U.S. patent application number 13/966123 was filed with the patent office on 2013-12-12 for system and method for facilitating observation of monitored physiologic data.
This patent application is currently assigned to Covidien LP. The applicant listed for this patent is Covidien LP. Invention is credited to Scott Amundson, Keith Batchelder, Paul Mannheimer, James Ochs.
Application Number | 20130331668 13/966123 |
Document ID | / |
Family ID | 41435226 |
Filed Date | 2013-12-12 |
United States Patent
Application |
20130331668 |
Kind Code |
A1 |
Batchelder; Keith ; et
al. |
December 12, 2013 |
SYSTEM AND METHOD FOR FACILITATING OBSERVATION OF MONITORED
PHYSIOLOGIC DATA
Abstract
Present embodiments are directed to a system and method capable
of detecting and graphically indicating physiologic patterns in
patient data. For example, present embodiments may include a
monitoring system that includes a monitor capable of receiving
input relating to patient physiological parameters and providing
indications or alarms related to oxygen saturation declines and
oxygen desaturation patterns associated with sleep apnea. Present
embodiments may include methods and systems for mediating between
alarms and other indicators associated with oxygen desaturation and
ventilatory instability.
Inventors: |
Batchelder; Keith; (New
York, NY) ; Amundson; Scott; (Oakland, CA) ;
Ochs; James; (Seattle, WA) ; Mannheimer; Paul;
(Danville, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Covidien LP |
Mansfield |
MA |
US |
|
|
Assignee: |
Covidien LP
Mansfield
MA
|
Family ID: |
41435226 |
Appl. No.: |
13/966123 |
Filed: |
August 13, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12609344 |
Oct 30, 2009 |
8515513 |
|
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13966123 |
|
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61111620 |
Nov 5, 2008 |
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Current U.S.
Class: |
600/323 |
Current CPC
Class: |
A61B 5/14552 20130101;
A61B 5/14551 20130101; A61B 5/4818 20130101; A61B 5/743
20130101 |
Class at
Publication: |
600/323 |
International
Class: |
A61B 5/1455 20060101
A61B005/1455 |
Claims
1. A computer-implemented method of evaluating a condition of a
patient, the method comprising: receiving data from a sensor
associated with one or more physiological parameters of the
patient; activating a pattern monitoring function that operates on
the data; and adjusting a tolerance of a desaturation monitoring
function that operates on the data when: the pattern monitoring
function is activated, and the tolerance does not meet a
predetermined criterion.
2. The computer-implemented method of claim 1, wherein adjusting
the tolerance of a desaturation monitoring function comprises
increasing the tolerance.
3. The computer-implemented method of claim 1, wherein the
predetermined criterion is an alarm limit, and wherein the
desaturation monitoring function comprises triggering an alarm when
an oxygen desaturation reaches the alarm limit.
4. The computer-implemented method of claim 1, wherein, when
activated, the pattern monitoring function comprises: detecting a
reciprocation in the received data; and qualifying the
reciprocations as indicative of apnea.
5. The computer-implemented method of claim 3, wherein detecting a
reciprocation comprises detecting a reciprocation using an adaptive
window.
6. The computer-implemented method of claim 4, wherein qualifying
the reciprocation comprises: calculating one or more metrics
associated with the reciprocation; comparing the one or more
metrics with one or more respective limits; and qualifying the
reciprocation based at least in part on the comparison.
7. The computer-implemented method of claim 6, wherein the one or
more metrics comprise one or more of fall slope, magnitude, slope
ratio, and path length ratio.
8. The computer-implemented method of claim 6, wherein qualifying
the reciprocation further comprises qualifying the reciprocation
based at least in part on a linear qualification function.
9. The computer-implemented method of claim 8, wherein the linear
qualification function is determined based at least in part on a
least squares analysis.
10. The computer-implemented method of claim 4, further comprising
determining a saturation pattern detection index based at least in
part on a magnitude and variability of a qualified
reciprocation.
11. The computer-implemented method of claim 10, further comprising
determining a frequency of qualified reciprocations, wherein the
saturation pattern detection index is determined when the frequency
reaches a minimum threshold.
12. A system of evaluating a condition of a patient, the system
comprising processing equipment configured for: receiving data from
a sensor associated with one or more physiological parameters of
the patient; activating a pattern monitoring function that operates
on the data; and adjusting a tolerance of a desaturation monitoring
function that operates on the data when: the pattern monitoring
function is activated, and the tolerance does not meet a
predetermined criterion.
13. The system of claim 12, wherein the processing equipment is
further configured for increasing the tolerance.
14. The system of claim 12, wherein the processing equipment is
further configured for: detecting a reciprocation in the received
data; and qualifying the reciprocations as indicative of apnea.
15. The system of claim 12, wherein the predetermined criterion is
an alarm limit, and wherein then processing equipment is further
configured for triggering an alarm when an oxygen desaturation
reaches the alarm limit.
16. A computer-implemented method of evaluating a condition of a
patient, the method comprising: receiving physiological data from a
sensor, wherein the physiological data is associated with one or
more physiological parameters; determining whether a desaturation
monitoring function is active, wherein the desaturation monitoring
function detects oxygen desaturation events in the received
physiological data; activating, when the desaturation monitoring
function is active, a saturation pattern monitoring function,
wherein the saturation pattern monitoring function identifies an
oxygen saturation pattern; determining, in response to the
activation of the pattern monitoring function, whether an alarm
limit of the desaturation monitoring function is associated with a
minimum tolerance; adjusting the alarm limit of the desaturation
monitoring function when the alarm limit is not associated with a
minimum tolerance.
17. The computer-implemented method of claim 16, wherein adjusting
the alarm limit of the desaturation monitoring function comprises
increasing the alarm limit based at least in part on a maximum
tolerance.
18. The computer-implemented method of claim 16 wherein, when
activated, the pattern monitoring function comprises determining a
saturation pattern detection index based at least in part on the
identified oxygen saturation pattern.
19. The computer-implemented method of claim 18, wherein saturation
pattern detection index is based at least in part on an identified
oxygen saturation pattern that is indicative of a frequency of
oxygen desaturation events, and wherein the alarm limit of the
desaturation monitoring function is not associated with a frequency
of oxygen desaturation events.
20. The computer-implemented method of claim 19, wherein the
identified oxygen saturation pattern is indicative of ventilatory
instability of the patient, and wherein the saturation pattern
detection index is indicative of the severity of the ventilatory
instability.
Description
RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/609,344, filed on Oct. 30, 2009, which
claims the benefit of U.S. Provisional Application No. 61/111,620,
filed Nov. 5, 2008, both of which are incorporated herein by
reference in their entireties.
BACKGROUND
[0002] The present disclosure relates generally to user-interface
applications for patient monitoring devices. In particular, present
embodiments relate to display features that facilitate observation
of monitored physiological data with patient monitoring
instruments.
[0003] This section is intended to introduce the reader to various
aspects of art that may be related to various aspects of the
present disclosure, which are described and/or claimed below. This
discussion is believed to be helpful in providing the reader with
background information to facilitate a better understanding of the
various aspects of the present disclosure. Accordingly, it should
be understood that these statements are to be read in this light,
and not as admissions of prior art.
[0004] Patient monitors include medical devices that facilitate
measurement and observation of patient physiological data. For
example, pulse oximeters are a type of patient monitor. A typical
patient monitor cooperates with a sensor to detect and display a
patient's vital signs (e.g., temperature, pulse rate, or
respiratory rate) and/or other physiological measurements (e.g.,
water content of tissue, or blood oxygen level) for observation by
a user (e.g., clinician). For example, pulse oximeters are
generally utilized with related sensors to detect and monitor a
patient's functional oxygen saturation of arterial hemoglobin
(i.e., SpO.sub.2) and pulse rate. Other types of patient monitors
may be utilized to detect and monitor other physiological
parameters. The use of patient monitors may improve patient care by
facilitating supervision of a patient without continuous attendance
by a human observer (e.g., a nurse or physician).
[0005] A patient monitor may include a screen that displays
information relating to operation and use of the patient monitor. A
typical patient monitor screen may display patient data for further
interpretation by a user. Such display information may include
indications that relate to a patient's physiological conditions. In
addition, a patient monitor may also be capable of generating
alarms related to the patient's condition (e.g., changes in a
physiological parameter), as well as alarms related to certain
operating characteristics of the monitor itself (e.g., low battery
alarms). These patient-related alarms may alert a caregiver to
conditions that may benefit from medical intervention. However,
because a monitor may display various patient information that may
be associated with a number of alarms, a monitor may generate so
many alarms that the patient's rest is disturbed, and the caregiver
may not be able to quickly interpret an individual alarm,
particularly if multiple alarms are triggered at once.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] Advantages of present embodiments may become apparent upon
reading the following detailed description and upon reference to
the drawings in which:
[0007] FIG. 1 is a perspective view of an exemplary patient
monitor;
[0008] FIG. 2 is a perspective view of the exemplary patient
monitor in a system with separate devices;
[0009] FIG. 3 is a block diagram of an exemplary electronic
device;
[0010] FIG. 4 is a representation of an exemplary display with
graphical indicators;
[0011] FIG. 5 is an exemplary flowchart depicting a method of
mediating between ventilatory instability alarms and saturation
alarm limit detection alarms;
[0012] FIG. 6 is a block diagram of an exemplary electronic
device;
[0013] FIG. 7 is an exemplary graph of SpO.sub.2 trend data with an
upper band and lower band based on mean and standard deviation
values; and
[0014] FIG. 8 is an exemplary graph including an SpO.sub.2 trend
that contains a ventilatory instability SpO.sub.2 pattern and a
trend of the resulting saturation pattern detection index.
DETAILED DESCRIPTION
[0015] One or more specific embodiments of the present disclosure
will be described below. In an effort to provide a concise
description of these embodiments, not all features of an actual
implementation are described in the specification. It should be
appreciated that in the development of any such actual
implementation, as in any engineering or design project, numerous
implementation-specific decisions must be made to achieve the
developers' specific goals, such as compliance with system-related
and business-related constraints, which may vary from one
implementation to another. Moreover, it should be appreciated that
such a development effort might be complex and time consuming, but
would nevertheless be a routine undertaking of design, fabrication,
and manufacture for those of ordinary skill having the benefit of
this disclosure.
[0016] Present embodiments provide methods and systems for
mediating between multiple alarms and indicators on a medical
device such as a patient monitor. As provided, when an indicator
related to a patient parameter, e.g., oxygen saturation, is
displayed with or without other related indicators, its alarm
settings and/or display characteristics may be altered depending
upon which other indicators and alarms are present. For example, to
efficiently and effectively utilize alarms, it may be useful to
limit alarm settings for metrics that may have overlapping effects.
Specifically, certain aspects of sleep apnea monitoring may overlap
the monitoring of oxygen saturation declines, and, thus, when both
metrics are being monitored, it may be desirable to change the
tolerance settings for oxygen saturation alarms to reduce redundant
alarms. Accordingly, a monitor in accordance with present
embodiments may impose certain tolerance settings for alarms
related to a first metric, such has oxygen saturation declines,
when alarms related to a second metric, such as sleep apnea
detection, are eneabled. Because a user, or in certain embodiments
a manufacturer, may enable or disable certain monitoring
functionalities, for example a sleep apnea monitoring
functionality, the present embodiments may provide a medical device
with alarm management that mediates alarm settings and tolerance
levels to reduce alarm redundancies.
[0017] Medical devices, in the course of monitoring a patient, may
generate certain alerts that provide information to a caregiver
about a patient's physiological condition. For example, a pulse
oximetry device may include a graphical indicator or other display
alert that notifies a caregiver when a patient's oxygen saturation
dips below a certain range or threshold. A caregiver may wish to be
alerted to such changes because declines in oxygen saturation may
be associated with a range of clinical conditions.
[0018] In a patient care environment, a sleeping patient may awaken
before a nurse has had the opportunity to acknowledge the alarm,
investigate the patient's condition, and/or silence the alarm. To
eliminate false alarms that may irritate the patient, the alarm may
include certain limits that may reduce alarm notifications for
short, sudden dips in oxygen saturation that may be associated with
signal interference rather than a change in a patient's condition.
For example, an alarm related to oxygen desaturation may only be
triggered if an oxygen saturation decline lasts for a certain
amount of time or happens with a certain frequency. Such limits may
be part of a more complex algorithm or saturation decline detection
feature for determining whether a user should be notified of a
saturation decline. In addition, the alarm limits may further be
determined by user input. For example, a user may decide that an
alarm should be more tolerant, and, thus, trigger a notification
when the saturation decline is of a greater magnitude lasts for a
longer period of time, and/or occurs with a higher frequency. On
the other hand, alarm limits that are less tolerant, i.e., more
stringent, may trigger a user notification when a relatively
smaller decline in oxygen saturation occurs, or when the decline
lasts for a relatively shorter duration, and/or occurs with a lower
frequency.
[0019] In embodiments, a medical device may facilitate observation
of a patient's oxygen saturation and may generate alarms associated
with both saturation declines and certain patterns associated with
saturation declines. For example, certain patterns in oxygen
saturation data gathered by a medical monitoring system may be used
to assess sleep apnea. Obstructive sleep apnea is a condition in
which a patient's breathing is temporarily interrupted when
sleeping, and may be assessed by looking for characteristic
patterns of ventilatory instability in the oxygen saturation data.
These patterns may be detected by the monitor, and appropriate
indicators of these patterns may be provided on a display with
corresponding alerts and/or alarms.
[0020] In embodiments in which a medical monitor may assess both
ventilatory instability and saturation declines, it may be
advantageous to eliminate or reduce alarms that may be redundant
between these two monitoring functions. For example, in certain
embodiments, a saturation decline detection feature may trigger an
alarm when a certain number of "dips" below threshold occur in a
given time period, i.e., an alarm limit may be associated with a
certain frequency of desaturation events. Additionally, a
ventilatory instability detection feature may also trigger an alarm
when a cluster is detected, which may occur when these dips are
part of a desaturation pattern. In such an exemplary case, the
saturation decline alarm may be redundant, because the ventilatory
instability detection feature may monitor such clusters of
desaturation. In other words, while the pattern detection feature
and the saturation decline detection feature are monitoring
different metrics, their monitoring may overlap and, in
embodiments, may trigger two different alarms in response to the
same oxygen desaturation events. Accordingly, a medical monitor may
adjust its alarm settings and tolerance limits for monitoring both
saturation declines and ventilatory instability to reduce redundant
alarms.
[0021] FIG. 1 is a perspective view of a patient monitor 10 in
accordance with an exemplary embodiment of the present disclosure.
Specifically, the patient monitor 10 illustrated by FIG. 1 is a
pulse oximeter that is configured to detect and monitor blood
oxygen saturation levels, pulse rate, and so forth. It should be
noted that while the illustrated embodiment includes a pulse
oximeter, other embodiments may include different types of patient
monitors 10. For example, the patient monitor 10 may be
representative of a vital signs monitor, a critical care monitor,
an obstetrical care monitor, or the like.
[0022] The illustrated patient monitor 10 includes a front panel 12
coupled to a body 14 of the monitor 10. The front panel 12 includes
a display screen 16 and various indicators 18 (e.g., indicator
lights and display screen graphics) that facilitate operation of
the monitor 10 and observation of a patient's physiological metrics
(e.g., pulse rate). Some of the indicators 18 are specifically
provided to facilitate monitoring of a patient's physiological
parameters. For example, the indicators 18 may include
representations of the most recently measured values for SpO.sub.2,
pulse rate, index values, and pulse amplitude. In embodiments, the
indicators 18 may include an indicator related to ventilatory
instability and an indicator related to oxygen saturation declines.
In an embodiment, one indicator 18 may be a triangular indicator
that is related to an index of ventilatory instability determined
by the monitor 10. When the index increases, the triangle fills
from bottom to top. In an embodiment, the indicators 18 may also
include Sat Seconds indicator that provides an indication related
to low oxygen saturation. Other indicators 18 may be specifically
provided to facilitate operation of the monitor 10. For example,
the indicators 18 may include an A/C power indicator, a low battery
indicator, an alarm silence indicator, a mode indicator, and so
forth. The front panel 12 may also include a speaker 20 for
emitting audible indications (e.g., alarms), a sensor port 22 for
coupling with a sensor 24 (e.g., a temperature sensor, a pulse
oximeter sensor) and other monitor features.
[0023] Additionally, the front panel 12 may include various
activation mechanisms 26 (e.g., buttons and switches) to facilitate
management and operation of the monitor 10. For example, the front
panel 12 may include function keys (e.g., keys with varying
functions), a power switch, adjustment buttons, an alarm silence
button, and so forth. It should be noted that in other embodiments,
the indicators 18 and activation mechanisms 26 may be arranged on
different parts of the monitor 10. In other words, the indicators
18 and activation mechanisms 26 need not be located on the front
panel 12. Indeed, in some embodiments, activation mechanisms 26 are
virtual representations in a display or actual components disposed
on separate devices.
[0024] In some embodiments, as illustrated in FIG. 2, the monitor
10 may cooperate with separate devices, such as a separate screen
28, a wireless remote 30, and/or a keyboard 32. These separate
devices may include some of the indicators 18 and activation
mechanisms 26 described above. For example, buttons 34 on the
remote 30 and/or keyboard 32 may operate as activation mechanisms
26. Specifically, for example, the buttons 34 may cause the monitor
10 to perform specific operations (e.g., power up, adjust a
setting, silence an alarm) when actuated on the separate device.
Similarly, the indicators 18 and/or activation mechanisms 26 may
not be directly disposed on the monitor 10. For example, the
indicators 18 may include icons, indicator lights, or graphics on
the separate screen 28 (e.g., a computer screen). Further, the
activation mechanisms 26 may include programs or graphic features
that can be selected and operated via a display. It should be noted
that the separate screen 28 and/or the keyboard 32 may communicate
directly or wirelessly with the monitor 10.
[0025] FIG. 3 shows an exemplary block diagram of a monitor 10
including saturation pattern detection feature 38 and a saturation
alarm limit detection feature 40. Data 42 collected by a sensor 24
may be processed by the monitor 10 in any suitable manner and
reported to both the saturation pattern detection feature 38 and
the saturation alarm limit detection feature 40. The sensor data 24
may be analyzed by one or both of the saturation pattern detection
feature 38 and the saturation alarm limit detection feature 40
(both discussed below) to provide an output to one or more
notification features 44. The notification features 44 may include
an output for display on a display screen 28, such as a graphical
or text indicator, an output to drive indicator lights, and/or an
output to audible alarm structures, such as speakers.
[0026] FIG. 4 is an exemplary display 50 including exemplary user
notifications of graphical indicators for notifying a healthcare
provider about patient ventilatory instability (via saturation
pattern detection feature 38) and oxygen desaturation (via
saturation alarm limit detection feature 40). In embodiments, a
desaturation indicator 52 may be a Sat Seconds indicator that
relates to oxygen saturation information. As shown, a SatSeconds
indicator may be displayed as a dashed circle that may fill up in
relation to the information provided by a calculation algorithm,
such as one that may be stored in a memory (e.g., a mass storage
device, an EEPROM, an optical memory device, a RAM, a ROM, or any
other device for storing machine-readable instructions) and
executed by a processor as part of a saturation alarm limit
detection feature 40.
[0027] Sat Seconds indicator 52 may assist a healthcare provider in
focusing on desaturations related to a patient condition rather
than short desaturations that may be the result of measurement
anomalies. The Sat Seconds indicator 52 may display results
determined by the saturation alarm limit detection feature 40
(i.e., a Sat Second analyzing function), which in an embodiment
analyzes desaturation events by multiplying their duration
(seconds) by the number of percentage points the patient exceeds
the alarm limit.
[0028] In an embodiment, the saturation alarm limit detection
feature 40 may determine if an oxygen desaturation event has
occurred by analyzing a plot of oxygen saturation versus time. The
Sat Seconds saturation alarm limit detection feature 40 may
integrate the area under the curve of time spent below a certain
oxygen saturation threshold. Accordingly, sudden, short
desaturation readings that may be measurement noise (e.g., that
otherwise may trigger nuisance alarms) may be eliminated from a Sat
Seconds counter clock while more prolonged desaturations may be
counted. In embodiments, a SatSeconds limit, or clock, shown as
indicator may be set to 10, 25, 50 or 100 SatSeconds, with 100
SatSecond representing the highest tolerance (i.e. least stringent
(and 10 SatSeconds representing the lowest tolerance (i.e., most
stringent) settings. In an embodiment, the clock may be set to 100,
and therefore only events that equal or surpass the 100 SatSeconds
limit may trigger an alarm. In addition, the Sat Seconds indicator
52 may fill up in relation to the Sat Seconds count. For example,
the indicator 52 may be full when the count reaches 100. In
embodiments, the saturation alarm limit detection feature 40 may
incorporate techniques such as those provided in U.S. Pat. No.
5,865,736 to Baker Jr. et al., U.S. Pat. No. 6,754,516 to
Mannheimer, and U.S. Pat. No. 7,123,950 to Mannheimer, the
specifications of which are incorporated by reference in their
entireties herein for all purposes.
[0029] In embodiments, the display 50 may also include a Saturation
Pattern Detection (SPD) graphical indicator 54 that may provide
information to a user related to the occurrence, frequency, and/or
magnitude of the patterns detected. The information may be based on
a scoring metric, for example a Saturation Pattern Detection index
(SPDi index), as provided herein, which is proportional to the
magnitude and variability of qualified reciprocations. The SPD
calculation feature may be capable of notifying a user of
ventilatory instability that corresponds to a certain SPDi index
value. In embodiments, when the SPDi is at or above a threshold
setting, the user may be notified via a graphical indicator 54.
[0030] As illustrated in FIG. 4, the graphical indicator 54 may be
represented on display 50 as a dashed triangle that may graphically
fill from top to bottom as a monitored and/or calculated value
increases. For example, in one embodiment, the graphical indicator
54 may gradually fill as the SPDi index calculated by an SPDi
calculation feature increases. Further, the graphical indicator 54
may include a tolerance level indicator 56 that displays an index,
for example 1, 2, or 3, for tolerance or sensitivity settings of
High, Medium, and Low, respectively, for the SPDi calculation
feature. The tolerance settings may set the threshold for
triggering a change in the graphical indicator 54 and/or for
triggering SPD-associated alarms (e.g., audible and/or visual
alarms). As shown in FIG. 4, the graphical indicator 54 may be
empty, indicating that an SPDi index is below a certain
threshold.
[0031] The indicator 54 may have multiple possible display states,
which may include: empty, 25% full, 50% full, 75% full, or 100%
full. In embodiments, the indicator 54 may fill in any suitable
manner. For example, a graphical indicator may have any number of
fill states, e.g., filling up in 10%, 20%, 25%, or 50% increments.
A filled state of the graphical indicator 54 may trigger a primary
or secondary alarm. In an embodiment, a primary alarm, such as a
text alert, may be triggered when the graphical indicator 54 begins
to fill. When the indicator 54 has reached a full state, a
secondary alarm, such as an audio alarm, may then be triggered. The
"filling up" may represent the addition of a fill (e.g., any color
pixels) to the area of the triangle. In one embodiment, the
graphical indicator 54 may fill up when the calculated SPDi index
is higher than a tolerance setting. As noted, the High Tolerance,
Medium Tolerance, and Low Tolerance alarm limits may refer to
certain default values of the SPDi index, such as 24, 15, and 6,
respectively. When the SPDi index is higher than, for example, 24
(High Tolerance setting), the graphical indicator 54 may begin to
fill. In an embodiment, the graphical indicator 54 may begin to
fill up when the SPDi index is lower than but near 24, whereby an
SPDi index of 24 represents a "full" state. In such an embodiment,
the approximately 25% full graphical indicator as shown may
represent an SPDi index of, for example, 18.
[0032] In embodiments, a user may have the ability to change
certain settings on the monitor 10 related to the graphical
indicators and monitoring function. FIG. 5 is a flow chart
depicting a method 58 of mediating between SPD monitoring functions
and indicators and saturation alarm limits (e.g. Sat Seconds
limits) and indicators. In an embodiment, a user may be able to
select an option in which the monitor 10 activates SPD calculation
features 38 and associated indicators and alarms (step 60).
[0033] In embodiments, the SPD feature 38 may be activated only
when a Sat Seconds feature, such as saturation alarm limit feature
40, is also active. In such embodiments, prior to activating the
SPD feature 38, the monitor may establish whether a Sat Seconds
calculation and/or display feature is activated at step 62. In
embodiments in which Sat Seconds is not active and initiation of
the SPD feature 38 is being requested, the monitor 10 may display
an error message or other notification at step 64. In embodiments
in which Sat Seconds is active, the monitor 10 may activate the SPD
feature 38 (step 66) and adjust or mediate the Sat Seconds alarm
and notification settings to account for the additional features
provided in the SPD feature 38. For example, at step 68, a Sat
Seconds alarm limit may be automatically raised to a higher
tolerance upon activation of the SPD feature 38. The monitor 10 may
assess the setting of the Sat Seconds tolerance, and if the
tolerance is not already set to a highest possible level, the
monitor 10 may automatically reset the Sat Seconds tolerance. In
such an embodiment, with a Sat Seconds alarm limit set to a higher
tolerance, alarms associated with oxygen desaturation may occur
less frequently while SPD-associated alarms for ventilatory
instability may be triggered. Because certain aspects of the SPD
calculation feature 38 may overlap with the saturation alarm limit
detection feature 40, by setting a Sat Seconds limit to a higher
tolerance, redundant alarm triggering may be reduced. In addition,
the occurrence of redundant alarms may also be reduced if certain
overlapping monitoring features of the saturation alarm limit
detection feature 40 calculation or algorithm are disabled. For
example, at step 70, a frequency trigger portion of the algorithm
may be disabled. For example, a portion of the saturation alarm
limit detection feature 40 that triggers a Sat Seconds count when a
certain number of oxygen saturation dips occurs within a certain
time period, no matter their magnitude or duration, may be
disabled. Because the SPD calculation feature 38 may monitor and
assess the occurrence of desaturation clusters, it may be redundant
to trigger an additional alarm with the saturation alarm limit
detection feature 40 when clusters of desaturations occur in a
short time period.
[0034] In an embodiment, a user may be able to change the default
values on the limits to user-selected values and override the Sat
Seconds alarm limit. In addition, a user may select between
multiple SPD tolerance settings for High, Medium, or Low Tolerance
of the SPD-associated alarms. In an embodiment, a monitor 10 may
store certain default values associated with SPDi index values.
These default values may be determined based on clinical
observations of a test patient population or other input from
healthcare providers. For example, the default High Tolerance value
may be associated with an SPDi index value of 24. Accordingly, any
SPD-associated alarms may not trigger until the SPDi index for a
calculated window of time is at or near 24.
[0035] In another embodiment, a user may input specific values for
High, Medium, and Low Tolerance limits. A user may select any
value, so long as the High Tolerance limit is higher than the
Medium Tolerance limit, and the Medium Tolerance limit is higher
than the Low Tolerance limit. A monitor 10 may be able trigger an
error message if a user attempts to set a limit of less than zero
or if a user attempts to set a High Tolerance limit that is lower
than a Medium Tolerance limit, and so on.
[0036] Monitoring of ventilatory instability that may be associated
with sleep apnea may be accomplished via saturation pattern
detection feature 38. SPD feature 38 may be stored on a tangible,
computer-readable medium (e.g., a memory) and/or hardware capable
of detecting the presence of a saturation pattern in a series of
physiologic data. For example, FIG. 6 is a block diagram of an
electronic device or pattern detection feature 38 in accordance
with present embodiments. The electronic device is generally
indicated by the reference number 200. The electronic device 200
(e.g., an SpO.sub.2 monitor and/or memory device) may comprise
various subsystems represented as functional blocks in FIG. 6.
Those of ordinary skill in the art will appreciate that the various
functional blocks shown in FIG. 6 may comprise hardware elements
(e.g., circuitry), software elements (e.g., computer code stored on
a hard drive) or a combination of both hardware and software
elements. For example, each functional block may represent software
code and/or hardware components that are configured to perform
portions of an algorithm. Specifically, in the illustrated
embodiment, the electronic device 200 includes a reciprocation
detection (RD) feature 202, a reciprocation qualification (RQ)
feature 204, a cluster determination (CD) feature 206, SPD
calculator feature 208, and a user notification (UN) feature 210.
Each of these components and the coordination of their functions
will be discussed in further detail below.
[0037] It should be noted that, in order to detect certain data
patterns, embodiments of the present disclosure may utilize systems
and methods such as those disclosed in U.S. Pat. No. 6,760,608,
U.S. Pat. No. 6,223,064, U.S. Pat. No. 5,398,682, U.S. Pat. No.
5,605,151, U.S. Pat. No. 6,748,252, U.S. Pat. No. 8,187,201, U.S.
application Ser. No. 11/369,379 filed Mar. 7, 2006, and U.S.
application Ser. No. 11/351,787 filed Feb. 10, 2006. Accordingly,
U.S. Pat. No. 6,760,608, U.S. Pat. No. 6,223,064, U.S. Pat. No.
5,398,682, U.S. Pat. No. 5,605,151, U.S. Pat. No. 6,748,252, U.S.
Pat. No. 8,187,201, U.S. application Ser. No. 11/369,379 filed Mar.
7, 2006, and U.S. application Ser. No. 11/351,787 filed Feb. 10,
2006 are each incorporated herein by reference in their entirety
for all purposes.
[0038] The RD feature 202 may be capable of performing an algorithm
for detecting reciprocations in a data trend. Specifically, the
algorithm of the RD feature 202 may perform a statistical method to
find potential reciprocation peaks and nadirs in a trend of
SpO.sub.2 data. A nadir may be defined as a minimum SpO.sub.2 value
in a reciprocation. The peaks may include a rise peak (e.g., a
maximum SpO.sub.2 value in a reciprocation that occurs after the
nadir) and/or a fall peak (e.g., a maximum SpO.sub.2 value in a
reciprocation that occurs before the nadir). Once per second, the
RD feature 202 may calculate a 12 second rolling mean and standard
deviation of the SpO.sub.2 trend. Further, based on these mean and
standard deviation values, an upper band 220 and lower band 222
with respect to an SpO.sub.2 trend 224, as illustrated by the graph
226 in FIG. 7, may be calculated as follows:
Upper Band=mean+standard deviation;
Lower Band=mean-standard deviation.
[0039] Once the upper band 220 and lower band 222 have been
determined, potential reciprocation peaks and nadirs may be
extracted from the SpO.sub.2 trend 224 using the upper band 220 and
the lower band 224. Indeed, a potential peak may be identified as
the highest SpO.sub.2 point in a trend segment which is entirely
above the upper band 220. Similarly, a potential nadir may be
identified as the lowest SpO.sub.2 point in a trend segment that is
entirely below the lower band 222. In other words, peaks identified
by the RD feature 202 may be at least one standard deviation above
the rolling mean, and nadirs identified by the RD feature 202 may
be at least one standard deviation below the mean. If there is more
than one minimum value below the lower band 222, the last (or most
recent) trend point may be identified as a nadir. If more than one
maximum value is above the upper band 220, the point identified as
a peak may depend on where it is in relation to the nadir. For
example, regarding potential peaks that occur prior to a nadir
(e.g., fall peaks) the most recent maximum trend point may be used.
In contrast, for peaks that occur subsequent to a nadir (e.g., rise
peaks), the first maximum point may be used. In the example trend
data represented in FIG. 7, a peak and nadir is detected
approximately every 30-60 seconds.
[0040] In one embodiment, a window size for calculating the mean
and standard deviation may be set based on historical values (e.g.,
average duration of a set number of previous reciprocations). For
example, in one embodiment, a window size for calculating the mean
and standard deviation may be set to the average duration of all
qualified reciprocations in the last 6 minutes divided by 2. In
another embodiment, an adaptive window method may be utilized
wherein the window size may be initially set to 12 seconds and then
increased as the length of qualified reciprocations increases. This
may be done in anticipation of larger reciprocations because
reciprocations that occur next to each other tend to be of similar
shape and size. If the window remained at 12 seconds, it could
potentially be too short for larger reciprocations and may
prematurely detect peaks and nadirs. The following equation or
calculation is representative of a window size determination,
wherein the output of the filter is inclusively limited to 12-36
seconds, and the equation is executed each time a new reciprocation
is qualified:
TABLE-US-00001 If no qualified reciprocations in the last 6
minutes: Window Size = 12 (initial value) else: RecipDur = 1/2 *
current qualified recip duration + 1/2 * previous RecipDur Window
Size = bound(RecipDur,12,36).
[0041] With regard to SpO.sub.2 signals that are essentially flat,
the dynamic window method may fail to find the three points (i.e.,
a fall peak, a rise peak, and a nadir) utilized to identify a
potential reciprocation. Therefore, the RD feature 202 may limit
the amount of time that the dynamic window method can search for a
potential reciprocation. For example, if no reciprocations are
found in 240 seconds plus the current adaptive window size, the
algorithm of the RD feature 202 may timeout and begin to look for
potential reciprocations at the current SpO.sub.2 trend point and
later. The net effect of this may be that the RD feature 202
detects potential reciprocations less than 240 seconds long.
[0042] Once potential peaks and nadirs are found using the RD
feature 202, the RQ feature 204 may pass the potential
reciprocations through one or more qualification stages to
determine if a related event is caused by ventilatory instability.
A first qualification stage may include checking reciprocation
metrics against a set of limits (e.g., predetermined hard limits).
A second qualification stage may include a linear qualification
function. In accordance with present embodiments, a reciprocation
may be required to pass through both stages in order to be
qualified.
[0043] As an example, in a first qualification stage, which may
include a limit-based qualification, four metrics may be calculated
for each potential reciprocation and compared to a set of limits.
Any reciprocation with a metric that falls outside of these limits
may be disqualified. The limits may be based on empirical data. For
example, in some embodiments, the limits may be selected by
calculating the metrics for potential reciprocations from sleep lab
data where ventilatory instability is known to be present, and then
comparing the results to metrics from motion and breathe-down
studies. The limits may then be refined to filter out true
positives.
[0044] The metrics referred to above may include fall slope,
magnitude, slope ratio, and path length ratio. With regard to fall
slope, it may be desirable to limit the maximum fall slope to
filter out high frequency artifact in the SpO.sub.2 trend, and
limit the minimum fall slope to ensure that slow SpO.sub.2 changes
are not qualified as reciprocations. Regarding magnitude, limits
may be placed on the minimum magnitude because of difficulties
associated with deciphering the difference between ventilatory
instability reciprocations and artifact reciprocations as the
reciprocation size decreases, and on the maximum magnitude to avoid
false positives associated with sever artifact (e.g., brief changes
of more than 35% SpO.sub.2 that are unrelated to actual ventilatory
instability). The slope ratio may be limited to indirectly limit
the rise slope for the same reasons as the fall slope is limited
and because ventilatory instability patterns essentially always
have a desaturation rate that is slower than the resaturation (or
recovery) rate. The path length ratio may be defined as Path
Length/((Fall Peak-Nadir)+(Rise Peak-Nadir)), where Path
Length=.SIGMA.|Current SpO.sub.2 Value-Previous SpO.sub.2 value|
for all SpO.sub.2 values in a reciprocation, and the maximum path
length ratio may be limited to limit the maximum standard deviation
of the reciprocation, which limits high frequency artifact. The
following table (Table I) lists the above-identified metrics along
with their associated equations and the limits used in accordance
with one embodiment:
TABLE-US-00002 TABLE I Metric Equation Minimum Maximum Fall Slope
(Nadir - Fall Peak)/Time -1.6 (Fast -0.08 (Fast between Fall Peak
and Nadir Response Response Mode) Mode) -1 (Normal -0.05 (Normal
Response Response Mode) Mode) Magnitude Max(Rise Peak, Fall Peak) -
3 35 Nadir Slope |Fall Slope/Rise Slope| 0.05 1.75 Ratio Path Path
Length = .sub..SIGMA.|Current N/A 2 Length SpO2 Value - Previous
SpO2 Ratio Value|for all SpO2 values in a Reciprocation. Path
Length Ratio = Path Length/((Fall Peak - Nadir) + (Rise Peak -
Nadir))
[0045] As indicated in Table I above, an oximetry algorithm in
accordance with present embodiments may operate in two response
modes: Normal Response Mode or Fast Response Mode. The selected
setting may change the SpO.sub.2 filtering performed by the
oximetry algorithm, which in turn can cause changes in SpO.sub.2
patterns. Therefore a saturation pattern detection feature may also
accept a response mode so that it can account for the different
SpO.sub.2 filtering. Table I indicates values associated with both
types of response mode with regard to the Fall Slope values.
[0046] A second qualification stage of the RQ feature 204 may
utilize a object reciprocation qualification feature. Specifically,
the second qualification stage may utilize a linear qualification
function based on ease of implementation, efficiency, and ease of
optimization. The equation may be determined by performing a least
squares analysis. For example, such an analysis may be performed
with MATLAB.RTM.. The inputs to the equation may include the set of
metrics described below. The output may be optimized to a maximum
value for patterns where ventilatory instability is known to be
present. The equation may be optimized to output smaller values
(e.g., 0) for other data sets where potential false positive
reciprocations are abundant.
[0047] To simplify optimization, the equation may be factored into
manageable sub-equations. For example, the equation may be factored
into sub-equation 1, sub-equation D, and sub-equation 2, as will be
discussed below. The output of each sub-equation may then be
substituted into the qualification function to generate an output.
The outputs from each of the sub-equations may not be utilized to
determine whether a reciprocation is qualified in accordance with
present embodiments. Rather, an output from a full qualification
function may be utilized to qualify a reciprocation. It should be
noted that the equations set forth in the following paragraphs
describe one set of constants. However, separate sets of constants
may be used based on the selected response mode. For example, a
first set of constants may be used for the Normal Response Mode and
a second set of constants may be used for the Fast Response
Mode.
[0048] Preprocessing may be utilized in accordance with present
embodiments to prevent overflow for each part of the qualification
function. The tables (Tables II-VII) discussed below, which relate
to specific components of the qualification function may
demonstrate this overflow prevention. Each row in a table contains
the maximum value of term which is equal to the maximum value of
the input variable multiplied by the constant, wherein the term
"maximum" may refer to the largest possible absolute value of a
given input. Each row in a table contains the maximum intermediate
sum of the current term and all previous terms. For example, a
second row may contain the maximum output for the second term
calculated, as well as the maximum sum of terms 1 and 2. It should
be noted that the order of the row may match the order that the
terms are calculated by the RQ feature 204. Further, it should be
noted that in the tables for each sub-equation below, equations may
be calculated using temporary signed 32-bit integers, and, thus,
for each row in a table where the current term or intermediate term
sum exceeds 2147483647 or is less than -2147483647 then an
overflow/underflow condition may occur.
[0049] A first sub-equation, sub-equation 1, may use metrics from a
single reciprocation. For example, sub-equation 1 may be
represented as follows:
Eq1Score=SlopeRatio*SrCf+PeakDiffTdCf+FallSlope*FsCf+PathRatio*PrCf+Eq1O-
ffset,
where SrCf, PdCf, FsCf, PrCf, and Eq1Offset may be selected using
least squares analysis (e.g., using MATLAB.RTM.). PeakDiff may be
defined as equal to |Recip Fall Peak-Recip Rise Peak|. It should be
noted that PeakDiff is typically not considered in isolation but in
combination with other metrics to facilitate separation. For
example, a true positive reciprocation which meets other criteria
but has a high peak difference could be an incomplete recovery.
That is, a patient's SpO.sub.2 may drop from a baseline to a
certain nadir value, but then fail to subsequently recover to the
baseline. However, when used in combination with other metrics in
the equation, PeakDiff may facilitate separation of two
classifications, as large peak differences are more abundant in
false positive data sets.
[0050] With regard to sub-equation 1, the tables (Tables II and
III) set forth below demonstrate that the inputs may be
preprocessed to prevent overflow. Further, the tables set forth
below include exemplary limits that may be utilized in sub-equation
1 in accordance with present embodiments. It should be noted that
Table II includes Fast Response Mode constants and Table III
includes Normal Response Mode constants.
TABLE-US-00003 TABLE II Maximum Maximum Constant Maximum
Intermediate Variable Variable Variable Value (b) Term Value Sum
(sum of all Term Type Value (a) Preprocessing (Fast Mode) (a * b)
previous rows) Overflow PeakDiff*PdCf U8 100 None. This value may
-29282 -2928200 -2928200 NO not exceed 100 since the maximum
SpO.sub.2 value accepted is 100 SlopeRatio*SrCf U8 255 None -1534
-391170 -3319370 NO FallSlope*FsCf S16 -32768 None -19 622592
-2696778 NO PathRatio*PrCf U16 65535 None -7982 -523100370
-525797148 NO Eq1Offset N/A N/A N/A 809250 809250 -524987898 NO
TABLE-US-00004 TABLE III Maximum Maximum Constant Maximum
Intermediate Variable Variable Variable Value (b) Term Value Sum
(sum of all Term Type Value (a) Preprocessing (Normal Mode) (a * b)
previous rows) Overflow PeakDiff*PdCf U8 100 None. This value may
-33311 -3331100 -3331100 NO not exceed 100 since the maximum SpO2
value accepted is 100 SlopeRatio*SrCf U8 255 None -2151 -548505
-3879605 NO FallSlope*FsCf S16 -32768 None -706 23134208 19254603
NO PathRatio*PrCf U16 65535 None -6178 -404875230 -385620627 NO
Eq1Offset N/A N/A N/A 576330 576330 -385044297 NO
[0051] A second sub-equation, sub-equation D, may correspond to a
difference between two consecutive reciprocations which have passed
the hard limit qualifications checks, wherein consecutive
reciprocations include two reciprocations that are separated by
less than a defined time span. For example, consecutive
reciprocations may be defined as two reciprocations that are less
than 120 seconds apart. The concept behind sub-equation D may be
that ventilatory instability tends to be a relatively consistent
event, with little change from one reciprocation to the next.
Artifact generally has a different signature and tends to be more
random with greater variation among reciprocations. For example,
the following equation may represent sub-equation D:
EqD=SlopeRatioDiff*SrDCf+DurationDiff*DDCf+NadirDiff*NdCf+PathLengthRati-
oDiff*PrDCf_EqDOffset,
where, SrDCf, DDCf, NdCf, PrDCf, and EqDOffset may be selected
using least squares analysis (e.g., using MATLAB.RTM.). With regard
to other variables in sub-equation D, SlopeRatioDiff may be defined
as |Current Recip Slope Ratio-Slope Ratio of last qualified Recip|;
DurationDiff may be defined as |Current Recip Duration-Duration of
last qualified Recip|; NadirDiff may be defined as |Current Recip
Nadir-Nadir value of last qualified Recip|; and PathLengthRatioDiff
may be defined as |Current Recip Path Length Ratio-Path Length
Ratio of last qualified Recip|.
[0052] With regard to sub-equation D, the tables (Tables IV and V)
set forth below demonstrate that the inputs may be preprocessed to
prevent overflow. Further, the tables set forth below include
exemplary limits that may be utilized in sub-equation D in
accordance with present embodiments. It should be noted that Table
IV includes Fast Response Mode constants and Table V includes
Normal Response Mode constants.
TABLE-US-00005 TABLE IV Maximum Maximum Constant Maximum
Intermediate Variable Variable Variable Value (b) Term Value Sum
(sum of all Term Type Value (a) Preprocessing (Fast Mode) (a * b)
previous rows) Overflow EqDOffset N/A N/A N/A 885030 885030 885030
NO SlopeRatioDiff * SrDCf U8 255 None -2809 -716295 168735 NO
DurationDiff * DDCf U16 240 The Recip -2960 -710400 -541665 NO
detection module may only detect recips less than or equal to 240
seconds long NadirDiff * NdCf U8 100 This value may -13237 -1323700
-1865365 NO not exceed 100 since the maximum SpO2 value accepted is
100 PathLengthRatioDiff * PrDCf U16 65535 None -7809 -511762815
-513628180 NO
TABLE-US-00006 TABLE V Maximum Maximum Constant Maximum
Intermediate Variable Variable Variable Value (b) Term Value Sum
(sum of all Term Type Value (a) Preprocessing (Normal Mode) (a * b)
previous rows) Overflow EqDOffset N/A N/A N/A 847650 847650 847650
NO SlopeRatioDiff * SrDCf U8 255 None -2629 -670395 177255 NO
DurationDiff * DDCf U16 240 The Recip -4282 -1027680 -850425 NO
detection module may only detect recips less than or equal to 240
seconds long NadirDiff * NdCf U8 100 This value may -11705 -1170500
-2020925 NO not exceed 100 since the maximum SpO2 value accepted is
100 PathLengthRatioDiff * PrDCf U16 65535 None -7844 -514056540
-516077465 NO
[0053] A third sub-equation, sub-equation 2, may combine the output
of sub-equation D with the output of sub-equation 1 for a
reciprocation (e.g., a current reciprocation) and a previous
reciprocation. For example, the following equation may represent
sub-equation 2:
Eq2Score=EqDScore*DCf+Eq1ScoreCurrent*CurrEq1Cf+Eq1ScorePrev*PrevEq1Cf,
where DCf, N1Cf, PrevEq1Cf, and Eq2Offset may be selected using
least squares analysis (e.g., using MATLAB.RTM.). With regard to
other variables in sub-equation 2, EqDScore may be described as the
output of sub-equation D; Eq1ScoreCurrent may be described as the
output of sub-equation 1 for a current reciprocation; and
Eq1ScorePrev may be described as the output of sub-equation 1 for
the reciprocation previous to the current reciprocation.
[0054] With regard to sub-equation 2, the tables (Tables VI and
VII) set forth below demonstrate that the inputs may be
preprocessed to prevent overflow. Further, the tables set forth
below include exemplary limits that may be utilized in sub-equation
2 in accordance with present embodiments. It should be noted that
Table VI includes Fast Response Mode constants and Table VII
includes Normal Response Mode constants.
TABLE-US-00007 TABLE VI Maximum Maximum Constant Maximum
Intermediate Variable Variable Variable Value (b) Term Value Sum
(sum of all Term Type Value (a) Preprocessing (Fast Mode) (a * b)
previous rows) Overflow Eq2Offset N/A N/A N/A -203800 -203800
-203800 NO EqDScore * DCf S32 -501590 The largest output 529
-265341110 -265544910 NO for sub-equation D may be -513628180 (see
Table IV). The input value may be scaled by dividing the value by
1024. Therefore the largest input value may be -501590 Eq1ScorePrev
* PrevEq1Cf S32 -512683 The largest output 333 -170723439
-436268349 NO for sub-equation 1 may be -524987898 (see Table II).
The input value may be scaled by dividing the value by 1024.
Therefore the largest input value may be -512683 Eq1ScoreCurrent *
CurrEq1Cf S32 -512683 Same as previous row 617 -316325411
-752593760 NO
TABLE-US-00008 TABLE VII Maximum Maximum Constant Maximum
Intermediate Variable Variable Variable Value (b) Term Value Sum
(sum of all Term Type Value (a) Preprocessing (Normal Mode) (a * b)
previous rows) Overflow Eq2Offset N/A N/A N/A -194550 -194550
-194550 NO EqDScore * DCf S32 -503981 The largest output 532
-268117892 -268312442 NO for sub- equation D may be -516077465 (see
Table V). The input value may be scaled by dividing the value by
1024. Therefore the largest input value may be -503981 Eq1ScorePrev
* PrevEq1Cf S32 -376000 The largest output 496 -186496000
-454808442 NO for sub-equation 1 may be -385024297 (see Table III).
The input value may be scaled by dividing the value by 1024.
Therefore the largest input value may be -376000 Eq1ScoreCurrent *
CurrEq1Cf S32 -376000 Same as previous row 406 -152656000
-607464442 NO
[0055] A qualification function may utilize the output of each of
the equations discussed above (i.e., sub-equation 1, sub-equation
D, and sub-equation 2) to facilitate qualification and/or rejection
of a potential reciprocation. For example, the output of the
qualification function may be filtered with an IIR filter, and the
filtered output of the qualification function may be used to
qualify or reject a reciprocation. An equation for an unfiltered
qualification function output in accordance with present
embodiments is set forth below:
QFUnfiltered=Eq1Score*SingleRecipWt*Eq2Cf+N2Score*MultipleRecipWt*Eq2Cf+-
NConsecRecip*ConsecCf+RecipMax*MaxCf+Artifact %*ArtCf+QFOffset,
where Eq2Cf, ConsecCf, MaxCf, ArtCf, and QFOffset may be selected
using least squares analysis (e.g., using MATLAB.RTM.), and, as
indicated above, Eq1Score may be defined as the output of
sub-equation 1.
[0056] Other metrics in the unfiltered qualification function
include SingleRecipWt, MultipleRecipWt, NConsecRecip, RecipMax, and
Artifact %. With regard to SingleRecipWt and MultipleRecipWt, when
there are two or more consecutive qualified reciprocations (e.g.,
qualified reciprocations that are less than 120 seconds apart)
present, SingleRecipWt may equal 0 and MultipleRecipWt may equal 1.
However, when only a single reciprocation is present, SingleRecipWt
may equal 1 and MultipleRecipWt may equal 0.
[0057] NConseRecip, which may be defined as equal to
max(NConsecRecip`,QFConsecMax), may include a count of the number
of consecutive reciprocations (e.g., reciprocations that are less
than or equal to 120 seconds apart) that have passed the hard limit
checks. The value for NConsecRecip may be reset to 0 whenever a gap
between any two partially qualified reciprocations exceeds 120
seconds. This may be based on the fact that ventilatory instability
is a relatively long lasting event as compared to artifact.
Therefore, as more reciprocations pass the hard limit checks, the
qualification function may begin qualifying reciprocations that
were previously considered marginal. However, to guard against a
situation where something is causing a longer term artifact event
(e.g., interference from nearby equipment), the value may be
clipped to a maximum value to limit the metrics influence on the
qualification function output.
[0058] RecipMax, which may be defined as equal to max(Fall Peak,
Rise Peak), may facilitate making decisions about marginal
reciprocations. Indeed, marginal reciprocations with higher maximum
SpO.sub.2 values may be more likely to get qualified than marginal
reciprocations with lower SpO.sub.2 values. It should be noted that
this metric works in tandem with the NConsecRecip metric, and
multiple marginal reciprocations with lower maximum SpO.sub.2
values may eventually, over a long period of time, get qualified
due to the NConsecRecip metric.
[0059] The metric Artifact % may be defined as an artifact
percentage that is equal to 100*Total Artifact Count/Recip
Duration, where Total Artifact Count is the number of times and
artifact flag was set during the reciprocation. Present embodiments
may include many metrics and equations that are used to set the
artifact flag. Because of this it is a generally reliable
indication of the amount of artifact present in the oximetry system
as a whole. Marginal reciprocations with a high Artifact % are less
likely to be qualified than marginal reciprocations with a low (or
0) artifact percentage.
[0060] A last component of the qualification function may include
an infinite impulse response (IIR) filter that includes
coefficients that may be tuned manually using a tool (e.g., a
spreadsheet) that models algorithm performance. The filtered
qualification function may be represented by the following
equation, which includes different constants for different modes
(e.g., Fast Response Mode and Normal Response Mode):
QFFiltered=SingleRecipWt*QFUnfiltered+((1-a)*QFUnfiltered+a*PrevQFFilter-
ed)*MultipleRecipWt,
where QFUnfiltered may be defined as the current unfiltered
qualification function output; PrevQFFiltered may be defined as the
previous filtered qualification function output; and where the
constat "a" may be set to 0.34 for Fast Response Mode and 0.5 for
Normal Response Mode.
[0061] The filtered output of the qualification function may be
compared to a threshold to determine if the current reciprocation
is the result of RAF or artifact. The optimum threshold may
theoretically be 0.5. However, an implemented threshold may be set
slightly lower to bias the output of the qualification function
towards qualifying more reciprocations, which may result in
additional qualification of false positives. The threshold may be
lowered because, in accordance with present embodiments, a cluster
determination portion of the algorithm, such as may be performed by
the CD feature 206, may require a certain number (e.g., 5) of fully
qualified reciprocations before an index may be calculated, and a
certain number (e.g., at least 2) of consecutive qualified
reciprocations (with no intervening disqualified reciprocations)
within the set of fully qualified reciprocations. Since multiple
reciprocations may be required, the clustering detection method may
be biased toward filtering out false positives. Accordingly, the
reciprocation qualification function threshold may be lowered to
balance the two processes.
[0062] The CD feature 206 may be capable of performing an algorithm
that maintains an internal reciprocation counter that keeps track
of a number of qualified reciprocations that are currently present.
When the reciprocation counter is greater than or equal to a
certain value, such as 5, the clustering state may be set to
"active" and the algorithm may begin calculating and reporting the
SPDi. When clustering is not active (e.g., reciprocation count
<5) the algorithm may not calculate the SPDi. The SPDi may be
defined as a scoring metric associated with the identification of a
saturation trend pattern generated in accordance with present
embodiment and may correlate to ventilatory instability in a
population of sleep lab patients.
[0063] The CD feature 206 may utilize various rules to determine
the reciprocation count. For example, when the clustering state is
inactive, the following rules may be observed: [0064] 1.) If the
distance between qualified reciprocation exceeds 120 seconds, then
the reciprocation count=0; [0065] 2.) If the current reciprocation
is qualified, and the time from the start of the current
reciprocation to the end of the last qualified reciprocation is
<=120 seconds, then the reciprocation count=reciprocation
count+1; [0066] 3.) If the current reciprocation is not qualified,
then the reciprocation count=max(reciprocation count--2, 0). Once
clustering is active, it may remain active until the time between
two qualified reciprocations exceeds 120 seconds. The following
table (Table II) illustrates an example of how the reciprocation
count rules may be applied to determine a clustering state.
TABLE-US-00009 [0066] TABLE VIII Current Time Since Reciprocation
Last Qualified Reciprocation Clustering Qualified Reciprocation
(seconds) Count State TRUE N/A 1 INACTIVE FALSE 60 0 INACTIVE TRUE
N/A 1 INACTIVE FALSE 60 0 INACTIVE TRUE N/A 1 INACTIVE TRUE 30 2
INACTIVE TRUE 120 3 INACTIVE FALSE 60 1 INACTIVE TRUE 10 2 INACTIVE
TRUE 20 3 INACTIVE TRUE 40 4 INACTIVE FALSE 30 2 INACTIVE FALSE 60
0 INACTIVE TRUE N/A 1 INACTIVE TRUE 20 2 INACTIVE TRUE 120 3
INACTIVE TRUE 10 4 INACTIVE FALSE 90 2 INACTIVE TRUE 120 3 INACTIVE
TRUE 60 4 INACTIVE TRUE 20 5 ACTIVE TRUE 30 6 ACTIVE FALSE 50 6
ACTIVE FALSE 100 6 ACTIVE TRUE 121 1 INACTIVE FALSE 50 0 INACTIVE
TRUE N/A 1 INACTIVE TRUE 30 2 INACTIVE TRUE 121 1 INACTIVE TRUE 10
2 INACTIVE TRUE 20 3 INACTIVE TRUE 40 4 INACTIVE TRUE 40 5
ACTIVE
[0067] When the clustering state is active, the SPDi calculation
feature 208 may calculate an unfiltered SPDi for each new qualified
reciprocation. The following formula may be used by the SPDi
calculation feature 208:
Unfiltered SPDi=a*Magnitude+b*PeakDelta+c*NadirDelta; [0068]
wherein a=1.4, b=2.0, c=0.2; [0069] wherein Magnitude=average
magnitude of all reciprocations in the last 6 minutes; [0070]
wherein PeakDelta=average of the three highest qualified
reciprocation rise peaks in the last 6 minutes minus the average of
the three lowest qualified reciprocation rise peaks in the last 6
minutes; and [0071] wherein NadirDelta=average of the three highest
qualified reciprocation nadirs in the last 6 minutes minus the
average of the three lowest qualified reciprocation nadirs in the
last 6 minutes. [0072] Wherein SPDi <=31
[0073] The above formula may be utilized to quantify the severity
of a ventilatory instability pattern. The constants and metrics
used may be based on input from clinical team members. It should be
noted that the PeakDelta parameter may be assigned the largest
weighting constant since the most severe patterns generally have
peak reciprocation values that do not recover to the same
baseline.
[0074] The unfiltered SPDi may be updated whenever clustering is
active and a new qualified reciprocation is detected. Non-zero SPDi
values may be latched for a period of time (e.g., 6 minutes). The
unfiltered SPDi may then be low pass filtered to produce the final
output SPDi value. The following IIR filter with a response time of
approximately 40 seconds may be used:
SPDi=Unfiltered SPDi/a+Previous Filtered SPDi*(a-1)/a;
[0075] wherein a=40.
[0076] FIG. 8 is an exemplary graph 260 including an SpO.sub.2
trend 262 that contains a ventilatory instability SpO.sub.2 pattern
and a trend of the resulting SPDi 264. In the illustrated example,
it should be noted that the SPDi is sensitive to the decreasing
peaks (incomplete recoveries) starting at approximately t=6000.
[0077] The UN feature 210 may be capable of determining if a user
notification function should be employed to notify a user (e.g.,
via a graphical or audible indicator) of the presence of a detected
patterns such as ventilatory instability. The determination of the
UN feature 210 may be based on a user configurable tolerance
setting and the current value of the SPDi. For example, the user
may have four choices for the sensitivity or tolerance setting:
Off, Low, Medium, and High. When the sensitivity or tolerance
setting is set to Off, an alarm based on detection of a saturation
pattern may never be reported to the user. The other three
tolerance settings (i.e., Low, Medium, and High) may each map to an
SPDi threshold value. For example, Low may map to an SPDi threshold
of 6, Medium may map to an SPDi threshold of 15, and High may map
to an SPDi threshold of 24. The thresholds may be based on input
from users. When the SPDi is at or above the threshold for a given
tolerance setting, the user may be notified that ventilatory
instability is present. As discussed below, the indication to the
user may include a graphical designation of the trend data
corresponding to the detected pattern. For example, the trend data
utilized to identify a ventilatory instability pattern may be
highlighted, flashing, or otherwise indicated on a user interface
of a monitor in accordance with present embodiments. Similarly,
parameters such as the SPDi value and the tolerance settings may be
graphically presented on a display.
[0078] While the embodiments of the present disclosure may be
susceptible to various modifications and alternative forms,
specific embodiments have been shown by way of example in the
drawings and will be described in detail herein. However, it should
be understood that the present embodiments are not intended to be
limited to the particular forms disclosed. Rather, present
embodiments are to cover all modifications, equivalents and
alternatives falling within the spirit and scope of present
embodiments as defined by the following appended claims.
* * * * *