U.S. patent application number 14/146932 was filed with the patent office on 2014-05-01 for systems, methods, and/or apparatuses for non-invasive monitoring of respiratory parameters in sleep disordered breathing.
This patent application is currently assigned to RESMED LIMITED. The applicant listed for this patent is RESMED LIMITED. Invention is credited to Dion Charles Chewe MARTIN, John David OATES.
Application Number | 20140116442 14/146932 |
Document ID | / |
Family ID | 39401242 |
Filed Date | 2014-05-01 |
United States Patent
Application |
20140116442 |
Kind Code |
A1 |
MARTIN; Dion Charles Chewe ;
et al. |
May 1, 2014 |
SYSTEMS, METHODS, AND/OR APPARATUSES FOR NON-INVASIVE MONITORING OF
RESPIRATORY PARAMETERS IN SLEEP DISORDERED BREATHING
Abstract
In certain example embodiments, an air delivery system includes
a controllable flow generator operable to generate a supply of
pressurized breathable gas to be provided to a patient for
treatment and a pulse oximeter. In certain example embodiments, the
pulse oximeter is configured to determine, for example, a measure
of patient effort during a treatment period and provide a patient
effort signal for input to control operation of the flow generator.
Oximeter plethysmogram data may be used, for example, to determine
estimated breath phase; sleep structure information; autonomic
improvement in response to therapy; information relating to
relative breathing effort, breathing frequency, and/or breathing
phase; vasoconstrictive response, etc. Such data may be useful in
diagnostic systems.
Inventors: |
MARTIN; Dion Charles Chewe;
(Sydney, AU) ; OATES; John David; (Sydney,
AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
RESMED LIMITED |
Bella Vista |
|
AU |
|
|
Assignee: |
RESMED LIMITED
Bella Vista
AU
|
Family ID: |
39401242 |
Appl. No.: |
14/146932 |
Filed: |
January 3, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12514696 |
May 13, 2009 |
8646447 |
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PCT/AU07/01744 |
Nov 13, 2007 |
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14146932 |
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60858414 |
Nov 13, 2006 |
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Current U.S.
Class: |
128/204.23 |
Current CPC
Class: |
A61M 16/0057 20130101;
A61M 2230/202 20130101; A61B 5/7278 20130101; A61M 2230/202
20130101; A61B 5/02416 20130101; A61B 5/4818 20130101; A61M
2016/0021 20130101; A61M 2230/205 20130101; A61B 5/4812 20130101;
A61B 5/4809 20130101; A61M 2230/205 20130101; A61B 5/4836 20130101;
A61B 5/0205 20130101; A61M 16/026 20170801; A61M 2230/005 20130101;
A61M 2230/42 20130101; A61M 2230/005 20130101; A61M 2016/0039
20130101; A61B 5/14551 20130101; A61M 16/0051 20130101; A61B 5/4848
20130101; A61M 2205/581 20130101 |
Class at
Publication: |
128/204.23 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/1455 20060101 A61B005/1455; A61B 5/0205 20060101
A61B005/0205; A61M 16/00 20060101 A61M016/00 |
Claims
1. An air delivery system, comprising: a controllable flow
generator operable to generate a supply of pressurized breathable
gas to be provided to a patient for treatment; a pulse oximeter;
and a controller configured to determine sleep structure
information for the patient and track the sleep structure
information to indicate a therapy's effectiveness based at least in
part on output from the pulse oximeter.
2. The air delivery system of claim 1, wherein the sleep structure
information includes information about the patient's start, finish,
and/or REM sleep states.
3. The air delivery system of claim 2, wherein information about
the patient's sleep state is extrapolated via heart period
analysis.
4. The air delivery system of claim 1, wherein the controllable
flow generator is adapted to provide Continuous Positive Airway
Pressure (CPAP).
5. The air delivery system of claim 1, wherein the sleep structure
information is tracked by performing statistical or fractal
analysis of pulse interval data.
6. The air delivery system of claim 1, wherein the sleep structure
information is tracked by analyzing heart-rate variability indices
to indicate sleep onset.
7. A method for diagnosing sleep disordered breathing, said method
comprising: deriving a pulse oximeter signal from a patient;
processing the pulse oximeter signal to generate a patient effort
signal indicative of respiratory rate; and tracking sleep structure
information for the patient to indicate a therapy's
effectiveness.
8. The method of claim 7, wherein the sleep structure information
includes information about the patient's start, finish, and/or REM
sleep states
9. The method of claim 8, wherein information about the patient's
sleep state is extrapolated via heart period analysis.
10. The method of claim 7, wherein the sleep structure information
is tracked by performing statistical or fractal analysis of pulse
interval data.
11. The method of claim 7, wherein the sleep structure information
is tracked by analyzing heart-rate variability indices to indicate
sleep onset.
12. The method of claim 7, wherein the patient effort signal is
based upon an arterial blood pressure waveform variation in
peak-to-peak amplitude.
13. A respiratory effort monitoring apparatus, comprising: a pulse
oximeter configured to derive a pulse oximeter signal; and a signal
processor configured to receive the pulse oximeter signal and
generate a patient effort signal indicative of respiratory rate;
wherein the signal processor is configured to track sleep structure
information for the patient to indicate a therapy's
effectiveness.
14. The respiratory effort monitoring apparatus of claim 13,
wherein the sleep structure information includes information about
the patient's start, finish, and/or REM sleep states.
15. The respiratory effort monitoring apparatus of claim 14,
wherein information about the patient's sleep state is extrapolated
via heart period analysis.
16. The respiratory effort monitoring apparatus of claim 13,
wherein the signal processor is configured to trend sleep structure
information for the patient to indicate a therapy's effectiveness
over a period of time.
17. The respiratory effort monitoring apparatus of claim 13,
wherein the sleep structure information is tracked by performing
statistical or fractal analysis of pulse interval data.
18. The respiratory effort monitoring apparatus of claim 13,
wherein the sleep structure information is tracked by analyzing
heart-rate variability indices to indicate sleep onset.
19. The respiratory effort monitoring apparatus of claim 13,
wherein the patient effort signal is based upon an arterial blood
pressure waveform variation in peak-to-peak amplitude.
20. The respiratory effort monitoring apparatus of claim 13,
further comprising a filter adapted to filter heart rate out of the
pulse oximeter signal.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. application Ser.
No. 12/514,696, filed on May 13, 2009, now allowed, which claims
the benefit of U.S. Provisional Application No. 60/858,414, filed
on Nov. 13, 2006, the entire contents of which is hereby
incorporated herein in its entirety. This application incorporates
by reference the entire contents of each of PCT Application No. WO
2006/037,184, filed on Oct. 6, 2005, and U.S. Provisional
Application No. 60/615,961, filed on Oct. 6, 2004.
FIELD OF THE INVENTION
[0002] The invention relates to monitoring parameters relevant to
Sleep Disordered Breathing (SDB).
BACKGROUND OF THE INVENTION
[0003] Sleep Disordered Breathing (SDB) has been traditionally
identified as being associated with Obstructive Sleep Apnea (OSA)
and Cheyne-Stokes Respiration (CSR). Today there are a number of
other conditions also recognized as being associated with SDB
including, e.g., cardiovascular disease, stroke and diabetes, etc.
Patients with these conditions and SDB may benefit from the
treatment of their SDB with positive pressure ventilatory support
by some form of mechanical ventilator.
[0004] While basic nasal Continuous Positive Airway Pressure (CPAP)
ventilators may not monitor their patients, in general, the
patients benefit from having a device which monitors the patients
as part of some kind of control loop. In particular devices are
known to monitor pressure, flow and patient effort.
[0005] An existing problem for known devices includes
discriminating between obstructive sleep apnea (OSA) and central
sleep apnea (CSA). OSA is indicative of upper airway collapse and
can be used as an input to auto-titration algorithms for the CPAP
pressure applied or the end-expiratory pressure (EEP) used in a
bi-level device. CSA can be indicative of over-ventilation and can
therefore be used as an input to algorithms that auto-titrate the
ventilation of the patient. Clearly, miscategorizing an apnea as
either closed or open results in these titration algorithms
prescribing sub-optimal parameters for the treatment of the
patient.
[0006] Obstructive and central sleep apnea are discriminated in
known devices by injecting a 1 cm peak-to-peak 4 Hz oscillation
into the treatment pressure waveshape and measuring the resulting 4
Hz flow. The general term for this technique is Forced Oscillation
Technique (FOT). The phasic difference in the flow to the pressure
waveshape is indicative of the compliance of the load which is then
used to deduce if the upper airway is opened or closed.
Unfortunately, this method does not give any information on events
that include upper airway narrowing/closure and simultaneous
central sleep apnea.
[0007] Obstructive and central sleep apnea are also discriminated
in known devices by detecting the cardiogenic flow. The cardiogenic
flow is the airflow induced in the lungs during a heart beat due to
the proximity of the lungs to the heart. During OSA, there is
therefore never any cardiogenic flow. Like the previous solution,
it is also unable to determine if CSA and OSA have occurred
concurrently.
[0008] Another existing problem for known devices includes
inferring high patient respiratory effort. Patient respiratory
effort is a key indicator used by clinicians when evaluating the
acute state of a patient in a number of diseases including sleep
apnea, obstructive lung disease, and various restrictive diseases.
Despite its known value, it has not enjoyed widespread use as
either an input to flow generator titration algorithms or as a
recorded clinical parameter due to the inconvenience or
impracticality of the transducers involved.
[0009] The "gold standard" in terms of accuracy for monitoring
effort is an oesophageal catheter which a patient is required to
swallow. Unfortunately, this is uncomfortable and awkward for a
patient and not practical outside a clinic. Respiratory bands
around the patient's chest and abdomen are known to monitor effort.
Suprasternal notch effort sensors are also known, as well as the
use of EMG and ECG sensors. These techniques are all unsuitable for
home use.
[0010] Another existing problem for known devices includes
measuring and storing vaso-specific parameters, such as cardiac
afterload, vascular tone, heart rate variability, sympathetic
nervous system activity in general, and/or central venous pressure.
If these parameters were available in real-time in a flow
generator, they could be used to (a) contribute to auto-titration
algorithms and (b) be recorded with respiratory specific parameters
to allow physicians to observe long-term trends and have a richer
data set to determine the long term management of the patient.
[0011] Yet another existing problem for known devices includes
limiting the mean mask pressure. Auto-titrating CPAP algorithms
aimed at eliminating OSA or upper airway resistance syndrome (UARS)
may use breath flow analysis to limit upper airway narrowing.
Pressure beyond certain levels may, in some patients, be
deleterious to cardiac function. Equally, a lower pressure may be
beneficial to cardiac function provided it does not result in
complete closure of the upper airway (e.g., a lower pressure may
promote UA closure). It is desirable to include cardiovascular
parameters in auto-titration schemes such that respiratory therapy
(e.g., CPAP pressure) can be continuously optimized. Such
parameters may include cardiac afterload, vascular tone, heart rate
variability, sympathetic nervous system activity in general, and/or
central venous pressure if they could be acquired non-invasively
and conveniently.
[0012] ResMed's AutoSet CS and AutoSet CS2 devices specifically
target patients with heart disease. These devices address the
`excessive CPAP pressure` problem by imposing a maximum average
pressure of 15 cmH.sub.2O.
[0013] Another known sensor is a suprasternal notch effort sensor.
See U.S. Pat. No. 6,445,942 (Berthon-Jones). Other known techniques
for monitoring apneas and hypopneas are described in U.S. Pat. No.
6,091,973 (Colla et al.) and U.S. Pat. No. 6,363,270 (Colla et
al.). Another related U.S. patent is U.S. Pat. No. 5,704,345
(Berthon-Jones) which describes distinguishing open and closed
airway apneas amongst other things. U.S. Pat. No. 6,484,719
(Berthon-Jones) describes a servo-ventilator which uses a flow
sensor. The contents of all these patents are hereby expressly
incorporated by reference herein.
[0014] Thus, a need has developed in the art to overcome one or
more of these and other disadvantages.
SUMMARY OF THE INVENTION
[0015] One aspect of the present invention relates to therapy
through ventilator optimization through one or more of improving
synchrony, pressure support/volume autotitration, reducing
side-effects (e.g., excessive EEP); and patient management (such
as, for example, trending respiration, cardiac/autonomic function,
sleep structure, endothelial function, etc.).
[0016] Another aspect of the present invention relates to therapy
through CPAP optimization, including one or more of OSA/CSA
discrimination, reducing side-effects (e.g., excessive EEP),
auto-titration, and patient management (such as, for example,
trending cardiac/autonomic function, sleep structure, endothelial
function, etc).
[0017] Still another aspect of the present invention relates to
monitoring and/or diagnosis. SDB diagnosis and patient management
may be achieved via oximetry alone or in combination with
respiratory flow (apnealink). SDB diagnosis may include both OSA
and CSA.
[0018] Yet another aspect of the present invention relates to
monitoring and/or diagnosis via early detection of exacerbations in
respiratory disease (e.g., trending).
[0019] An aspect of the present invention relates to monitoring
and/or diagnosis by monitoring of autonomic function, sleep
quality, respiratory timing/effort, and vascular tone (e.g.,
arterial stiffness) in general (including, for example, patient
sub-groups such as cardiac failure, stroke, hypertension,
pediatrics, obesity-hypoventilation syndrome, motor-neurone
disease, etc.).
[0020] It will be appreciated that in any of the above, the PPG
information may stand alone, or be combined/correlated with
respiratory flow or traditional SpO.sub.2 from the oximeter or with
other respiratory monitors such as trans-cutaneous CO.sub.2. It
also will be appreciated that the techniques for monitoring,
detection, and treatment may be used alone or in any
combination.
[0021] Certain example embodiments provide for an air delivery
system, comprising a controllable flow generator operable to
generate a supply of pressurized breathable gas to be provided to a
patient for treatment; a pulse oximeter configured to generate,
during a treatment period, a patient effort signal for input to
control operation of the flow generator; and a controller
configured to derive an estimated breath phase of the patient
independent of measured flow, based at least in part on the patient
effort signal.
[0022] In certain example embodiments, a method for treating sleep
disordered breathing is provided, with the method comprising
deriving a pulse oximeter signal from a patient; processing the
pulse oximeter signal to generate a patient effort signal
indicative of respiratory rate; and deriving an estimated breath
phase of the patient independent of measured flow based at least in
part on the patient effort signal.
[0023] In still other example embodiments, a respiratory effort
monitoring apparatus is provided, comprising a pulse oximeter
configured to derive a pulse oximeter signal; and a signal
processor configured to receive the pulse oximeter signal and
generate signals indicative of respiratory effort. The apparatus
can be used in conjunction with a method for diagnosing SDB,
respiratory disease (including asthma), and/or cardiac failure
(e.g., periodic breathing). The signal processor may be configured
to derive an estimated breath phase of the patient independent of
measured flow based at least in part from the patient effort
signal.
[0024] In other example embodiments, an air delivery system
comprises a controllable flow generator operable to generate a
supply of pressurized breathable gas to be provided to a patient
for treatment; a pulse oximeter; and a controller configured to
determine sleep structure information for the patient is tracked to
indicate a therapy's effectiveness based at least in part on output
from the pulse oximeter.
[0025] Still other certain example embodiments provide a method for
diagnosing sleep disordered breathing, with that method comprising
deriving a pulse oximeter signal from a patient; processing the
pulse oximeter signal to generate a patient effort signal
indicative of respiratory rate; and tracking sleep structure
information for the patient to indicate a therapy's
effectiveness.
[0026] Certain example embodiments provide a respiratory effort
monitoring apparatus, comprising a pulse oximeter configured to
derive a pulse oximeter signal; and a signal processor configured
to receive the pulse oximeter signal and generate a patient effort
signal indicative of respiratory rate; wherein the signal processor
is configured to track sleep structure information for the patient
to indicate a therapy's effectiveness.
[0027] Certain other example embodiments provide an air delivery
system, comprising a controllable flow generator operable to
generate a supply of pressurized breathable gas to be provided to a
patient for treatment; a pulse oximeter configured to determine a
measure of patient effort during a treatment period and provide a
patient effort signal for input to control operation of the flow
generator; and a controller configured to extract information
regarding patient-ventilator asynchrony from the patient effort
signal, and to measure and/or monitor the patient's autonomic
improvement in response to therapy.
[0028] Still other example embodiments provide a method for
monitoring cardio-respiratory data associated with sleep disordered
breathing, with the method comprising deriving a pulse oximeter
signal from a patient; processing the pulse oximeter signal to
generate a patient effort signal indicative of respiratory rate;
measuring and/or monitoring the patient's autonomic improvement in
response to therapy; and extracting patient-ventilator asynchrony
from the patient effort signal.
[0029] In certain example embodiments, a respiratory effort
monitoring apparatus is provided, comprising a controllable flow
generator operable to generate a supply of pressurized breathable
gas to be provided to a patient for treatment; a pulse oximeter
configured to determine a measure of patient effort during a
treatment period and provide a patient effort signal for input to
control operation of the flow generator; and a controller to
extract patient-ventilator asynchrony from the patient effort
signal, and to measure and/or monitor the patient's autonomic
improvement in response to therapy.
[0030] In certain other example embodiments, an air delivery system
for clinical management and/or prediction of respiratory
exacerbations is provided, comprising a controllable flow generator
operable to generate a supply of pressurized breathable gas to be
provided to a patient for treatment; and a pulse oximeter; wherein
the controllable flow generator and/or the pulse oximeter are
configured to determine information relating to relative breathing
effort, breathing frequency, and/or breathing phase.
[0031] In still other example embodiments, a method for clinical
management and/or prediction of respiratory exacerbations is
provided, the method comprising deriving a pulse oximeter signal
from a patient; and processing the pulse oximeter signal to
determine information relating to relative breathing effort,
breathing frequency, and/or breathing phase, with or without
standard oximeter metrics such as oxygen saturation and average
heart-rate.
[0032] Yet other example embodiments provide a respiratory effort
monitoring apparatus for clinical management and/or prediction of
respiratory exacerbations comprising a controllable flow generator
operable to generate a supply of pressurized breathable gas to be
provided to a patient for treatment; and a pulse oximeter; wherein
the controllable flow generator and/or the pulse oximeter are
configured to determine information relating to relative breathing
effort, breathing frequency, and/or breathing phase.
[0033] Certain example embodiments provide an air delivery system
with provision for assessment of endothelial dysfunction comprising
a controllable flow generator operable to generate a supply of
pressurized breathable gas to be provided to a patient for
treatment; a pulse oximeter configured to measure the patient's
vasoconstrictive response to treatment; and a controller configured
to trend the vasoconstrictive response over a given time period to
indicate a change in endothelial function.
[0034] Still other example embodiments provide a method for
assessment of endothelial dysfunction, the method comprising
deriving a pulse oximeter signal from a patient; processing the
pulse oximeter to measure the patient's vasoconstrictive response
to treatment; and trending the vasoconstrictive response over a
given time period to indicate a change in endothelial function.
[0035] Certain other example embodiments provide a method of
monitoring sleep-disordered breathing, the method comprising
deriving a pulse oximeter signal from a patient; processing the
pulse oximeter signal to generate a patient effort signal
indicative of respiratory rate; measuring saturation variation;
and, correlating the patient effort signal with the saturation
variation to detect and/or quantify the level of periodic breathing
by the patient.
[0036] Parameters of interest (e.g., cardiac afterload, vascular
tone, heart rate variability, and/or central venous pressure, etc.)
can be estimated from a pulse oximeter plethysmograph. Currently,
pulse oximeters are primarily employed for monitoring SpO.sub.2 and
heart-rate. Some pulse oximeters display a plethysmograph, but as
far as is known, none of the information present in the
plethysmograph is used as input to auto-titrate respiratory or
cardiovascular therapies. Peripheral Arterial Tone (PAT) is a novel
multi-cell finger plethysmography system that focuses specifically
on arterial tone. This technology may be an alternative to pulse
oximetry as the sensing modality. Pulse-transit time (PTT) also
contains information on autonomic activity and arterial tone.
[0037] Each aspect can be manifested in the form of a method and/or
apparatus for non-invasive monitoring of one or more parameters
relating to the diagnosis of a patient's health disorder, e.g.,
sleep disordered breathing, congestive heart failure, stroke,
respiratory disease, etc., and/or controlling a ventilator or other
respiratory therapy device in accordance with the monitored
parameter and/or the derived diagnosis.
[0038] Another aspect of the invention is to monitor a patient
using pulse oximeter plethysmography without treating them.
[0039] Further aspects of the invention are set out in the attached
claims.
[0040] Other aspects, features, and advantages of this invention
will become apparent from the following detailed description when
taken in conjunction with the accompanying drawings, which are a
part of this disclosure and which illustrate, by way of example,
principles of this invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0041] The accompanying drawings facilitate an understanding of the
various embodiments of this invention. In such drawings:
[0042] FIG. 1 shows a pulse oximeter waveform transformed into an
effort signal.
[0043] FIG. 2 shows a range of pulse oximetry applications in
accordance with various embodiments of the invention.
[0044] FIG. 3 shows a therapy system in accordance with an
embodiment of the invention.
[0045] FIG. 3A is a schematic diagram of a monitoring system
according to an embodiment of the present invention.
[0046] FIG. 4 shows an algorithm for Upper-airway obstruction
(inspiratory flow limitation) and Auto-EEP/AutoCPAP in accordance
with an embodiment of the invention.
[0047] FIG. 5 shows an algorithm for Auto-EEP titration/Automated
Pressure Support titration in accordance with an embodiment of the
invention.
[0048] FIG. 6 shows an algorithm for Detection of elevated
Sympathetic Nervous System (SNS) or reduced cardiac output--cardiac
patients on CPAP/AutoCPAP/Comfort (fixed low-support bilevel)
devices in accordance with an embodiment of the invention.
[0049] FIG. 7 shows an algorithm for AutoCPAP on cardiac patients
in accordance with an embodiment of the invention.
[0050] FIG. 8 is a block diagram illustrating a procedure for
initializing NPPV therapy rate settings, based on respiratory rate
information, in accordance with an embodiment of the present
invention.
[0051] FIG. 9 is a block diagram illustrating a procedure for
initializing NPPV therapy trigger threshold settings, based on
positively identifying cardiogenic flow amplitude, in accordance
with an embodiment of the present invention.
DETAILED DESCRIPTION OF EMBODIMENTS
[0052] Pulse oximeter plethysmography (sometimes referred to simply
as "pulse oximetry" or "photo-plethysmogram") is a standard method
of obtaining blood oxygenation data in a non-invasive and
continuous manner. Oximeters use two wavelengths of light to solve
for hemoglobin saturation. The waveforms are created by the
absorption produced by pulsatile arterial blood volume, which
represents the alternating current (AC) signal. The absorption
produced by nonpulsatile blood, venous and capillary blood, and
tissue absorption is depicted by the direct current (DC) signal.
See Hartert et al., Use of Pulse Oximetry to Recognize Severity of
Airflow Obstruction in Obstructive Airway Disease, Correlation with
Pulsus Paradoxus, Chest 1999:115:475-481. A pulse oximeter signal
from Hartert et al. is shown in FIG. 1.
[0053] Currently, pulse oximeters are primarily employed for
monitoring SpO.sub.2 and heart-rate; however, in accordance with an
embodiment of the invention, the pulse oximeter is used as an
indication of patient effort in a respiratory therapy device.
Respiratory effort can be seen in the arterial blood pressure
waveform as variation in the peak-to-peak amplitude. This is caused
by the effect of the respiratory pleural pressure swings on cardiac
output throughout the breathing cycle. Inspiration is associated
with reduced systolic blood pressure, and this respiratory effect
on blood pressure is referred to as `pulsus paradoxus.`
[0054] This effect has been proposed as a measure of respiratory
loading in various areas (e.g., asthma exacerbation, obstructive
lung disease, etc.), where a variation of >10 mmHg is associated
with high respiratory effort. The reference standard for measuring
arterial blood pressure is invasive (catheter), so indirect methods
are desired. One such method is pulse-transit time (PTT), where the
variation in blood pressure causes a variation in vascular
compliance, transduced as the propagation time of the pulse from
the heart to the periphery. Another method is the oximeter
plethysmographic waveform, which relates the volume of arterial
blood in the tissue bed being sensed (usually finger or ear).
Changes in cardiac output throughout the respiratory cycle may be
seen as variation in the plethysmogram's peak-to-peak amplitude,
consistent with the arterial blood pressure observations. This
variation in cardiac output, combined with the variation in central
venous pressure due to respiration, also induces changes in the
baseline/offset of the PPG signal synchronous with breathing. A
third factor seen in the PPG is affected by breathing: the heart
period is also modulated somewhat by respiration, primarily via the
respiratory neural outflow, and to a lesser extent in response to
the arterial pressure variations induced by respiration.
[0055] Since the pulse oximeter plethysmogram is more related to
volume of blood in the tissues, variation in the baseline/offset of
the pulsatile component may be a more sensitive indicator of
cardiopulmonary interaction than the cardiac output variation
(Hartert et al., Use of Pulse Oximetry to Recognize Severity of
Airflow Obstruction in Obstructive Airway Disease--Correlation with
Pulsus Paradoxis; Chest 1999; 115: 475-481).
[0056] Other factors (e.g., arterial tone, cardiac performance,
postural changes, etc.) can also cause variations in the PPG, so
processing is required to analyze the variation over the
respiratory frequencies, and can be aided further by correlating
the variation with respiratory flow information provided by the
flow generator. A progressive increase in PPpleth (pulsus paradoxus
from the plethysmogram) over a number of breaths (e.g., 3-5) may
indicate increasing efforts associated with impending upper airway
(UA) collapse. A dramatic increase in PPpleth might indicate UA
obstruction.
[0057] The waveform may be characterised into the following
categories:
[0058] (a) Pulsatile amplitude: The AC amplitude of the pulse is
most indicative of vascular compliance, which is greatly affected
by arterial tone/sympathetic nervous system activity when looked at
over 20-30 seconds or greater (for an example of methods, refer Am
J Physiol Heart Circ Physiol 283; H434-H439, 2002). As such, it can
indicate arousal from apnea, and over many days/weeks, may
demonstrate the long-term benefits of abolishing OSA/UARS on SNS
activity. The finger is the best site for detecting the effect of
autonomic activity on vascular compliance. Pulse oximetry at the
ear is less sensitive to autonomic activity, but can offer a
relative estimate of central blood pressure, given that vascular
compliance exerts a lesser effect.
[0059] (b) Offset or baseline: Respiration induces a phasic
variation in the pulse baseline (pulsus paradoxus) that varies in
accordance with respiratory effort (the pressor response). This
effect has been used to identify airway resistance (asthma) and
obstruction (obstructive lung disease). See Comparison of
traditional and plethysmographic methods for measuring pulsus
paradoxus (Clark J et al., Arch Pediatr Adolesc Med 2004. 158:
48-51) and use of pulse oximetry to recognize severity of airflow
obstruction in obstructive airway disease; correlation with pulsus
paradoxus (Hartert et al., Chest 1999. 115: 475-481. Available
online at http://www.chestjournal.org/cgi/reprint/115/2/475).
[0060] (c) Pulse rhythm/timing: Pulse timing and heart period can
shed light on numerous physiological factors, dealt with in turn
below.
Sympatho-Vagal Balance:
[0061] Heart-rate variability indices (HRV, traditionally derived
from ECG signals) can be calculated from the pulse period,
inferring sympatho-vagal balance as performed routinely in ECG
analysis (for an overview, see Circulation 1996; 93:
1043-1065).
Sleep Structure:
[0062] Statistical or fractal analysis of pulse interval data
throughout a night can distinguish sleep-wake state. REM sleep is
similar to wake periods in the fractal component of HRV/heart
period, but the non-REM sleep stages differ significantly from
awake (see
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt-
=Abstract &list_uids=12059594;
http://ajpheart.physiology.org/cgi/reprint/280/1/H17;
http://ajpheart.physiology.org/cgi/reprint/283/1/H434). HRV data
might be analyzed to indicate sleep onset, since the patient must
pass through non-REM sleep prior to achieving REM sleep. Heart-rate
can be obtained via various monitors (e.g., ECG, standard oximeter,
etc.). The PPG inherently contains heart period, and provided this
period information is not averaged, it can be used to conduct
traditional HRV analyses. Thus, the PPG signal provides the
opportunity to capture unfiltered heart-rate data, in contrast to
typical pulse oximeter heart-rate outputs. One method of
discriminating sleep/wake from HRV is taught by Ivanov et al.,
Europhysics Letters 1999, 48(5): 594-6000.
Vascular Tone and Sympathetic Activation:
[0063] Simultaneous access to an ECG signal in addition to the
oximeter pulse timing can offer an indication of vascular tone,
which can augment the sensitivity or specificity of any conclusions
regarding arousal, respiratory effort, or sympathetic tone. The ECG
can indicate the moment of electrical systole, so the time delay
between this central cardiac timing and the arrival of the pulse at
the periphery can define the left-ventricle's pre-ejection period
plus the transit time of the pulse through the systemic arteries to
the peripheral probe site. This overall duration is the traditional
definition of pulse-transit time, or PTT. Pulse transit time is
affected by vascular tone (also referred to as arterial stiffness),
which can be affected by factors such as sympathetic activation and
blood pressure. Consequently PTT can be an indicator of arousal
(transient increases in sympathetic outflow and BP) and an
indicator of average BP/average sympathetic activation when viewed
over longer periods. An alternate means of calculating
pulse-transit time is to replace the timing of electrical systole
with mechanical systole, e.g., cardiogenic flow (CGF) seen in a
respiratory flow signal (e.g., acquired during ventilator or CPAP
therapy or using a nasal pressure transducer), after deducting a
fixed propagation delay from lung to nares. By combining the timing
of the cardiogenic respiratory flow signal with the timing of the
plethysmographic pulse it may be possible to calculate relative
variations in pulse-transit time more accurately than traditional
PTT (pulse transmission time) estimates, since the left-ventricle's
pre-ejection period is not included in the measured duration (the
pre-ejection period is known to sometimes detract from the
sensitivity of the ECG-derived PTT measurement). In addition, by
acquiring the CGF pulse at a consistent place within the
respiratory cycle (end expiration, when it is most readily seen),
the respiratory-induced fluctuations in PTT can be ignored. That
leaves just the PTT variations due to either BP variation or
changes in arterial tone (sympathetic activation), both of which
shorten the PTT, and both of which are associated with arousal,
thereby offering another important SDB parameter. For an overview
of PTT methods and applications, refer to Thorax 1999; 54:
452-458.
[0064] (d) Waveshape: The wave morphology contains similar
information to that seen in arterial catheter pressure signals or
applanation tonometry. Some examples include:
Vascular Tone:
[0065] For example, the position and relative amplitude of the
"dicrotich notch" (e.g., the bump on the trailing shoulder of the
pleth waveform) can point to the degree and timing of pressure-wave
reflections of the forward-going wave from the peripheral
circulation, and may be itself an indicator of vasomotor tone
(SNS). This feature can be trended using established methods: for
example, the first-derivative of the plethysmogram is closely
related to arterial flow to the area, while the second-derivative
of the waveform has been proposed as an indicator of vasoactivity
and arterial compliance (e.g., Hypertension 1998; 32: 365-370).
Cardiac Congestion/Impeded Venous Return:
[0066] Venous pulsation may be apparent in the waveform, which
represents interaction between a number of factors, but in our case
may indicate the effect of excessive CPAP (increased central venous
pressure) or improvement in congestive heart failure (reduced
central venous pressure). Certain examples of venous pulsation
within the PPG waveform are illustrated in chapter 23 of Clinical
Monitoring: Practical applications for anesthesia and critical
care, WB Saunders & Co, 2001.
[0067] Methods for extracting the above parameters from the raw PPG
exist, comprising, for example, time-domain or frequency-domain
signal processing techniques, or elements of both. One example are
the methods taught in WO 03/00125 A1 and WO 2004/075746 A2,
employing the continuous wavelet transform to extract the
respiratory signals and arterial tone information from the raw PPG.
Time-domain analysis of assessing baseline fluctuations from the
PPG are summarized by Shamir et al, British Journal of Anaesthesia
1999, 82(2): 178-81
[0068] Recent developments in oximeter signal processing has
allowed device performance to be more robust when presented with
movement and low perfusion. Modern embedded processors allow more
sophisticated post-processing of plethysmographic waveforms, and
even the most advanced oximeter technology is available as OEM
module format. These technological advances, together with the
underlying information present in the plethysmogram combined with
information from the therapy device, may permit a respiratory
device to employ an oximeter as part of a servo-controlled
therapy.
[0069] The information present in the plethysmogram may be useful
to diagnosis-only studies as well, since it can indicate arousals
that may not be evident as a desaturation.
[0070] Respiratory effects can also be seen as variation in cardiac
timing, termed `respiratory sinus arrhythmia,` which may also be
used to extract respiratory timing information.
[0071] An aspect of the invention relates to the combination of (1)
oximeter plethysmograph-derived parameters with (2) respiratory
flow information, to augment real-time control algorithms for a
respiratory therapy device.
[0072] This arrangement may prove superior to current techniques
if, for example, it permits a more thorough and timely estimate of
the patient's acute condition allowing algorithms to prescribe more
appropriate therapy. The parameters are also optionally stored
within the flow generator to give a physician a richer data set for
long term patient management. This is superior to current
technologies as it gives a physician data from flow generators used
in an unsupervised environment similar to that gained in a sleep
study.
[0073] Plethysmographic parameters useful for titration and long
term patient management include all those noted above (e.g.,
patient effort, vascular compliance, heart rate variability,
arrhythmia detection, venous pulsation, and SpO2, etc.).
[0074] In accordance with an embodiment of the invention, a pulse
oximeter signal 10 is fed through signal processor 20, for example,
a low pass filter, peak detection, nadir detection or averaging.
The filter is designed to remove signals indicative of heart rate
and leave signals indicative of respiratory rate.
[0075] Once the raw PPG signal is acquired from the pulse oximeter,
it may be analyzed in a number of ways as shown in FIG. 2 and
described in further detail below:
[0076] (i) Open-Closed Apnea Discrimination. The
plethysmographically derived respiratory effort estimate can be
used during episodes of apnea (using respiratory flow data) to
indicate whether the apnea is opened (non-obstructed) or closed
(obstructed), useful in automatic titration algorithm logic. For
example, a low or zero flow signal is indicative of an apnea. If
the apnea occurs in the absence of effort as measured by the pulse
oximeter (e.g., no change or reduction in the relative effort
signal), then the apnea is regarded as being "open." However, if
there is effort (e.g., increase in the relative effort signal),
then the apnea is regarded as being "closed."
[0077] (ii) High airway resistance. Similarly, a period of high
respiratory effort derived from the oximeter plethysmograph (e.g.,
increase in the relative effort signal) combined with unchanged or
reduced respiratory flow, or combined with flow limitation
(inferred by flow waveshape, as taught, for example, in U.S. Pat.
Nos. 5,704,345, 6,920,877, and 6,988,994, each of which is
incorporated herein by reference in its entirety) can imply the
presence of significant airway resistance, be it due to expiratory
flow limitation or upper-airway resistance. In both cases, the
combination of high relative effort with unchanged or low measured
respiratory flow may be an indicator to increase applied PEEP.
[0078] (iii) Relative work of breathing: In the absence of
respiratory flow limitation (adjudged from respiratory flow
waveshape or estimated volumes), persistently high respiratory
effort may indicate inadequate pressure support
(under-ventilation).
[0079] (iv) Used in conjunction with a flow based measure of phase
(such as described in U.S. Pat. No. 6,484,719, which is
incorporated herein by reference in its entirety).
[0080] (v) Using the relative effort information to augment
ResMed's AutoSet CPAP algorithm. Increasing patient effort (e.g.,
increase in relative effort over 3-5 breaths) is indicative of
impending upper-airway instability. AutoCPAP titration based on
increased patient effort may be more pre-emptive of obstruction
than the current flattening based algorithm.
[0081] (vi) Using the effort information as a basis for an
algorithm in a ResMed's VPAP or AutoCS device to titrate applied
PEEP. It is conceivable that titration algorithms based on
inspiratory waveshape will be challenged when used in devices that
change the pressure during the breath cycle. Changes in patient
effort may not be as dependent on intra-breath changes in pressure
and hence may be more robust to these types of therapy.
[0082] (vii) Using the relative effort signal as an early indicator
that a patient has been overventilated. This may be a possible
consequence of inappropriate servo-ventilation, where a ventilator
augments the patient's ventilation to achieve a target level. This
indicator can be used to titrate the target ventilation.
[0083] In addition, by offering an alternate estimate of breath
phase independent of measured flow, spontaneous breath phase may be
more accurately assessed, permitting, for example: [0084]
Indication of patient-ventilator asynchrony, useful for acute
ventilatory configuration, for assisting clinical management of
chronically ventilated patients, etc. [0085] Predictive
breath-phase algorithms improving synchrony, particularly in
conditions such as obstructive lung disease where inspiratory flow
is not an accurate indicator of the start of inspiratory
effort.
[0086] (viii) Using venous pulsation as an input to ResMed's
AUTOSET CPAP algorithms for patients with OSA and heart failure.
Increases in venous pulsation can be used to limit the CPAP
pressure applied to safer limits.
[0087] (ix) Using vascular compliance as an input to ResMed's CPAP
algorithms. Changes in vascular compliance can be indicative of
patient arousals. This can be used to augment the data currently
used for automatically prescribing CPAP levels.
[0088] (x) Comparison of the respiratory effort estimate with the
respiratory device's own estimate of breath phase (parameter used
in ResMed's AutoCS2 and AutoVPAP) may allow a more robust
breath-tracking scheme within the respiratory device; for example,
it may improve leak rejection or leak estimation.
[0089] (xi) Sleep state--inference of sleep structure, sleep onset
and sleep termination.
[0090] Analysis of the plethysmographic waveshape, possibly in
combination with other monitored variables, may be used to optimize
CPAP or VPAP therapies to reduce arterial stiffness, independently
associated with poor cardiovascular prognosis. For example:
[0091] (i) Calculation and trending of pulse-transit time (method
outlined in [0057] above). Accurate PTT estimation may offer
additional information to that of the plethysmograph alone,
contributing to the estimation of arterial tone/SNS activity and/or
respiratory effort, and allowing closed-loop therapies aiming to
optimise arterial compliance.
[0092] (ii) The morphology of the plethysmographic waveform may
offer information directly associated with vascular compliance, for
example, the position of the so-called `diochrotic notch` relative
to the initial systolic peak, allowing closed-loop therapies aiming
to optimise arterial compliance.
[0093] With reference to FIG. 3-7 it is noted that:
[0094] THERAPY ALGORITHM adjustments may include: [0095] Level of
PEEP/CPAP [0096] Level of Pressure support
[0097] Concerning the two feedback signals F/B1 and F/B2 it is
noted that:
[0098] F/B1 (Airflow-inferred patient parameter) may include any or
all of the following: [0099] Minute ventilation estimate [0100]
Inspiratory airflow limitation (e.g., UA flattening index) [0101]
Expiratory airflow limitation (e.g., expiratory flow waveform
morphology) [0102] Tidal volume [0103] Leak [0104] Cardiac timing
(time of systolic ejection, extracted from cardiogenic flow) [0105]
Respiratory phase
[0106] F/B 2 (PPG-inferred patient parameter) may include any or
all of the following: [0107] Relative indication of respiratory
effort (e.g., high effort leads to increased respiratory baseline
variation of PPG, pulsus paradoxus) [0108] Absolute indication of
respiratory rate [0109] Patterns of respiratory effort and rate
indicative of respiratory control anomalies or apnea type
(crescendo/decrescendo in breathing effort, statistical derivations
from respiratory patterns) [0110] Indication of respiratory rate
(e.g., variation of PPG amplitude and timing parameters) [0111]
Relative indication of worsening cardiac function (e.g., cardiac
decompensation results in increased respiratory baseline variation
of PPG, pulsus paradoxus) [0112] Relative indication of venous
congestion (e.g., degree of venous pulsation in PPG-morphological
analysis) [0113] Relative variation in sympathetic nervous system
activity or arterial compliance (e.g., variation of PPG pulse
amplitude over >20-30 second timescale, or shift in location of
dicrotic notch) [0114] Standard pulse oximetry (SpO.sub.2) [0115]
Arrival time of systolic pulse at periphery (e.g., systolic rise in
PPG). [0116] Pulse rate
[0117] CLINICAL TARGETS may include: [0118] Minimum ventilation
(e.g., Respiratory Insufficiency, Obesity Hypoventilation patients)
[0119] Nominal ventilation (e.g., Cheyne-Stokes Respiration
patients [0120] Optimal synchrony [0121] Sleep quality (all
patients) [0122] Long-term cardiac function (e.g.,
CHF/CSR/hypertensive patients). [0123] Anticipation/prediction of
cardiac decompensation (e.g., CHF patients) [0124] Optimal arterial
compliance [0125] Minimum CPAP/EEP/PEEP [0126] Maximum
CPAP/EEP/PEEP [0127] Minimum Pressure Support [0128] Maximum
Pressure [0129] Maximum Average Pressure
[0130] FIG. 3A is a schematic diagram for a monitoring system
according to an embodiment of the present invention. Concerning the
feedback signals F/B1 and F/B2, and the "Combined Processing" box,
it is noted that:
[0131] F/B 1 (Airflow-inferred patient parameter) may include any
or all of the following: [0132] Inspiratory airflow limitation
(e.g., UA flattening index) [0133] Expiratory airflow limitation
(e.g., expiratory flow waveform morphology) [0134] Cardiac timing
(time of systolic ejection, extracted from cardiogenic flow) [0135]
Respiratory phase [0136] Time course of breath amplitude and
derived statistics
[0137] F/B 2 (PPG-inferred patient parameter) may include any or
all of the following: [0138] Relative indication of respiratory
effort (e.g., high effort leads to increased respiratory baseline
variation of PPG, pulsus paradoxus), trended over durations
relevant to the application (any amount of time ranging from, for
example, a number of breaths to a number of months). [0139]
Absolute indication of respiratory rate. [0140] Relative indication
of worsening cardiac function (e.g., cardiac decompensation results
in increased respiratory baseline variation of PPG, pulsus
paradoxus) [0141] Relative indication of venous congestion (e.g.,
degree of venous pulsation in PPG-morphological analysis) [0142]
Relative variation in sympathetic nervous system activity or
arterial compliance (e.g., variation of PPG pulse amplitude over
>20-30 second timescale, or shift in location of dicrotic
notch), as seen during arousal from sleep. [0143] Standard pulse
oximetry (SpO.sub.2) [0144] Arrival time of systolic pulse at
periphery (e.g., systolic rise in PPG). [0145] Pulse rate
[0146] COMBINED PROCESSING may include: [0147] F/B 2 alone with no
combined processing (e.g., a single parameter derived from the
oximeter signal). [0148] Delay between respiration changes (F/B 1)
and blood gas adjustments (F/B 2), eg to infer circulatory delay.
[0149] Pulse transit time (PTT) indicated by delay between
cardiogenic flow pulses (F/B 1) and arrival of the pulse at the
periphery (F/B 2). [0150] Multiple parameters within F/B 2 alone
(e.g., respiratory effort and oxygen saturation).
[0151] CLINICAL MONITORING TARGETS may include: [0152] Assessment
of SDB [0153] Assessment of sleep quality (all patients) [0154]
Assessment of cardiac function (e.g., CHF/CSR/hypertensive
patients) as an adjunct to patient management. [0155] Early warning
of exacerbation of respiratory compromise, as common in chronic
obstructive pulmonary disease or asthma.
[0156] FIGS. 4-7 show a number of algorithms performing various
embodiments of the invention. Embodiments of the invention may take
the form of a method and/or apparatus to monitor, in a non-invasive
manner, one or more parameters, e.g., pulse oximetry and/or air
flow, relating, e.g., to a patient's breathing and/or heart
activity.
[0157] The monitored parameter or parameters may be used for
diagnostic purposes, e.g., to log data, to produce a report or an
alarm or otherwise signal a physician. In addition, or in the
alternative, the values of the monitored parameter(s) may be used
to control, e.g., stop, start or vary, the delivery of pressurized
gas (e.g., timing, flow pressure) from a blower, ventilator or the
like, to the patient.
[0158] FIG. 4 shows an open/closed airway apnea algorithm. An
airflow signal is analyzed and a determination is made as to
whether it is within normal bounds. If it is then CPAP/EPAP therapy
is maintained at its current level. If the airflow signal is not
normal, for example low indicative of an apnea, then the effort
signal is analyzed. If the effort is high then an obstructive apnea
may be indicated and the appropriate therapy is to increase the
treatment pressure.
[0159] FIG. 5 shows an algorithm for patients suffering general
respiratory insufficiency. The algorithm defines when pressure
support, or End Expiratory Pressure (EEP) should be varied.
[0160] FIG. 6 shows an algorithm which may be part of a monitoring
system for evaluating cardiac performance. A cardiac patient may be
receiving CPAP therapy and have an additional monitoring device
with the algorithm of FIG. 6. Alternatively the CPAP device may
incorporate the pulse oximeter. The two signals F/B/1 and F/B/2 are
analyzed. Where the values are indicative of elevated levels of SNS
activity, or decompensation (poor cardiac performance) an alert is
generated. The alert may be in the form of an audible alarm, or
part of a messaging system which reports to a physician.
[0161] FIG. 7 depicts an algorithm for cardiac patients on CPAP
therapy. The algorithm is similar to that in FIG. 4. However, it
has the additional step that venous congestion is monitored through
the pulse oximeter. If venous congestion is worsening, then CPAP
pressure will not be increased, but restored to a previous
level.
[0162] FIG. 8 depicts a procedure for initializing NPPV therapy
rate settings, based on respiratory rate information. Preferably,
this is performed after attaching oximeter probe (F/B2), but can be
attached prior to commencing ventilation.
[0163] FIG. 9 depicts a procedure for initializing NPPV therapy
trigger threshold settings, based on positively identifying
cardiogenic flow amplitude. Preferably, this is performed once
ventilation is initiated, e.g., so a respiratory flow signal is
available.
[0164] The combination of traditional oximetry data (saturation,
heart rate, pulse timing information) and respiratory timing and
effort information (inferred from additional processing of a PPG
and/or from the addition of a nasal or oronasal cannulae data) may
permit new diagnostic possibilities. For example: [0165]
Circulatory delay (delay between breathing changes and saturation
changes), an indicator of heart-failure severity or cardiac
decompensation. [0166] `True` Pulse Transit Time (PTT), via the
delay between cardiogenic flow pulses seen by the nasal pressure
transducer at end-expiration (seen at the nares) and the arrival of
pulse at the periphery (from the oximeter plethysmogram), as
described above. [0167] By extracting respiratory effort
information from the raw PPG (pulsus paradoxus) a simple diagnostic
system may offer all the key information required for SDB screening
except sleep staging: breathing pattern, oxygen saturation, arousal
(PTT) or increased systemic vascular resistance, and high effort
periods (apnea discrimination and respiratory-effort related
arousal (RERA) classification). This system may optionally include
a nasal pressure transducer, depending on the relative importance
of the derived signals: nasal airflow combined with respiratory
effort permits straight-forward discrimination between central and
obstructive apneas, but conversely with suitable signal processing,
the same information can be gleaned by combining information from
the PPG. For example, if increased relative breathing effort
precedes oxygen desaturation, an obstructive apnea is discriminated
from a central or mixed apnea, in which the desaturation is not
accompanied or preceded by increased effort. Similarly, classifier
or pattern recognition techniques may be applied to the time course
of breathing effort to distinguish obstructive from central
apnea.
[0168] Other specific examples of where aspects of the invention
may be used include:
[0169] (a) Using respiratory-related cardiac rhythm variations
(e.g., "respiratory sinus arrhythmia") to track and predict
breath-phase, and to use the prediction for ventilator triggering.
Such variations may conveniently be detected in the PPG, but may
also be detected by other cardiac monitoring devices such as ECG
electrodes. Typically the respiratory variation imposed on cardiac
timing occurs too late to be used as a ventilator trigger:
ventilators ideally offer respiratory support coincident with early
inspiration, preferably within 100 msec of the patient's initial
inspiratory effort. Ventilators typically monitor inspiratory flow
or airway pressure variations as a trigger. In severe obstructive
respiratory disorders (e.g., COPD) the respiratory flow or pressure
information is a poor indicator of inspiratory timing. In such
disorders, an alternative `window` into respiratory activity may
offer superior results. Respiration, particularly labored
respiration, is known to affect cardiac timing and cardiac output.
By monitoring cardiac performance over previous breath cycles, and
deriving a respiratory phase signal from cardiac information, the
timing of the next inspiratory effort can be predicted, provided
the latency of extracting the respiratory signal is not excessive
(e.g., more than 1 breath delayed). The central-to-peripheral
propagation time for the pulse is typically around 200 msec (the
"pulse transit time"), and at best the cardiac cycle would offer a
low sample-rate estimate of breath phase (about 4-6 beats per
breath). So it is unlikely that a prediction of start of
inspiration will not offer precise inspiratory timing. However,
such a method still offers significant utility in disease states
such as COPD, where ventilator synchronisation via respiratory flow
is typically very delayed, and where breath timing may be more
entrained than in normal breathing (and therefore predictability
being potentially greater).
[0170] (b) Using heart-period analysis to infer sleep onset within
a sleep-disorder screener device.
[0171] (c) A number of measures also can aid in clinical management
of home, chronic ventilation, and/or CPAP patients. For example,
tracking sleep structure (e.g., start, finish, REM extrapolated
from HRV analysis, etc., as described above) within a therapy
device can indicate the therapy's effectiveness. Patient-ventilator
asynchrony can be extracted from, for example, a PPG spontaneous
effort signal. A patient's autonomic improvement in response to
therapy (e.g., CPAP) can be measured based on HRV analysis and also
can be monitored. An index has been published (Khoo et al., Cardiac
Autonomic Control in Obstructive Sleep Apnea--Efects of Long-term
CPAP Therapy. Am J Respir Crit Care Med Vol 164. pp 807-812, 2001)
that shows a dosage response between CPAP and autonomic nervous
activity (e.g., sympathetic and parasympathetic). The index may
highlight the benefits of CPAP therapy in minimizing the risk of
further adverse vascular events. The index is based on heart-rate
variability (originally as acquired by ECG), corrected for the
effect of respiratory effort on heart-rate. The PPG signal
possesses information on both, so it may by suited to provide this
index for long-term trending.
[0172] (d) Clinical management and prediction of respiratory
exacerbations may be possible, in part, for example, because a
pulse oximeter is comfortable and sufficiently easy to use for it
to be a routine, long-term home nocturnal monitory. As such, for
any chronic respiratory disorder associated with exacerbations that
have progressive onset, early detection may be possible if there
was a device that could infer relative breathing effort, breathing
frequency, breathing phase (including inspiratory and/or expiratory
timing). The PPG signal may provide this, and coupled with the
inherent arterial oxygenation, may offer a coarse representation of
respiratory efficiency (e.g., output vs. input). Applicable
conditions may include, for example, asthma, COPD, and the
like.
[0173] (e) Assessment of endothelial dysfunction: conditions such
as respiratory-related arousal during sleep can be considered an
involuntary intervention to provoke a sympathetic response. The
degree of vasoconstrictive response seen in the PPG acquired from a
finger pulse oximeter, trended over a given period (e.g., days,
weeks, and/or months), may indicate change in endothelial function,
which may be a marker of improving or worsening patient status
(e.g., the onset of pre-eclampsia, etc.). The arousal may be
entirely spontaneous, or if the patient is on CPAP therapy, can be
periodically invoked via a CPAP pressure step-down.
[0174] (f) Detection/diagnosis of periodic breathing, (e.g., in
cardiac failure patients), evident as periodic variation in the
relative respiratory effort signal.
[0175] (g) In a ventilator system equipped with customized PPG
monitoring, detecting dramatic drop in cardiac output (inferred
from PPG amplitude reductions), and asserting an alarm. A drop in
cardiac output may be a consequence of many clinically relevant
circumstances, e.g., applying excessive positive pressure in a
patient with hypovolemia (Yamakage, Can J Anesth 2005 52(2): 207),
excessive dynamic hyperinflation/air trapping (Perel, B J A
76(1):168-169) (Conacher, Lancet 1995 346:448).
[0176] Advantages for the patient include, for example, more
comfort and ease of use. Aspects of the invention provide optimal
therapy without being festooned with sensors, e.g., a finger or ear
probe is sufficient. Advantages for the physician include, for
example, ease to administer. Aspects of the invention provide
simple application, automated therapies, and long term patient
management feedback. Other advantages include less expensive and
improved therapy.
[0177] Although the invention has been described with reference to
particular embodiments, it is to be understood that these
embodiments are merely illustrative of the application of the
principles of the invention. Numerous modifications may be made
therein and other arrangements may be devised without departing
from the spirit and scope of the invention. For example, those
skilled in the art recognize that there are other indications of
upper airway instability, resistance or obstruction which are not
necessarily accompanied by or associated with flow flattening.
[0178] Also, the various embodiments described above may be
implemented in conjunction with other embodiments, e.g., aspects of
one embodiment may be combined with aspects of another embodiment
to realize yet other embodiments. In addition, while the invention
has particular application to patients who suffer from OSA, it is
to be appreciated that patients who suffer from other illnesses
(e.g., congestive heart failure, diabetes, morbid obesity, stroke,
bariatric surgery, etc.) can derive benefit from the above
teachings. Moreover, the above teachings have applicability with
patients and non-patients alike in non-medical applications.
* * * * *
References