sub.n) are
filtered and combined in a regression vector (.phi..sup.T) (block
315). Differences and/or derivatives of the aforementioned values
(i.e., z and .phi..sup.T) are calculated to generate outputs
m.sup.2, {tilde over (z)} and {tilde over (.phi.)} (block 320). In
addition, time delayed versions of z (i.e., ze.sup.-sdt) and {tilde
over (.phi.)} (i.e., {tilde over (.phi.)}e.sup.-sdt) are created
(blocks 317, 318). m.sup.2, {tilde over (z)}, {tilde over (.phi.)}
and .THETA..sup.T are combined to generate an estimated normalized
prediction error ( ) (block 325); and m.sup.2, {tilde over (.phi.)}
and are used along with a previously computed covariance matrix
(P.sub.0) to calculate an updated covariance matrix (P) (block
330). The newly calculated covariance matrix (P) is stored and
maintained as the previously computed covariance (P.sub.0) for use
in later updating of the covariance matrix (block 335). The updated
covariance matrix (P) is used along with the previously computed
and {tilde over (.phi.)} values to calculate an updated system
parameter vector (.THETA.) (block 340). In addition, a time delayed
version of .THETA. (i.e., .THETA. e.sup.-sdt) generated (block
319). As discussed above, the system parameter vector (.THETA.)
incorporates various system parameters including, for example,
tubing compliance (C.sub.T), lung compliance (C.sub.L), lumped
resistance (R.sub.P), and leakage (.lamda..sub.LEAK).
[0063] During the above mentioned processing (blocks 305-340),
various of the interim values may be used either separately or in
combination to estimate patient effort (block 345). For example, as
depicted in FIG. 3 above, z correlates to patient effort. Further,
as z may be calculated using other constituent elements, the
constituent elements may also be used to estimate patient effort.
Based on the disclosure provided herein, one of ordinary skill in
the art will recognize other uses of the constituent elements to
predict patient effort.
[0064] Turning to FIG. 5, a microprocessor based system 400 for
determining patient effort is depicted in accordance with various
embodiments of the present invention. System 400 includes a
microprocessor 410 communicably coupled to a computer readable
medium 460. Microprocessor 410 may be any processor known in the
art that is capable of receiving various input values, and
executing software or firmware instructions to provide an output
based on the input values. Computer readable medium 460 may be any
media capable of storing instructions that are executable by
microprocessor 410. Based on the disclosure provided herein, one of
ordinary skill in the art will recognize a variety of processors
that may be used in relation to different embodiments of the
present invention. As just some examples, computer readable medium
460 may be a hard disk drive, a tape drive, a portable solid state
memory, a CD ROM, a RAM, combinations of the aforementioned, or the
like. Based on the disclosure provided herein, one of ordinary
skill in the art will recognize a variety of media and combinations
of the media that may be used in relation to different embodiments
of the present invention.
[0065] Instructions 450 when executed cause microprocessor 410 to
receive various I/O via an I/O interface 420. The received I/O
include measured inlet gas flows 422, 424, and a measured outlet
gas flow 426. In some cases, the measured inlet gas flows measure
the flow of Air and Oxygen, respectively. It should be noted that
more or fewer than two inlet gas flows may be measured depending
upon the particular embodiment of the present invention.
[0066] Outlet gas flow 426 measures the gas flow being exhaled from
system 400. Further, the received I/O include measured inlet gas
pressures 428, 430 associated with the respective inlet gas flows
422, 424. It should be noted that where there are more or fewer
inlet gas flows that the I/O may include more or fewer measured gas
pressure inputs. Further, in some embodiments of the present
invention, a single gas pressure input may be provided in place of
inlet gas pressures 428, 430 where a single gas pressure sensor is
placed in system 400 at a location that allows it to provide a
pressure value that effectively combines inlet gas pressures 428,
430. Further, instructions 450 when executed cause microprocessor
410 to implement a patient effort algorithm using the I/O received
via I/O interface 420, and providing a patient effort output 440.
Such a patient effort algorithm may be, but is not limited to, the
patient effort algorithms discussed above in relation to FIG. 2 and
FIG. 4. As part of implementing the patient effort algorithm,
instructions 450 cause microprocessor 410 to calculate a variety of
otherwise unknown system parameters including, but not limited to,
tubing compliance 412 (C.sub.T), lung compliance 414 (C.sub.L),
lumped resistance 416 (R.sub.P), and leakage 418
(.lamda..sub.LEAK). The aforementioned system parameters may be
used in a variety of interim calculations with the results of one
or more of the interim calculations providing results that are
predictive of patient effort output 440.
[0067] In addition, microprocessor based system 400 may include a
graphical user interface driver 490 and a graphical user interface
495. Graphical user interface 495 may be any interface that
provides for graphically portraying information from microprocessor
based system 400 to a user. Thus, graphical user interface 495 may
be any display known in the art. In some cases, graphical user
interface 495 may further include an ability to receive input from
a user. The ability to receive input may be provided by, for
example, a touch screen capability, a keyboard, a mouse, and/or the
like deployed in association with graphical user interface 495.
Graphical user interface driver 490 may be any circuit, system or
device known in the art that is capable of converting information
from microprocessor based system 400 into graphical information
displayable via graphical user interface 495.
[0068] FIG. 6 is a flow diagram 500 depicting a method in
accordance with some embodiments of the present invention for
triggering a ventilation cycle. Following flow diagram 500, a
pressure is measured (block 505), an inlet flow is measured (block
510), and an outlet flow is measured (block 515). In some cases,
the pressure is measured in a tube connecting a ventilator to a
person being ventilated. In some cases, the pressure is measured
near a gas inlet and/or near a gas outlet. In other cases, the
pressure is measured near a junction of the gas inlet with the gas
outlet. In various cases, the pressure measurement is a single
point pressure measurement, while in other cases the pressure
measurement is a multiple point pressure measurement and the
measured pressure is a mathematical combination of two or more
pressure measurements. Measuring the inlet flow may include
measuring the flow of a single gas, or measuring the flows of two
or more gases and aggregating the multiple flow values. Measuring
the outlet flow may include, but is not limited to, measuring the
flow of gas at the outlet of the ventilation system. The outlet
flow is subtracted from the inlet flow at a particular instance to
generate an instantaneous net flow (block 520).
[0069] The net flow and measured pressure for a given instant are
used to calculate an updated prediction of patient effort (block
525). This process may be done using the approach discussed above
in relation to FIG. 4. It is then determined whether the updated
prediction of patient effort indicates an onset condition (block
530). Where an onset condition is indicated (block 530), a
ventilation cycle is triggered to begin (block 535). As an example,
the updated prediction of patient effort may be the filtered
patient effort signal (.phi..sub.d) that was discussed above. The
filtered patient effort signal is a function of the derivative of
patient effort ({dot over (p)}.sub.p) as set forth in the following
equation:
.PHI. d = b P p ( s ) p . P ( s ) .LAMBDA. ( s ) . ##EQU00032##
[0070] Thus, the filtered patient effort signal is expected to be
negative when the actual patient effort (p.sub.p) is decreasing.
Therefore, the onset of inspiration is indicated when the filtered
patient effort signal becomes less than zero (e.g., exhibits a
negative zero crossing where the signal transitions from a positive
value to a negative value). This indicator may be used to
synchronize the onset of a ventilation cycle with patient effort.
Such synchrony results in improved patient ventilation. In some
cases, a ventilation cycle is triggered to begin once the filtered
patient effort signal is less than zero. In other cases, a
ventilation cycle is triggered to begin once the filtered patient
effort signal reaches a predefined negative threshold value or
positive threshold value. It should be noted that while the
filtered patient effort signal is used in the preceding example,
that one or more other signals may be similarly used. For example,
prediction error signal, z-z,54 may also be used as it is similarly
correlated with actual patient effort. Based on the disclosure
provided herein, one of ordinary skill in the art will recognize a
variety of other signals that may be used to initiate a ventilation
cycle.
[0071] Alternatively, it is determined whether the updated
prediction of patient effort indicates an end condition (block
540). Where an end condition is indicated (block 540), a previously
started ventilation cycle is triggered to terminate (block 545). As
an example, the updated prediction of patient effort may be the
same filtered patient effort signal used to trigger the onset of
inspiration. As the filtered patient effort signal is a function of
the derivative of patient effort, the end of inspiration is
indicated when the filtered patient effort signal becomes greater
than zero (e.g., exhibits a positive zero crossing where the signal
transitions from a negative value to a positive value). Such an
indicator may be used to synchronize the termination of a
ventilation cycle with patient effort, and thereby provide improved
patient ventilation. In some cases, a ventilation cycle is
triggered to end once the filtered patient effort signal is greater
than zero. In other cases, a ventilation cycle is triggered to end
once the filtered patient effort signal reaches a predefined
negative threshold value or positive threshold value. Again, it
should be noted that while the filtered patient effort signal is
used in the preceding example, that one or more other signals may
be similarly used. For example, prediction error signal, z- may
also be used as it is similarly correlated with actual patient
effort. Based on the disclosure provided herein, one of ordinary
skill in the art will recognize a variety of other signals that may
be used to terminate a ventilation cycle.
[0072] Turning to FIG. 7, a timing diagram 600 shows the process of
triggering multiple ventilation cycles based on a proxy of patient
effort. In this case, the proxy of patient effort is the filtered
patient effort signal (.phi..sub.d) 610. An actual patient effort
signal (P.sub.p) 620 is shown to demonstrate the synchrony
achievable using different embodiments of the present invention. It
should be noted that while filtered patient effort signal 610 is
shown as the ventilation trigger, that one or more other signals
may be similarly used. For example, prediction error signal, z- may
also be used as it is similarly correlated with actual patient
effort. Based on the disclosure provided herein, one of ordinary
skill in the art will recognize a variety of other signals that may
be used to effectuate triggering.
[0073] As shown, the transition of filtered patient effort signal
610 through a negative zero crossing point 612a corresponds to the
beginning of an actual patient inspiration effort 622a. A
subsequent positive zero crossing point 614a corresponds to the
onset of exhalation 624a. This process is depicting for a number of
ventilation cycles. Consistent with timing diagram 600, a positive
zero crossing of filtered patient effort signal 610 may be used to
trigger the beginning of a ventilation cycle, and a negative zero
crossing of filtered patient effort signal 610 may be used to
trigger the end of a ventilation cycle.
[0074] FIG. 8 includes a timing diagram 710 showing a process of
triggering off of a pressure sensor corresponding to p.sub.y, a
timing diagram 720 showing a process of triggering off of an
estimated patient effort signal, p.sub.y-{circumflex over
(p)}.sub.y, and a timing diagram 730 showing a process of
triggering off of another signal correlated to patient effort,
z-{circumflex over (z)}. As shown by timing diagram 710, the
pressure sensor exhibits a noise level 711 with a trigger threshold
713 set a noise buffer amount 712 below the expected noise level
711 to avoid false triggering. As shown, the pressure corresponding
to p.sub.y eventually drops below trigger threshold 713 resulting
in a detected inspiration onset 714 (represented a vertical dashed
line). Detected inspiration threshold 714 occurs a delay period 715
after an actual inspiration onset 716 (represented by a vertical
dashed line). As can be seen from timing diagram 710, the magnitude
of delay period 715 is a function of noise level 711 and noise
buffer amount 712.
[0075] Noise associated with a pressure measurement is not
necessarily correlated with that associated with flow measurements.
By combining information derived from both pressure and flow
measurements in the development of an estimated patient effort
signal, the amount of noise expected is typically reduced when
compared with the noise expected when only a single measurement is
used. A noise buffer amount is often chosen based on the magnitude
of expected noise. Thus, in some embodiments of the present
invention, both the expected noise level and noise buffer amount
are less than that exhibited in single measurement systems. The
reduction of these variables allows for a detected inspiration that
is correlated more closely in time with an actual inspiration
onset. Timing diagrams 720, 730 graphically depict such a reduced
trigger delay.
[0076] Following timing diagram 720, the estimated patient effort
signal, p.sub.y-{circumflex over (p)}.sub.y, exhibits a relatively
small noise level 721 with a trigger threshold 723 set a noise
buffer amount 722 above the expected noise level 721 to avoid false
triggering. As shown, the estimated patient effort signal
eventually exceeds trigger threshold 723 resulting in a detected
inspiration onset 724 (represented a vertical dashed line).
Detected inspiration onset 724 occurs a delay period 725 after an
actual inspiration onset 726 (represented by a vertical dashed
line). Delay period 725 is less than that which results when only a
single point of measurement is used. Similarly, following timing
diagram 730, the estimated patient effort signal, z-{circumflex
over (z)}, exhibits a relatively small noise level 731 with a
trigger threshold 733 set a noise buffer amount 732 above the
expected noise level 731 to avoid false triggering. As shown, the
estimated patient effort signal eventually exceeds trigger
threshold 733 resulting in a detected inspiration onset 734
(represented a vertical dashed line). Detected inspiration onset
734 occurs a delay period 735 after an actual inspiration onset 736
(represented by a vertical dashed line). Delay period 735 is less
than that which results when only a single point of measurement is
used.
[0077] Turning to FIG. 9, a flow diagram 800 shows a method for
providing ventilation in proportion to patient effort in accordance
with various embodiments of the present invention. Following flow
diagram 800, a pressure is measured (block 805), an inlet flow is
measured (block 810), and an outlet flow is measured (block 815).
In some cases, the pressure is measured in a tube connecting a
ventilator to a person being ventilated. In some cases, the
pressure is measured near a gas inlet and/or near a gas outlet. In
other cases, the pressure is measured near a junction of the gas
inlet with the gas outlet. In various cases, the pressure
measurement is a single point pressure measurement, while in other
cases the pressure measurement is a multiple point pressure
measurement and the measured pressure is a mathematical combination
of two or more pressure measurements. Measuring the inlet flow may
include measuring the flow of a single gas, or measuring the flows
of two or more gases and aggregating the multiple flow values.
Measuring the outlet flow may include, but is not limited to,
measuring the flow of gas at the outlet of the ventilation system.
The outlet flow is subtracted from the inlet flow at a particular
instance to generate an instantaneous net flow (block 820).
[0078] The net flow and measured pressure for a given instant are
used to calculate an updated prediction of patient effort (block
825). This process may be done using the approach discussed above
in relation to FIG. 4. Desired gas delivery parameter(s) of gas to
be delivered by the ventilator an instant corresponding to the
calculated patient effort is/are then calculated (block 840). In
some embodiments of the present invention, the gas delivery
parameters are flow and/or pressure. In this case, a desired
pressure and flow of gas delivery are each a function of patient
effort. For example, where patient effort is determined to be a
value at an instant x described by a function f(x), then the
calculated pressure may be described at an instant using the
function g(f(x)) and the calculated flow at an instant may be
described by the function h(f(x)). In one particular embodiment of
the present invention, the function g and the function h are each
constant multipliers. In such a case, the calculated pressure at an
instant x is k.sub.1f(x) and the calculated flow at the instant x
is k.sub.2f(x), where k.sub.1 is the constant corresponding to
pressure and k.sub.2 is the constant corresponding to flow. Based
on the disclosure provided herein, one of ordinary skill in the art
will recognize other functions g functions h that may be used in
relation to different embodiments of the present invention. The
pressure used as a metric for delivering gas may be, but is not
limited to, wye pressure or patient lung pressure. The flow used as
a metric for delivering gas may be, but is not limited to, patient
lung flow or inlet gas flow.
[0079] It is then determined whether the updated prediction of
patient effort indicates an inspiration phase (block 830). In some
embodiments of the present invention, an inspiration phase is
indicated where the derivative of patient effort {dot over
(p)}.sub.P is greater than zero. Where an inspiration phase is
indicated (block 830), gas is delivered to a recipient in
accordance with the gas delivery parameters previously calculated
(block 835). Again, gas delivery parameters may include, but are
not limited to, pressures and flows of gas or gas components (e.g.,
oxygen, air, nitrogen, helium, etc.) to be delivered to a patient.
Otherwise, where an inspiration phase is not indicated (block 830),
gas delivery is not provided. Such an approach provides for gas
delivery at a rate and/or pressure as a function of the patient's
effort. Such an approach provides for increased patient comfort as
well as less interference with a patient's own attempts at
breathing.
[0080] Turning to FIG. 10, four timing diagrams 910, 920, 930, 940
graphically depict providing ventilation in proportion to patient
effort in accordance with one or more embodiments of the present
invention. Timing diagram 910 depicts patient effort as a function
of time, and timing diagram 920 depicts a derivative of patient
effort as a function of time. As shown, when the derivative of
patient effort is greater than zero (corresponding to an
inspiration phase), patient effort is described as a function f(x).
It should be noted that while timing diagram 910 shows patient
effort as the same function repeating over time, that a first
instance of f.sub.1(x) 912 may differ substantially from the second
instance of f.sub.2(x) 914 depending upon the breathing pattern of
the particular patient.
[0081] A timing diagram 930 depicts an effort by a ventilator to
increase the pressure at the wye connection to offset a pressure
decrease caused by patient effort. As shown, during the inspiration
phase (i.e., when the derivative of patient effort is greater than
zero), the ventilator attempts to raise the pressure at the wye
connection as a function of patient effort, g(f.sub.1(x)) 932. On a
subsequent breath, the ventilator attempts to raise the pressure at
the wye as a function of patient effort, g(f.sub.2(x)) 934. In this
particular case, the function g is a constant k.sub.1, however,
other time varying functions may be used in accordance with
different embodiments of the present invention.
[0082] Similarly, during the inspiration phase, the ventilator
increases the flow of gas to a patient as a function of patient
effort, h(f.sub.1(x)) 942. On a subsequent breath, the ventilator
increases the flow of gas to a patient as a function of patient
effort, h(f.sub.2(x)) 944. In this particular case, the function h
is a constant k.sub.2, however, other time varying functions may be
used in accordance with different embodiments of the present
invention. In some cases, the functions g and h may be proportional
or inversely proportional to patient effort. It should be noted
that in the sense that gas delivery is provided as a function of
patient effort, that patient effort may be determined based
directly on patient effort (i.e., patient interpleural pressure),
or on a first or higher order derivative of patient effort.
[0083] Turning to FIG. 11, an exemplary graphical interface 1000
showing the display of patient effort corresponding to an actively
breathing patient in accordance with some embodiments of the
present invention. Graphical interface 1000 includes a graphical
display of filtered patient effort (.phi..sub.d) 1010, and patient
effort (p.sub.p) 1020 each as a function of time. It should be
noted that other indications of patient effort may be displayed in
addition to those depicted or in place of those depicted depending
upon the particular embodiment of the present invention.
[0084] In the depicted embodiment, time is displayed across a
horizontal axis and the value of the respective patient effort
value is displayed across a left axis. As time proceeds, the time
increments across the horizontal axis are updated to reflect a
window around the current time.
[0085] In addition, two user movable vertical bars 1012, 1022 are
disposed over graph 1010 and graph 1020. This allows a user to
place a begin bar 1012 and an end bar 1022 at particular times to
measure an event. The time difference between begin bar 1012 and
end bar 1022 may be displayed to the user, along with the value of
filtered patient effort and patient effort at the respective
instants in time. In some cases, begin bar 1012 and end bar 1022
may be used via a keyboard command or a mouse command. Based on the
disclosure provided herein one of ordinary skill in the art will
recognize a variety of I/O that may be used to manipulate begin bar
1012 and end bar 1022 in relation to graphs 1010, 1020.
[0086] In addition, various metrics relating to the graphically
displayed patient effort may be calculated and displayed via
graphical interface 1000. For example, a mean time between breaths
1030 may be calculated and displayed. Such a mean time may be
calculated based on a defined number of breaths, where a time
between each of the breaths is calculated from the end of
expiration to the beginning of subsequent inspiration. Based on the
disclosure provided herein, one of ordinary skill in the art will
appreciate a variety of approaches that may be used to calculate
mean time between breaths in accordance with different embodiments
of the present invention. As another example, a peak breathing
effort 1040 may be displayed. Peak breathing effort 1040 may be the
maximum value recorded on either of graph 1010 or graph 1020 over
the course of a defined number of breaths depending upon the
particular implementation. As yet another example, peak effort per
breath 1050 may be displayed. Peak effort per breath 1050 may
indicate the peak value of either graph 1010 or graph 1020 for a
most current breath. Alternatively it may indicate the peak value
of either graph 1010 or graph 1020 for a breath identified by begin
bar 1012. As yet a further example, a duration of last inspiration
1060 may be displayed. Duration of the last inspiration 106
indicates a time from when the onset of inspiration was detected
until the end of inspiration was detected for the most recent
breath. In one case, this may be achieved by detecting when a first
derivative of the patient effort exceeds a threshold until it
returns below the threshold. As another example, a duration of the
last expiration 1070 may be displayed. In some cases, duration of
the last expiration 1070 may be calculated by detecting when a
first derivative of the patient effort falls below a threshold
until the time when the first derivative returns above the
threshold. As another example, an average duration of inspiration
1080 and an average duration of expiration 1090 may be displayed.
The may be calculated by averaging a number of the previously
discussed expiration durations and inspiration durations.
[0087] Turning to FIG. 12, an exemplary graphical interface 1100
showing the display of a respiratory parameter corresponding to an
actively breathing patient in accordance with some embodiments of
the present invention. In particular, a graph 1110 depicts an
estimated value of the patient resistance parameter as a function
of time. In some embodiments, the patient resistance parameter is
referred to as "estimated Rp" because it is the result of
calculation as distinguished from the actual value of the patient
resistance. It should be noted that while graphical interface 1100
is described as showing estimated Rp, that other respiratory
parameters may be displayed in accordance with different
embodiments of the present invention. For example, graphical
interface 1100 may be augmented to display lung compliance or
leakage parameters, with these additionally displayed parameters
determined using the same or similar set of equations as described
herein. Based on the disclosure provided herein, one of ordinary
skill in the art will recognize a variety of respiratory parameters
that may be displayed. In some cases, the displayed respiratory
parameters may be used by a monitoring clinician for real time
assessment of a patient. Alternatively, or in addition, the
displayed respiratory parameters may be used to determine a
potential system malfunction or to indicate a disconnect of the
patient from the ventilator. As one particular example, a dramatic
increase in Rp may indicate a partial obstruction. Based on the
disclosure provided herein, one of ordinary skill in the art will
recognize a variety of advantages that may be achieved in
accordance with one or more embodiments of the present
invention.
[0088] As shown, time is displayed across a horizontal axis and the
value of estimated Rp is displayed across a left axis. In some
embodiments, as time proceeds, the time increments across the
horizontal axis are updated to reflect a window around the cuiTent
time. Additionally, in some embodiments, two user movable vertical
bars 1112, 1113 are disposed over graph 1110. This allows a user to
place a begin bar 1112 and an end bar 1113 at particular times to
measure an event. The time difference between begin bar 1112 and
end bar 1113 may be displayed to the user, along with the value of
filtered patient effort and patient effort at the respective
instants in time. In some cases, begin bar 1112 and end bar 1113
may be used via a keyboard command or a mouse command. Based on the
disclosure provided herein one of ordinary skill in the art will
recognize a variety of I/O that may be used to manipulate begin bar
1112 and end bar 1113 in relation to graph 1110.
[0089] In this particular example, for an initial period 1120
estimated Rp is initialized with a value of five (5)
cmH.sub.2O/Ips. At this time, the actual value of Rp is nearer to
thirty (30) cmH.sub.2O/Ips. Over a period of time, the algorithm
used to determine the value of estimated Rp adaptively adjusts
until the estimated value approximates the actual value for Rp
during a period 1130. Sometime around the fifty (50) second mark,
an obstruction is removed from the ventilation system resulting in
a dramatic decrease in the actual value of Rp. At this point, the
algorithm adaptively adjusts by lowering the value of estimated Rp
until the estimated value approximates the actual value. During a
period 1140, the value of estimated Rp remains approximately
constant near the actual value of Rp.
[0090] In addition, various metrics relating to the graphically
displayed resistance parameter may be calculated and displayed via
graphical interface 1100. For example, a current Rp value 1150 may
be displayed, and an average Rp value 1160 may be displayed.
Average Rp value 1160 may be calculated by averaging a number of
values for Rp over a particular time period. In addition, a visual
alarm 1170 may be displayed. Such a visual alarm may be triggered
whenever a predefined increase or decrease in the value of
estimated Rp is detected. It should be noted that graphical
interface 1100 may be augmented to display a variety of other
information.
[0091] The present invention provides novel systems, methods and
devices delivering a gas in proportion to a patient effort. While
detailed descriptions of one or more embodiments of the invention
have been given above, various alternatives, modifications, and
equivalents will be apparent to those skilled in the art without
varying from the spirit of the invention. Therefore, the above
description should not be taken as limiting the scope of the
invention, which is defined by the appended claims.
* * * * *