U.S. patent application number 13/566874 was filed with the patent office on 2013-02-07 for systems and methods for detecting glucose level data patterns.
This patent application is currently assigned to DexCom, Inc.. The applicant listed for this patent is Robert J. Boock, Leif N. Bowman, Hari Hampapuram, Apurv Ullas Kamath, Phil Mayou, Michael Robert Mensinger, David Price, Eli Reihman, Peter C. Simpson, Kostyantyn Snisarenko, Keri Weindel. Invention is credited to Robert J. Boock, Leif N. Bowman, Hari Hampapuram, Apurv Ullas Kamath, Phil Mayou, Michael Robert Mensinger, David Price, Eli Reihman, Peter C. Simpson, Kostyantyn Snisarenko, Keri Weindel.
Application Number | 20130035865 13/566874 |
Document ID | / |
Family ID | 46690713 |
Filed Date | 2013-02-07 |
United States Patent
Application |
20130035865 |
Kind Code |
A1 |
Mayou; Phil ; et
al. |
February 7, 2013 |
SYSTEMS AND METHODS FOR DETECTING GLUCOSE LEVEL DATA PATTERNS
Abstract
Systems and methods for detecting and reporting patterns in
analyte concentration data are provided. According to some
implementations, an implantable device for continuous measurement
of an analyte concentration is disclosed. The implantable device
includes a sensor configured to generate a signal indicative of a
concentration of an analyte in a host, a memory configured to store
data corresponding at least one of the generated signal and user
information, a processor configured to receive data from at least
one of the memory and the sensor, wherein the processor is
configured to generate pattern data based on the received
information, and an output module configured to output the
generated pattern data. The pattern data can be based on detecting
frequency and severity of analyte data in clinically risky
ranges.
Inventors: |
Mayou; Phil; (San Diego,
CA) ; Hampapuram; Hari; (San Diego, CA) ;
Price; David; (Carlsbad, CA) ; Weindel; Keri;
(Concord, CA) ; Snisarenko; Kostyantyn; (San
Diego, CA) ; Mensinger; Michael Robert; (San Diego,
CA) ; Bowman; Leif N.; (Livermore, CA) ;
Boock; Robert J.; (Carlsbad, CA) ; Kamath; Apurv
Ullas; (San Diego, CA) ; Reihman; Eli; (San
Diego, CA) ; Simpson; Peter C.; (Encinitas,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Mayou; Phil
Hampapuram; Hari
Price; David
Weindel; Keri
Snisarenko; Kostyantyn
Mensinger; Michael Robert
Bowman; Leif N.
Boock; Robert J.
Kamath; Apurv Ullas
Reihman; Eli
Simpson; Peter C. |
San Diego
San Diego
Carlsbad
Concord
San Diego
San Diego
Livermore
Carlsbad
San Diego
San Diego
Encinitas |
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA |
US
US
US
US
US
US
US
US
US
US
US |
|
|
Assignee: |
DexCom, Inc.
San Diego
CA
|
Family ID: |
46690713 |
Appl. No.: |
13/566874 |
Filed: |
August 3, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13566678 |
Aug 3, 2012 |
|
|
|
13566874 |
|
|
|
|
61515786 |
Aug 5, 2011 |
|
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|
61660650 |
Jun 15, 2012 |
|
|
|
Current U.S.
Class: |
702/19 |
Current CPC
Class: |
A61B 5/14532 20130101;
G16H 50/20 20180101; Y02A 90/10 20180101; Y02A 90/26 20180101; G16H
15/00 20180101; G16H 50/70 20180101 |
Class at
Publication: |
702/19 |
International
Class: |
G06F 19/00 20110101
G06F019/00 |
Claims
1. An analyte monitoring system configured to measure an analyte
concentration of a host, the system comprising: a sensor configured
to generate sensor data indicative of a concentration of an analyte
in a host over time; a memory configured to store the sensor data;
a processor configured to receive the sensor data from at least one
of the memory and detecting a pattern in the data, the detecting
comprising identifying a plurality of events based on the sensor
data, associating at least some of the plurality of events based on
a criterion to from a set of events, and qualifying the set of
events as the detected pattern; and an output module configured to
output information representative of the detected pattern.
2. The system of claim 1, wherein the criterion is a timeframe.
3. The system of claim 1, wherein qualifying the set of events as a
pattern comprises determining a priority score associated with the
set.
4. The system of claim 3, wherein the set qualifies as a pattern if
the priority score exceeds a threshold.
5. The system of claim 3, wherein the associating includes forming
a plurality of sets, and wherein the set qualifies as a pattern if
the priority score of the set is greater than a predetermined
number of priority scores associated with the plurality of
sets.
6. The system of claim 3, wherein the qualifying further comprises
scoring each event in a set based on one or more scoring criteria,
wherein the priority score is a summation of the scores associated
with the events in the set.
7. The system of claim 6, wherein the scoring criteria comprising a
time of day associated with the event, wherein events associated
with certain predefined times of day are scored higher than events
associated with other times of day.
8. The system of claim 1, wherein identifying the plurality of
events comprises calculating an average distance for a segment of
time of the sensor data that exceeds a first predetermined
threshold analyte level.
9. The system of claim 8, wherein identifying the plurality of
events further comprises comparing a total amount of time the
segment exceeds a second predetermined threshold analyte level to a
threshold amount of time.
10. The system of claim 9, wherein the first predetermined
threshold analyte level and the second predetermined threshold
analyte level are the same.
11. The system of claim 1, wherein the outputting comprises
displaying the information representative of the detected pattern
on a user interface, wherein the displaying comprises one or more
of displaying a line graph depicting the detected pattern,
highlighting a chart corresponding to the detected pattern and
providing a timeframe in a textual format of a timeframe associated
with the detected pattern.
12. The system of claim 1, further comprising instructions stored
in computer memory, wherein the instructions, when executed by the
processor, cause the processor to perform the detecting and
outputting.
13. A method for identifying patterns based on monitored analyte
concentration sensor data, the method comprising: receiving data
from at least one input, the data including measurements of an
analyte concentration and time of day information associated with
the measurement; analyzing the received data to identify a
plurality of clinically significant events; determining patterns in
the analyzed data, the determining comprising grouping the events
based on time of day information into a plurality of event sets;
and displaying information based on one or more of the determined
patterns.
14. The method of claim 13, wherein the measurements are generated
using a continuous analyte sensor.
15. The method of claim 14, further comprising receiving user input
indicating a timeframe and selecting the measurements that fall
within the timeframe for the analyzing.
16. The method of claim 13, wherein the analyzing comprises
detecting a plurality of episodes, wherein each of the plurality of
episodes is detected by scanning the measurements using predefined
criteria to determine a start and end of each episode.
17. The method of claim 16, wherein analyzing further comprises
qualifying an episode as an event by filtering the episodes based
on one or more episode characteristics.
18. The method of claim 17, wherein the characteristics comprise
one or more of an average distance below a predetermined analyte
level and a total time below a predetermined analyte level.
19. The method of claim 13, wherein grouping the events comprises
calculating times between the events and grouping the events into
sets based on the times.
20. The method of claim 19, wherein calculating the times between
the events comprises calculating a time between the end of a first
event and the end of a second event.
21. The method of claim 19, wherein calculating the times between
the events comprises calculating a time between a nadir point of a
first event and a nadir point of a second event.
22. The method of claim 13, wherein the determining further
comprises selecting one or more sets as patterns, the selecting
comprising filtering the plurality of sets based on a priority
score of each group.
23. The method of claim 22, wherein the filtering filters out sets
that have a priority score less than a threshold amount.
24. The method of claim 22, wherein the filtering filters out sets
that have a priority score that is lower than the priority score of
a predetermined number of other sets of the plurality of sets.
25. The method of claim 13, wherein the measurements are glucose
concentration measurements and the events are hypoglycemic
events.
26. The method of claim 13, wherein the displaying comprises
displaying a timeframe corresponding to the at least one or more
detected patterns.
27. The method of claim 13, wherein the method is performed
automatically upon the expiration of a predetermined amount of
time.
28. The method of claim 13, wherein the method is performed
responsive to user input indicative of a request to initiate
pattern detection.
29. An analyte concentration pattern detection system configured to
perform the method of claim 13, the system comprising a continuous
analyte sensor to generate the analyte concentration measurements,
computer memory to store the generated analyte concentration
measurements, a processor module configured to perform the
analyzing and determining, and a user interface configured to
perform the displaying.
30. The system of claim 29, further comprising instructions stored
in the memory, wherein the instructions, when executed by the
processor module, cause the processor module to perform the
analyzing and determining.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 13/566,678, filed Aug. 3, 2012, which claims the benefit of
U.S. Provisional Appl. No. 61/515,786, filed Aug. 5, 2011, and U.S.
Provisional Appl. No. 61/660,650, filed Jun. 15, 2012, the
disclosure of which is hereby expressly incorporated by reference
in its entirety and is hereby expressly made a portion of this
application.
FIELD OF THE INVENTION
[0002] The embodiments relate generally to systems and methods for
analyzing and detecting patterns in data received from an analyte
sensor, such as a glucose sensor.
BACKGROUND OF THE INVENTION
[0003] Diabetes mellitus is a disorder in which the pancreas cannot
create sufficient insulin (Type I or insulin dependent) and/or in
which insulin is not effective (Type 2 or non-insulin dependent).
In the diabetic state, the victim suffers from high blood sugar,
which causes an array of physiological derangements (kidney
failure, skin ulcers, or bleeding into the vitreous of the eye)
associated with the deterioration of small blood vessels. A
hypoglycemic reaction (low blood sugar) may be induced by an
inadvertent overdose of insulin, or after a normal dose of insulin
or glucose-lowering agent accompanied by extraordinary exercise or
insufficient food intake.
[0004] Conventionally, a diabetic person carries a self-monitoring
blood glucose (SMBG) monitor, which typically requires
uncomfortable finger pricking methods. Due to the lack of comfort
and convenience, a diabetic will normally only measure his or her
glucose level two to four times per day. Unfortunately, these time
intervals are spread so far apart that the diabetic will likely
find out too late, sometimes incurring dangerous side effects, of a
hyperglycemic or hypoglycemic condition. In fact, it is not only
unlikely that a diabetic will take a timely SMBG value, but
additionally the diabetic will not know if his blood glucose value
is going up (higher) or down (lower) based on conventional
methods.
[0005] Consequently, a variety of non-invasive, transdermal (e.g.,
transcutaneous) and/or implantable electrochemical sensors are
being developed for continuously detecting and/or quantifying blood
glucose values. These devices generally transmit raw or minimally
processed data for subsequent analysis by the device or at a remote
device, which can include a display.
[0006] Conventional processing of the raw data by a device are
generally directed towards displaying information to the users
regarding their recent glucose trend and helping them take short
term actions, which in turn helps them stay in the target range and
improves the average glucose over a period of time. Patients may
also review data downloads either on their own or with their health
care physician to decide on longer term behavioral changes.
[0007] However, some issues with conventional tools for analyzing
data exist. Among these issues are the amount of time required to
analyze the data and the lack of user participation in downloading
or analyzing the data. For example, reviewing the downloads using
trend graphs is time consuming and requires some amount of
expertise to detect problem areas. Additionally, many users do not
consider reviewing downloads, or even initiating downloads from the
receivers, and are often unaware of some issues that may exist.
Finally, conventional techniques may provide excessive alerts to
the user, including alerts in response to measurements that do not
pose a risk to the user. As a result, the user may ignore some
important alerts which are provided by the conventional systems to
their detriment.
SUMMARY OF THE INVENTION
[0008] Details of one or more implementations of the subject matter
described in this specification are set forth in the accompanying
drawings and the description below. Other features, aspects, and
advantages will become apparent from the description, the drawings,
and the claims. Note that the relative dimensions of the following
figures may not be drawn to scale.
[0009] Accordingly, in a first aspect, an analyte concentration
monitoring system is provided, comprising: a display screen; one or
more processors; an input module configured to receive sensor data
from an analyte sensor configured to generate sensor data points
indicative of a measured an analyte concentration; memory; and one
or more programs, wherein the one or more programs are stored in
the memory and are configured to be executed by the one or more
processors, the one or more programs including: instructions to
apply a weighted value to each sensor data point falling within a
timeframe of sensor data, the weighted value depending upon a
concentration value of the sensor data; instructions to aggregate
the weighted sensor data according to time of day; and instructions
to display the aggregated, weighted sensor data on a chart.
[0010] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, aggregating
the sensor data according to time of day includes aggregating each
sensor data point that falls within the same five minute interval
of a day.
[0011] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the system
further comprises instructions to highlight significant time ranges
of the displayed sensor data, wherein significant time ranges of
displayed data are determined based on exceeding thresholds for
both frequency and severity.
[0012] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the
determination of significant time ranges has an increased
sensitivity during a predefined nighttime range of time, wherein
increased sensitivity corresponds to lower frequency and severity
thresholds.
[0013] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, displaying
the aggregated, weighted data includes displaying a high pattern
chart and a low pattern chart, wherein sensor data associated with
the high pattern chart are weighted differently than sensor data
displayed in the low pattern chart.
[0014] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the chart
includes an x-axis indicative of the timeframe and a y-axis
indicative of a magnitude, wherein the y-axis scale is a
predetermined percentage that less than a 100% of the maximum
possible sum of weighted values.
[0015] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the system
further comprises instructions to display a pattern summary table
on the display, the pattern summary table indicating a total number
of significant pattern matches and a description of one or more of
the most significant matches, wherein the pattern matches are
grouped into a plurality of categories corresponding to time of day
and glucose level, wherein the most significant pattern match
falling within in each of the four groups can is determined by
based on a total sum of all contributed values within the pattern
match interval.
[0016] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the system
further comprises a user interface incorporating the display
screen, wherein the user interface includes a timeframe selection
control that allows a user to select of modify the timeframe.
[0017] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the
timeframe selection control includes a user selectable drop down
menu configured to allow a user to select or modify a number of
days of the timeframe.
[0018] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the
timeframe selection control includes a slider bar that a user can
drag horizontally to modify the start and end dates of the
timeframe.
[0019] In an embodiment of the first aspect or in combination with
any other one or more embodiments of the first aspect, the chart is
automatically updated based on modification of the timeframe using
the timeframe selection control.
[0020] In a second aspect, a computer-implemented method is
provided for identifying patterns in continuous analyte data, the
method comprising: obtaining analyte data points from computer
memory falling within a designated date range; applying one of more
filters to the analyte data to generate contributor data points;
weighting each the contributor data point based on the contributor
data point's analyte value; assigning each weighted contributor
data point to a matching epoch; identifying if an epoch is a match
based on whether the contributor or contributors assigned to the
epoch meet at least one pattern threshold; determining one or more
patterns by scanning the epochs for flags; and outputting
information representative of the determined one or more
patterns.
[0021] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the
designated date range is at least three days.
[0022] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the one or
more filters comprises filters selected from the group consisting
of: (i) an analyte value filter that filters out analyte data
points that have an analyte concentration value falling outside of
a predetermined analyte level or range of analyte values; (ii) a
time range filter that filters out analyte data points measurements
that falls outside of a time of day range; (iii) a day of the week
filter that filters out analyte data points measured one or more
predetermined days of the week; and (iv) an event filter that
filters out data points that do not fall within a predetermined
amount of time from an occurrence of an event.
[0023] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the event
is an event selected from the group consisting of exercise, a meal,
sleep and administration of a medication.
[0024] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the method
further comprises receiving user input indicative of the event
using a user interface.
[0025] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, weighting
each contributor data point is based on a predetermined weight
assignment map or mathematical assignment function.
[0026] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, weighting
each contributor comprises assigning a weighted value to each
contributor, wherein the assigned weighted value is smaller if the
analyte value is less clinically significant than if the analyte
value is more clinically significant.
[0027] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the
assignment of weighted values is non-linear based on the analyte
value.
[0028] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, each epoch
spans a determined time of day and wherein each contributor is
added to the epoch that spans the corresponding time of day.
[0029] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the pattern
thresholds comprise thresholds selected from the group consisting
of: a threshold minimum number of contributors in an epoch; a
threshold average weighted value of the contributors in an epoch; a
threshold medium weighted value of the contributors in the epoch; a
threshold sum of the weighted values of the contributors in the
epoch; a threshold average difference of the weighted values of the
contributors in the epoch; a threshold standard deviation value of
the weighted values of the contributors in the epoch; and a
threshold correlation value.
[0030] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, at least
one of the one or more pattern thresholds is defined in terms of a
percentage.
[0031] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, an epoch is
flagged when at least some of the one or more thresholds are
satisfied.
[0032] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, determining
the one or more patterns comprises determining a start and an end
of the pattern.
[0033] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, determining
the start and the end of the pattern identifying a predetermined
minimum number of contiguous matching epochs or a predetermined
ratio of contiguous matching to non-matching epochs.
[0034] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the start
of the pattern is determined based on identifying a first threshold
number of contiguous matching epochs and the end of the pattern is
determined based on identifying a second threshold number
non-matching epochs.
[0035] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the
outputted information comprises the start time and the end
time.
[0036] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, determining
the one or more patterns is based on the frequency of matching
epochs and the weighted values of the matching epochs.
[0037] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, wherein the
outputted information comprises a triggering alert notifying a user
of the one or more detected patterns.
[0038] In an embodiment of the second aspect or in combination with
any other one or more embodiments of the second aspect, the
outputted information is processed further to form or modify a
medication administration routine.
[0039] In a third aspect, an analyte concentration pattern
detection system is provided which is configured to perform the
method of the second aspect or any one or more of its associated
embodiments, the system comprising a continuous analyte sensor to
generate the analyte concentration data points, computer memory to
store the generated analyte data points, a processor module
configured to perform the applying, the weighting, the assigning,
the identifying, and the determining.
[0040] In an embodiment of the third aspect, the system further
comprises instructions stored in the memory, wherein the
instructions, when executed by the processor module, cause the
processor module to perform the applying, the weighting, the
assigning, the identifying, and the determining.
[0041] In a fourth aspect, an analyte monitoring system is provided
which is configured to measure an analyte concentration of a host,
the system comprising: a sensor configured to generate sensor data
indicative of a concentration of an analyte in a host over time; a
memory configured to store the sensor data; a processor configured
to receive the sensor data from at least one of the memory and
detecting a pattern in the data, the detecting comprising
identifying a plurality of events based on the sensor data,
associating at least some of the plurality of events based on a
criterion to from a set of events, and qualifying the set of events
as the detected pattern; and an output module configured to output
information representative of the detected pattern.
[0042] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the
criterion is a timeframe.
[0043] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, qualifying
the set of events as a pattern comprises determining a priority
score associated with the set.
[0044] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the set
qualifies as a pattern if the priority score exceeds a
threshold.
[0045] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the
associating includes forming a plurality of sets, and wherein the
set qualifies as a pattern if the priority score of the set is
greater than a predetermined number of priority scores associated
with the plurality of sets.
[0046] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the
qualifying further comprises scoring each event in a set based on
one or more scoring criteria, wherein the priority score is a
summation of the scores associated with the events in the set.
[0047] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the scoring
criteria comprising a time of day associated with the event,
wherein events associated with certain predefined times of day are
scored higher than events associated with other times of day.
[0048] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, identifying
the plurality of events comprises calculating an average distance
for a segment of time of the sensor data that exceeds a first
predetermined threshold analyte level.
[0049] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, identifying
the plurality of events further comprises comparing a total amount
of time the segment exceeds a second predetermined threshold
analyte level to a threshold amount of time.
[0050] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the first
predetermined threshold analyte level and the second predetermined
threshold analyte level are the same.
[0051] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the
outputting comprises displaying the information representative of
the detected pattern on a user interface, wherein the displaying
comprises one or more of displaying a line graph depicting the
detected pattern, highlighting a chart corresponding to the
detected pattern and providing a timeframe in a textual format of a
timeframe associated with the detected pattern.
[0052] In an embodiment of the fourth aspect or in combination with
any other one or more embodiments of the fourth aspect, the system
further comprises instructions stored in computer memory, wherein
the instructions, when executed by the processor, cause the
processor to perform the detecting and outputting.
[0053] In a fifth aspect, a method is provided for identifying
patterns based on monitored analyte concentration sensor data, the
method comprising: receiving data from at least one input, the data
including measurements of an analyte concentration and time of day
information associated with the measurement; analyzing the received
data to identify a plurality of clinically significant events;
determining patterns in the analyzed data, the determining
comprising grouping the events based on time of day information
into a plurality of event sets; and displaying information based on
one or more of the determined patterns.
[0054] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
measurements are generated using a continuous analyte sensor.
[0055] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the method
further comprises receiving user input indicating a timeframe and
selecting the measurements that fall within the timeframe for the
analyzing.
[0056] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
analyzing comprises detecting a plurality of episodes, wherein each
of the plurality of episodes is detected by scanning the
measurements using predefined criteria to determine a start and end
of each episode.
[0057] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, analyzing
further comprises qualifying an episode as an event by filtering
the episodes based on one or more episode characteristics.
[0058] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
characteristics comprise one or more of an average distance below a
predetermined analyte level and a total time below a predetermined
analyte level.
[0059] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, grouping the
events comprises calculating times between the events and grouping
the events into sets based on the times.
[0060] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, calculating
the times between the events comprises calculating a time between
the end of a first event and the end of a second event.
[0061] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, calculating
the times between the events comprises calculating a time between a
nadir point of a first event and a nadir point of a second
event.
[0062] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
determining further comprises selecting one or more sets as
patterns, the selecting comprising filtering the plurality of sets
based on a priority score of each group.
[0063] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
filtering filters out sets that have a priority score less than a
threshold amount.
[0064] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
filtering filters out sets that have a priority score that is lower
than the priority score of a predetermined number of other sets of
the plurality of sets.
[0065] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
measurements are glucose concentration measurements and the events
are hypoglycemic events.
[0066] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the
displaying comprises displaying a timeframe corresponding to the at
least one or more detected patterns.
[0067] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the method
is performed automatically upon the expiration of a predetermined
amount of time.
[0068] In an embodiment of the fifth aspect or in combination with
any other one or more embodiments of the fifth aspect, the method
is performed responsive to user input indicative of a request to
initiate pattern detection.
[0069] In a sixth aspect, an analyte concentration pattern
detection system is provided which is configured to perform the
method according to the fifth aspect or any one or more of its
associated embodiments, the system comprising a continuous analyte
sensor to generate the analyte concentration measurements, computer
memory to store the generated analyte concentration measurements, a
processor module configured to perform the analyzing and
determining, and a user interface configured to perform the
displaying.
[0070] In an embodiment of the sixth aspect, the system further
comprises instructions stored in the memory, wherein the
instructions, when executed by the processor module, cause the
processor module to perform the analyzing and determining.
[0071] In a seventh aspect, a method is provided for alerting a
user based on measurements of an analyte concentration, the method
comprising: receiving data from at least one input, the data
including measurements of an analyte concentration and time of day
information; analyzing the received data; detecting a hypoglycemic
event based on the data exceeding at least one predetermined
threshold; and displaying a trend graph of glucose measurements
over time on a user interface, wherein the trend graph includes an
indication of a hypoglycemic reoccurrence risk associated with a
predetermined amount of time following the detected hypoglycemic
event.
[0072] In an embodiment of the seventh aspect or in combination
with any other one or more embodiments of the seventh aspect,
detecting the hypoglycemia event comprises determining an average
distance and a time a segment of time the measured analyte
concentration is below a predetermined analyte concentration
level.
[0073] In an embodiment of the seventh aspect or in combination
with any other one or more embodiments of the seventh aspect, the
method of Claim C1 is performed only when a user-selectable setting
is turned on.
[0074] In an embodiment of the seventh aspect or in combination
with any other one or more embodiments of the seventh aspect, the
predetermined amount of time is 48 hours.
[0075] In an embodiment of the seventh aspect or in combination
with any other one or more embodiments of the seventh aspect, the
method further comprises triggering an audible or visual alert
using the user interface responsive to detecting the hypoglycemic
event and detecting that a current rate of change of the analyte
concentration exceeds a predetermined threshold.
[0076] In an embodiment of the seventh aspect or in combination
with any other one or more embodiments of the seventh aspect, the
method further comprises triggering an audible or visual alert
using the user interface responsive to detecting the hypoglycemic
event and detecting that the current analyte concentration is below
a predetermined threshold.
[0077] In an eighth aspect, an analyte concentration pattern
detection system is provided which is configured to perform the
method according to the seventh aspect or any one or more of its
associated embodiments, the system comprising a continuous analyte
sensor to generate the analyte concentration measurements, computer
memory to store the generated analyte concentration measurements, a
processor module configured to perform the detecting, and a user
interface configured to perform the displaying.
[0078] In an embodiment of the eighth aspect, the system further
comprises instructions stored in the memory, wherein the
instructions, when executed by the processor module, cause the
processor module to perform the detecting.
BRIEF DESCRIPTION OF THE DRAWINGS
[0079] FIG. 1 is a diagram illustrating one embodiment of a
continuous analyte sensor system including a sensor electronics
module.
[0080] FIG. 2A is a perspective view of a sensor system including a
mounting unit and sensor electronics module attached thereto
according to one embodiment.
[0081] FIG. 2B is a side view of the sensor system of FIG. 2B.
[0082] FIG. 3 is an exemplary block diagram illustrating various
elements of one embodiment of a continuous analyte sensor system
and display device.
[0083] FIG. 4 illustrates a flowchart of a method of detecting
patterns according to some embodiments.
[0084] FIGS. 5A-5E illustrate example graphs of glucose levels over
a time period according to some embodiments.
[0085] FIG. 6 illustrates a flowchart of a pattern detection method
according to some embodiments.
[0086] FIG. 7 illustrates a flowchart of a pattern detection method
according to some embodiments.
[0087] FIGS. 8A-8D illustrate plots of weighted assignment maps
according to some embodiments.
[0088] FIG. 9 illustrates another example of a weighted mapping of
glucose values according to some embodiments.
[0089] FIG. 10 illustrates an example of a user interface for
displaying pattern results according to some embodiments.
[0090] FIG. 11 illustrates another graphical user interface for
displaying pattern results according to some embodiments.
[0091] FIG. 12 relates to a date slider control for selecting a
date timeframe according to some embodiments
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0092] The following description and examples illustrate some
exemplary embodiments of the disclosed invention in detail. Those
of skill in the art will recognize that there are numerous
variations and modifications of this invention that are encompassed
by its scope. Accordingly, the description of a certain exemplary
embodiment should not be deemed to limit the scope of the present
invention.
DEFINITIONS
[0093] In order to facilitate an understanding of the systems and
methods discussed herein, a number of terms are defined below. The
terms defined below, as well as other terms used herein, should be
construed to include the provided definitions, the ordinary and
customary meaning of the terms, and any other implied meaning for
the respective terms. Thus, the definitions below do not limit the
meaning of these terms, but only provide exemplary definitions.
[0094] The term "analyte" as used herein is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
substance or chemical constituent in a biological fluid (for
example, blood, interstitial fluid, cerebral spinal fluid, lymph
fluid or urine) that can be analyzed. Analytes can include
naturally occurring substances, artificial substances, metabolites,
and/or reaction products. In some embodiments, the analyte for
measurement by the sensor heads, devices, and methods is analyte.
However, other analytes are contemplated as well, including but not
limited to acarboxyprothrombin; acylcarnitine; adenine
phosphoribosyl transferase; adenosine deaminase; albumin;
alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),
histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,
tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;
arginase; benzoylecgonine (cocaine); biotinidase; biopterin;
c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin;
chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase;
conjugated 1-B hydroxy-cholic acid; cortisol; creatine kinase;
creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine;
de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA
(acetylator polymorphism, alcohol dehydrogenase, alpha
1-antitrypsin, cystic fibrosis, Duchenne/Becker muscular dystrophy,
analyte-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S,
hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab,
beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber
hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,
sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;
dihydropteridine reductase; diptheria/tetanus antitoxin;
erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty
acids/acylglycines; free B-human chorionic gonadotropin; free
erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine
(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;
galactose-1-phosphate uridyltransferase; gentamicin;
analyte-6-phosphate dehydrogenase; glutathione; glutathione
perioxidase; glycocholic acid; glycosylated hemoglobin;
halofantrine; hemoglobin variants; hexosaminidase A; human
erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone;
hypoxanthine phosphoribosyl transferase; immunoreactive trypsin;
lactate; lead; lipoproteins ((a), B/A-1, B); lysozyme; mefloquine;
netilmicin; phenobarbitone; phenytoin; phytanic/pristanic acid;
progesterone; prolactin; prolidase; purine nucleoside
phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium;
serum pancreatic lipase; sissomicin; somatomedin C; specific
antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,
arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus
medinensis, Echinococcus granulosus, Entamoeba histolytica,
enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B
virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus,
Leishmania donovani, leptospira, measles/mumps/rubella,
Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca
volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus,
Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia
(scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma
pallidium, Trypanosoma cruzi/rangeli, vesicular stomatis virus,
Wuchereria bancrofti, yellow fever virus); specific antigens
(hepatitis B virus, HIV-1); succinylacetone; sulfadoxine;
theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding
globulin; trace elements; transferring; UDP-galactose-4-epimerase;
urea; uroporphyrinogen I synthase; vitamin A; white blood cells;
and zinc protoporphyrin. Salts, sugar, protein, fat, vitamins, and
hormones naturally occurring in blood or interstitial fluids can
also constitute analytes in certain embodiments. The analyte can be
naturally present in the biological fluid, for example, a metabolic
product, a hormone, an antigen, an antibody, and the like.
Alternatively, the analyte can be introduced into the body, for
example, a contrast agent for imaging, a radioisotope, a chemical
agent, a fluorocarbon-based synthetic blood, or a drug or
pharmaceutical composition, including but not limited to insulin;
ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish);
inhalants (nitrous oxide, amyl nitrite, butyl nitrite,
chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine);
stimulants (amphetamines, methamphetamines, Ritalin, Cylert,
Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants
(barbituates, methaqualone, tranquilizers such as Valium, Librium,
Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine,
lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin,
codeine, morphine, opium, meperidine, Percocet, Percodan,
Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs
(analogs of fentanyl, meperidine, amphetamines, methamphetamines,
and phencyclidine, for example, Ecstasy); anabolic steroids; and
nicotine. The metabolic products of drugs and pharmaceutical
compositions are also contemplated analytes. Analytes such as
neurochemicals and other chemicals generated within the body can
also be analyzed, such as, for example, ascorbic acid, uric acid,
dopamine, noradrenaline, 3-methoxytyramine (3MT),
3,4-Dihydroxyphenylacetic acid (DOPAC), Homovanillic acid (HVA),
5-Hydroxytryptamine (5HT), and 5-Hydroxyindoleacetic acid
(FHIAA).
[0095] The term "A/D Converter" as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to
hardware and/or software that converts analog electrical signals
into corresponding digital signals.
[0096] The terms "processor module," "microprocessor" and
"processor" as used herein are broad terms and are to be given
their ordinary and customary meaning to a person of ordinary skill
in the art (and are not to be limited to a special or customized
meaning), and furthermore refer without limitation to a computer
system, state machine, and the like that performs arithmetic and
logic operations using logic circuitry that responds to and
processes the basic instructions that drive a computer.
[0097] The terms "sensor data", as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and are not to be limited to a special
or customized meaning), and furthermore refers without limitation
to any data associated with a sensor, such as a continuous analyte
sensor. Sensor data includes a raw data stream, or simply data
stream, of analog or digital signal directly related to a measured
analyte from an analyte sensor (or other signal received from
another sensor), as well as calibrated and/or filtered raw data. In
one example, the sensor data comprises digital data in "counts"
converted by an A/D converter from an analog signal (e.g., voltage
or amps) and includes one or more data points representative of a
glucose concentration. Thus, the terms "sensor data point" and
"data point" refer generally to a digital representation of sensor
data at a particular time. The term broadly encompasses a plurality
of time spaced data points from a sensor, such as a from a
substantially continuous glucose sensor, which comprises individual
measurements taken at time intervals ranging from fractions of a
second up to, e.g., 1, 2, or 5 minutes or longer. In another
example, the sensor data includes an integrated digital value
representative of one or more data points averaged over a time
period. Sensor data may include calibrated data, smoothed data,
filtered data, transformed data, and/or any other data associated
with a sensor.
[0098] The term "calibration" as used herein is a broad term and is
to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
process of determining a relationship between a raw data stream and
corresponding reference data, which can be used to convert raw data
into calibrated data (defined below). In some embodiments, such as
continuous analyte sensors, for example, calibration can be updated
or recalibrated over time as changes in the relationship between
the raw data and reference data occur, for example, due to changes
in sensitivity, baseline, transport, metabolism, and the like.
[0099] The terms "calibrated data" and "calibrated data stream" as
used herein are broad terms and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are
not to be limited to a special or customized meaning), and
furthermore refer without limitation to data that has been
transformed from its raw state to another state using a function,
for example a conversion function, to provide a meaningful value to
a user.
[0100] The terms "smoothed data" and "filtered data" as used herein
are broad terms and are to be given their ordinary and customary
meaning to a person of ordinary skill in the art (and are not to be
limited to a special or customized meaning), and furthermore refer
without limitation to data that has been modified to make it
smoother and more continuous and/or to remove or diminish outlying
points, for example, by performing a moving average of the raw data
stream. Examples of data filters include FIR (finite impulse
response), IIR (infinite impulse response), moving average filters,
and the like.
[0101] The terms "smoothing" and "filtering" as used herein are
broad terms and are to be given their ordinary and customary
meaning to a person of ordinary skill in the art (and are not to be
limited to a special or customized meaning), and furthermore refer
without limitation to a mathematical computation that attenuates or
normalizes components of a signal, such as reducing noise errors in
a raw data stream. In some embodiments, smoothing refers to
modification of a data stream to make it smoother and more
continuous or to remove or diminish outlying data points, for
example, by performing a moving average of the raw data stream.
[0102] The term "noise signal" as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
signal associated with noise on the data stream (e.g., non-analyte
related signal). The noise signal can be determined by filtering
and/or averaging, for example. In some embodiments, the noise
signal is a signal residual, delta residual (difference of
residual), absolute delta residual, and/or the like, which are
described in more detail elsewhere herein.
[0103] The term "algorithm" as used herein is a broad term and is
to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
computational process (associated with computer programming or
other written instructions) involved in transforming information
from one state to another.
[0104] The term "matched data pairs" as used herein is a broad term
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and furthermore refers without limitation
to reference data (for example, one or more reference analyte data
points) matched with substantially time corresponding sensor data
(for example, one or more sensor data points).
[0105] The term "counts" as used herein is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
unit of measurement of a digital signal. In one example, a raw data
stream measured in counts is directly related to a voltage (e.g.,
converted by an A/D converter), which is directly related to
current from the working electrode. In another example, counter
electrode voltage measured in counts is directly related to a
voltage.
[0106] The term "sensor" as used herein is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to
any device (or portion of a device) that measures a physical
quantity and converts it into a signal that can be processed by
analog and/or digital circuitry. Thus, the output of a sensor may
be an analog and/or digital signal. Examples of sensors include
analyte sensors, glucose sensors, temperature sensors, altitude
sensors, accelerometers, and heart rate sensors.
[0107] The terms "glucose sensor" as used herein is a broad term
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without
limitation to any sensor by which glucose can be quantified (e.g.,
enzymatic or non-enzymatic). For example, some embodiments of a
glucose sensor may utilize a membrane that contains glucose oxidase
that catalyzes the conversion of oxygen and glucose to hydrogen
peroxide and gluconate, as illustrated by the following chemical
reaction:
Glucose+O.sub.2.fwdarw.Gluconate+H.sub.2O.sub.2
[0108] Because for each glucose molecule metabolized, there is a
proportional change in the co-reactant O.sub.2 and the product
H.sub.2O.sub.2, one can use an electrode to monitor the current
change in either the co-reactant or the product to determine
glucose concentration.
[0109] The terms "coupled", "operably connected" and "operably
linked" as used herein are broad terms and are to be given their
ordinary and customary meaning to a person of ordinary skill in the
art (and are not to be limited to a special or customized meaning),
and furthermore refer without limitation to one or more components
being linked to another component(s), either directly or
indirectly, in a manner that allows transmission of signals between
the components. For example, modules of a computing device that
communicate via a common data bus are coupled to one another. As
another example, one or more electrodes of a glucose sensor can be
used to detect the amount of glucose in a sample and convert that
information into a signal, e.g., an electrical or electromagnetic
signal; the signal can then be transmitted to an electronic
circuit. In this case, the electrode is "operably linked" to the
electronic circuitry, even though the analog signal from the
electrode is transmitted and/or transformed by analog and/or
digital circuitry before reaching the electronic circuit. These
terms are broad enough to include wireless connectivity.
[0110] The term "physically connected" as used herein is a broad
term and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refers without
limitation to one or more components that are connected to another
component(s) through direct contact and/or a wired connection,
including connecting via one or more intermediate physically
connecting component(s). For example, a glucose sensor may be
physically connected to a sensor electronics module, and thus the
processor module located therein, either directly or via one or
more electrical connections.
[0111] The term "substantially" as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to
being largely but not necessarily wholly that which is
specified.
[0112] The term "host" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to
mammal, such as a human implanted with a device.
[0113] The term "continuous analyte sensor" as used herein is a
broad term and is to be given its ordinary and customary meaning to
a person of ordinary skill in the art (and is not to be limited to
a special or customized meaning), and furthermore refers without
limitation to a device, or portion of a device, that continuously
or continually measures a concentration of an analyte, for example,
at time intervals ranging from fractions of a second up to, for
example, 1, 2, or 5 minutes, or longer. In one exemplary
embodiment, a glucose sensor comprises a continuous analyte sensor,
such as is described in U.S. Pat. No. 7,310,544, which is
incorporated herein by reference in its entirety.
[0114] The term "continuous analyte sensing" as used herein is a
broad term and is to be given its ordinary and customary meaning to
a person of ordinary skill in the art (and is not to be limited to
a special or customized meaning), and furthermore refers without
limitation to the period in which monitoring of an analyte is
continuously or continually performed, for example, at time
intervals ranging from fractions of a second up to, for example, 1,
2, or 5 minutes, or longer. In one embodiment, a glucose sensor
performs continuous analyte sensing in order to monitor a glucose
level in a corresponding host.
[0115] The terms "reference analyte monitor," "reference analyte
meter," and "reference analyte sensor" as used herein are broad
terms and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without
limitation to a device that measures a concentration of an analyte
and can be used as a reference for a continuous analyte sensor, for
example a self-monitoring blood glucose meter (SMBG) can be used as
a reference for a continuous glucose sensor for comparison,
calibration, and the like.
[0116] The term "clinical acceptability", as used herein, is a
broad term and is to be given its ordinary and customary meaning to
a person of ordinary skill in the art (and is not to be limited to
a special or customized meaning), and refers without limitation to
determination of the risk of inaccuracies to a patient. Clinical
acceptability may consider a deviation between time corresponding
glucose measurements (e.g., data from a glucose sensor and data
from a reference glucose monitor) and the risk (e.g., to the
decision making of a diabetic patient) associated with that
deviation based on the glucose value indicated by the sensor and/or
reference data. One example of clinical acceptability may be 85% of
a given set of measured analyte values within the "A" and "B"
region of a standard Clarke Error Grid when the sensor measurements
are compared to a standard reference measurement.
[0117] The term "quality of calibration" as used herein, is a broad
term and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and refers without limitation to
the statistical association of matched data pairs in the
calibration set used to create the conversion function. For
example, an R-value may be calculated for a calibration set to
determine its statistical data association, wherein an R-value
greater than 0.79 determines a statistically acceptable calibration
quality, while an R-value less than 0.79 determines statistically
unacceptable calibration quality.
[0118] The term "sensor session" as used herein, is a broad term
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to a period
of time a sensor is in use, such as but not limited to a period of
time starting at the time the sensor is implanted (e.g., by the
host) to removal of the sensor (e.g., removal of the sensor from
the host's body and/or removal of the sensor electronics module
from the sensor housing).
[0119] The terms "noise," "noise event(s)," "noise episode(s),"
"signal artifact(s)," "signal artifact event(s)," and "signal
artifact episode(s)" as used herein are broad terms and are to be
given their ordinary and customary meaning to a person of ordinary
skill in the art (and are not to be limited to a special or
customized meaning), and furthermore refer without limitation to
signal noise that is substantially non-glucose related, such as
interfering species, macro- or micro-motion, ischemia, pH changes,
temperature changes, pressure, stress, or even unknown sources of
mechanical, electrical and/or biochemical noise for example.
[0120] The term "measured analyte values" as used herein is a broad
term and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without
limitation to an analyte value or set of analyte values for a time
period for which analyte data has been measured by an analyte
sensor. The term is broad enough to include sensor data from the
analyte sensor before or after data processing in the sensor and/or
receiver (for example, data smoothing, calibration, and the
like).
[0121] The term "estimated analyte values" as used herein is a
broad term and is to be given its ordinary and customary meaning to
a person of ordinary skill in the art (and is not to be limited to
a special or customized meaning), and furthermore refers without
limitation to an analyte value or set of analyte values, which have
been algorithmically extrapolated from measured analyte values. In
some embodiments, estimated analyte values are estimated for a time
period during which no data exists. However, estimated analyte
values can also be estimated during a time period for which
measured data exists, but is to be replaced by algorithmically
extrapolated (e.g. processed or filtered) data due to noise or a
time lag in the measured data, for example.
[0122] The term "calibration information" as used herein is a broad
term and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without
limitation to any information useful in calibration of a sensor.
Calibration information may include reference data received from a
reference analyte monitor, including one or more reference data
points, one or more matched data pairs formed by matching reference
data (e.g., one or more reference glucose data points) with
substantially time corresponding sensor data (e.g., one or more
continuous sensor data points), a calibration set formed from a set
of one or more matched data pairs, a calibration line drawn from
the calibration set, in vitro parameters (e.g., sensor
sensitivity), and/or a manufacturing code, for example.
[0123] The term "alarm" as used herein is a broad term, and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to
an alert or signal, such as an audible, visual, or tactile signal,
triggered in response to one or more alarm conditions. In one
embodiment, hyperglycemic and hypoglycemic alarms are triggered
when present or predicted clinical danger is assessed based on
continuous analyte data.
[0124] The term "transformed sensor data" as used herein is a broad
term, and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without
limitation to any data that is derived, either fully or in part,
from raw sensor data from one or more sensors. For example, raw
sensor data over a time period (e.g., 5 minutes) may be processed
in order to generated transformed sensor data including one or more
trend indicators (e.g., a 5 minute trend). Other examples of
transformed data include filtered sensor data (e.g., one or more
filtered analyte concentration values), calibrated sensor data
(e.g., one or more calibrated analyte concentration values), rate
of change information, trend information, rate of acceleration
information, sensor diagnostic information, location information,
alarm/alert information, calibration information, and/or the
like.
[0125] The term "sensor information" as used herein is a broad
term, and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without
limitation to information associated with measurement, signal
processing (including calibration), alarms, data transmission,
and/or display associated with a sensor, such as a continuous
analyte sensor. The term is broad enough to include raw sensor data
(one or more raw analyte concentration values), as well as
transformed sensor data. In some embodiments, sensor information
includes displayable sensor information.
[0126] The term "displayable sensor information" as used herein is
a broad term, and is to be given its ordinary and customary meaning
to a person of ordinary skill in the art (and is not to be limited
to a special or customized meaning), and furthermore refers without
limitation to information that is transmitted for display on one or
more display devices. As is discussed elsewhere herein, the content
of displayable sensor information that is transmitted to a
particular display device may be customized for the particular
display device. Additionally, formatting of displayable sensor
information may be customized for respective display devices.
Displayable sensor information may include any sensor data,
including raw sensor data, transformed sensor data, and/or any
information associated with measurement, signal processing
(including calibration), and/or alerts associated with one or more
sensors.
[0127] The term "data package" as used herein is a broad term, and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
combination of data that is transmitted to one or more display
devices, such as in response to triggering of an alert. A data
package may include displayable sensor information (e.g., that has
been selected and formatted for a particular display device) as
well as header information, such as data indicating a delivery
address, communication protocol, etc. Depending on the embodiment,
a data package may comprises multiple packets of data that are
separately transmitted to a display device (and reassembled at the
display device) or a single block of data that is transmitted to
the display device. Data packages may be formatted for transmission
via any suitable communication protocol, including radio frequency,
Bluetooth, universal serial bus, any of the wireless local area
network (WLAN) communication standards, including the IEEE 802.11,
802.15, 802.20, 802.22 and other 802 communication protocols,
and/or a proprietary communication protocol.
[0128] The term "direct wireless communication" as used herein is a
broad term, and is to be given its ordinary and customary meaning
to a person of ordinary skill in the art (and is not to be limited
to a special or customized meaning), and furthermore refers without
limitation to a data transmission that goes from one device to
another device without any intermediate data processing (e.g., data
manipulation). For example, direct wireless communication between a
sensor electronics module and a display device occurs when the
sensor information transmitted from the sensor electronics module
is received by the display device without intermediate processing
of the sensor information. The term is broad enough to include
wireless communication that is transmitted through a router, a
repeater, a telemetry receiver (e.g., configured to re-transmit the
sensor information without additional algorithmic processing), and
the like. The term is also broad enough to include transformation
of data format (e.g., via a Bluetooth receiver) without substantive
transformation of the sensor information itself.
[0129] The term "prospective algorithm(s)" as used herein is a
broad term, and is to be given its ordinary and customary meaning
to a person of ordinary skill in the art (and is not to be limited
to a special or customized meaning), and furthermore refers without
limitation to algorithms that process sensor information in
real-time (e.g., continuously and/or periodically as sensor data is
received from the continuous analyte sensor) and provide real-time
data output (e.g., continuously and/or periodically as sensor data
is processed in the sensor electronics module).
[0130] The term "retrospective algorithm(s)" as used herein is a
broad term, and is to be given its ordinary and customary meaning
to a person of ordinary skill in the art (and is not to be limited
to a special or customized meaning), and furthermore refers without
limitation to algorithms that process sensor information in
retrospect, (e.g., analysis of a set of data for a time period
previous to the present time period).
[0131] As employed herein, the following abbreviations apply: Eq
and Eqs (equivalents); mEq (milliequivalents); M (molar); mM
(millimolar) .mu.M (micromolar); N (Normal); mol (moles); mmol
(millimoles); .mu.mol (micromoles); nmol (nanomoles); g (grams); mg
(milligrams); .mu.g (micrograms); Kg (kilograms); L (liters); mL
(milliliters); dL (deciliters); .mu.L (microliters); cm
(centimeters); mm (millimeters); .mu.m (micrometers); nm
(nanometers); h and hr (hours); min. (minutes); s and sec.
(seconds); .degree. C. (degrees Centigrade).
Overview
[0132] In some embodiments, a system is provided for continuous
measurement of an analyte in a host that includes: a continuous
analyte sensor configured to continuously measure a concentration
of the analyte in the host and a sensor electronics module
physically connected to the continuous analyte sensor during sensor
use. In one embodiment, the sensor electronics module includes
electronics configured to process a data stream associated with an
analyte concentration measured by the continuous analyte sensor in
order to generate displayable sensor information that includes raw
sensor data, transformed sensor data, and/or any other sensor data,
for example. The sensor electronics module may further be
configured to generate displayable sensor information that is
customized for respective display devices, such that different
display devices may receive different displayable sensor
information.
Alerts
[0133] In some advantageous embodiments, one or more alerts are
associated with a sensor electronics module. For example, alerts
may be defined by one or more alert conditions that indicate when a
respective alert should be triggered. For example, a hypoglycemic
alert may include alert conditions indicating a glucose level below
a threshold. The alert conditions may also be based on transformed
or analyzed sensor data, such as trending data, pattern data,
and/or sensor data from multiple different sensors (e.g. an alert
may be based on sensor data from both a glucose sensor and a
temperature sensor). For example, a hypoglycemic alert may include
alert conditions indicating a minimum required trend in the host's
glucose level that must be present before triggering the alert. The
term "trend," as used herein refers generally to data indicating
some attribute of data that is acquired over time, e.g., such as
calibrated or filtered data from a continuous glucose sensor. A
trend may indicate amplitude, rate of change, acceleration,
direction, etc., of data, such as sensor data, including
transformed or raw sensor data.
[0134] In one embodiment, each of the alerts is associated with one
or more actions that are to be performed in response to triggering
of the alert. Alert actions may include, for example, activating an
alarm, such as displaying information on a display of the sensor
electronics module or activating an audible or vibratory alarm
coupled to the sensor electronics module, and/or transmitting data
to one or more display devices external to the sensor electronics
module. For any delivery action that is associated with a triggered
alert, one or more delivery options define the content and/or
format of the data to be transmitted, the device to which the data
is to be transmitted, when the data is to be transmitted, and/or a
communication protocol for delivery of the data.
[0135] In one embodiment, multiple delivery actions (each having
respective delivery options) may be associated with a single alert
such that displayable sensor information having different content
and formatting, for example, is transmitted to respective display
devices in response to triggering of a single alert. For example, a
mobile telephone may receive a data package including minimal
displayable sensor information (that may be formatted specifically
for display on the mobile telephone), while a desktop computer may
receive a data package including most (or all) of the displayable
sensor information that is generated by the sensor electronics
module in response to triggering of a common alert. Advantageously,
the sensor electronics module is not tied to a single display
device, rather it is configured to communicate with a plurality of
different display devices directly, systematically, simultaneously
(e.g., via broadcasting), regularly, periodically, randomly,
on-demand, in response to a query, based on alerts or alarms,
and/or the like.
[0136] In some embodiments, clinical risk alerts are provided that
include alert conditions that combine intelligent and dynamic
estimative algorithms that estimate present or predicted danger
with greater accuracy, more timeliness in pending danger, avoidance
of false alarms, and less annoyance for the patient. In general,
clinical risk alerts include dynamic and intelligent estimative
algorithms based on analyte value, rate of change, acceleration,
clinical risk, statistical probabilities, known physiological
constraints, and/or individual physiological patterns, thereby
providing more appropriate, clinically safe, and patient-friendly
alarms. Co-pending U.S. Publ. No. 2007-0208246-A1, which is
incorporated herein by reference in its entirety, describes some
systems and methods associated with the clinical risk alerts (or
alarms) described herein. In some embodiments, clinical risk alerts
can be triggered for a predetermined time period to allow for the
user to attend to his/her condition. Additionally, the clinical
risk alerts can be de-activated when leaving a clinical risk zone
so as not to annoy the patient by repeated clinical alarms (e.g.,
visual, audible or vibratory), when the patient's condition is
improving. In some embodiments, dynamic and intelligent estimation
determines a possibility of the patient avoiding clinical risk,
based on the analyte concentration, the rate of change, and other
aspects of the dynamic and intelligent estimative algorithms. If
there is minimal or no possibility of avoiding the clinical risk, a
clinical risk alert will be triggered. However, if there is a
possibility of avoiding the clinical risk, the system is configured
to wait a predetermined amount of time and re-analyze the
possibility of avoiding the clinical risk. In some embodiments,
when there is a possibility of avoiding the clinical risk, the
system is further configured to provide targets, therapy
recommendations, or other information that can aid the patient in
proactively avoiding the clinical risk.
[0137] In some embodiments, the sensor electronics module is
configured to search for one or more display devices within
communication range of the sensor electronics module and to
wirelessly communicate sensor information (e.g., a data package
including displayable sensor information, one or more alarm
conditions, and/or other alarm information) thereto. Accordingly,
the display device is configured to display at least some of the
sensor information and/or alarm the host (and/or care taker),
wherein the alarm mechanism is located on the display device.
[0138] In some embodiments, the sensor electronics module is
configured to provide one or a plurality of different alarms via
the sensor electronics module and/or via transmission of a data
packaging indicating an alarm should be initiated by one or a
plurality of display devices (e.g., sequentially and/or
simultaneously). In some embodiments, the sensor electronics module
determines which of the one or more alarms to trigger based on one
or more alerts that are triggered. For example, when an alert
triggers that indicates severe hypoglycemia, the sensor electronics
module can perform multiple actions, such as activating an alarm on
the sensor electronics module, transmitting a data package to a
small (key fob) indicating activation of an alarm on the display,
and transmitting a data package as a text message to a care
provider. As an example, a text message can appear on a small (key
fob) display, cell phone, pager device, and/or the like, including
displayable sensor information that indicates the host's condition
(e.g., "severe hypoglycemia").
[0139] In some embodiments, the sensor electronics module is
configured to wait a time period for the host to respond to a
triggered alert (e.g., by pressing or selecting a snooze and/or off
function and/or button on the sensor electronics module and/or a
display device), after which additional alerts are triggered (e.g.,
in an escalating manner) until one or more alerts are responded to.
In some embodiments, the sensor electronics module is configured to
send control signals (e.g., a stop signal) to a medical device
associated with an alarm condition (e.g., hypoglycemia), such as an
insulin pump, wherein the stop alert triggers a stop of insulin
delivery via the pump.
[0140] In some embodiments, the sensor electronics module is
configured to directly, systematically, simultaneously (e.g., via
broadcasting), regularly, periodically, randomly, on-demand, in
response to a query (from the display device), based on alerts or
alarms, and/or the like transmit alarm information. In some
embodiments, the system further includes a repeater such that the
wireless communication distance of the sensor electronics module
can be increased, for example, to 10, 20, 30, 50 75, 100, 150, or
200 meters or more, wherein the repeater is configured to repeat a
wireless communication from the sensor electronics module to the
display device located remotely from the sensor electronics module.
A repeater can be useful to families having children with diabetes.
For example, to allow a parent to carry, or place in a stationary
position, a display device, such as in a large house wherein the
parents sleep at a distance from the child.
Display Devices
[0141] In some embodiments, the sensor electronics module is
configured to search for and/or attempt wireless communication with
a display device from a list of display devices. In some
embodiments, the sensor electronics module is configured to search
for and/or attempt wireless communication with a list of display
devices in a predetermined and/or programmable order (e.g., grading
and/or escalating), for example, wherein a failed attempt at
communication with and/or alarming with a first display device
triggers an attempt at communication with and/or alarming with a
second display device, and so on. In one exemplary embodiment, the
sensor electronics module is configured to search for and attempt
to alarm a host or care provider sequentially using a list of
display devices, such as: 1) a default display device, 2) a key fob
device, 3) a cell phone (via auditory and/or visual methods, such
as, text message to the host and/or care provider, voice message to
the host and/or care provider, and/or 911).
[0142] Depending on the embodiment, one or more display devices
that receive data packages from the sensor electronics module are
"dummy displays", wherein they display the displayable sensor
information received from the sensor electronics module without
additional processing (e.g., prospective algorithmic processing
necessary for real-time display of sensor information). In some
embodiments, the displayable sensor information comprises
transformed sensor data that does not require processing by the
display device prior to display of the displayable sensor
information. Some display devices may comprise software including
display instructions (software programming comprising instructions
configured to display the displayable sensor information and
optionally query the sensor electronics module to obtain the
displayable sensor information) configured to enable display of the
displayable sensor information thereon. In some embodiments, the
display device is programmed with the display instructions at the
manufacturer and can include security and/or authentication to
avoid plagiarism of the display device. In some embodiments, a
display device is configured to display the displayable sensor
information via a downloadable program (for example, a downloadable
Java Script via the internet), such that any display device that
supports downloading of a program (for example, any display device
that supports Java applets) therefore can be configured to display
displayable sensor information (e.g., mobile phones, PDAs, PCs and
the like).
[0143] In some embodiments, certain display devices may be in
direct wireless communication with the sensor electronics module,
however intermediate network hardware, firmware, and/or software
can be included within the direct wireless communication. In some
embodiments, a repeater (e.g., a Bluetooth repeater) can be used to
re-transmit the transmitted displayable sensor information to a
location farther away than the immediate range of the telemetry
module of the sensor electronics module, wherein the repeater
enables direct wireless communication when substantive processing
of the displayable sensor information does not occur. In some
embodiments, a receiver (e.g., Bluetooth receiver) can be used to
re-transmit the transmitted displayable sensor information,
possibly in a different format, such as in a text message onto a TV
screen, wherein the receiver enables direct wireless communication
when substantive processing of the sensor information does not
occur. In one embodiment, the sensor electronics module directly
wirelessly transmits displayable sensor information to one or a
plurality of display devices, such that the displayable sensor
information transmitted from the sensor electronics module is
received by the display device without intermediate processing of
the displayable sensor information.
[0144] In some embodiments, one or more sensors are configured to
process data through communication with a "cloud" based processing
system. For example, the applications for processing the sensor
data may reside in one or more servers in communication with the
sensor. The applications can be queried by the sensor for
processing the data and determining trend or pattern
information.
[0145] In one embodiment, one or more display devices comprise
built-in authentication mechanisms, wherein authentication is
required for communication between the sensor electronics module
and the display device. In some embodiments, to authenticate the
data communication between the sensor electronics module and
display devices, a challenge-response protocol, such as a password
authentication is provided, where the challenge is a request for
the password and the valid response is the correct password, such
that pairing of the sensor electronics module with the display
devices can be accomplished by the user and/or manufacturer via the
password. However, any known authentication system or method useful
for telemetry devices can be used with the preferred
embodiments.
[0146] In some embodiments, one or more display devices are
configured to query the sensor electronics module for displayable
sensor information, wherein the display device acts as a master
device requesting sensor information from the sensor electronics
module (e.g., a slave device) on-demand, for example, in response
to a query. In some embodiments, the sensor electronics module is
configured for periodic, systematic, regular, and/or periodic
transmission of sensor information to one or more display devices
(for example, every 1, 2, 5, or 10 minutes or more). In some
embodiments, the sensor electronics module is configured to
transmit data packages associated with a triggered alert (e.g.,
triggered by one or more alert conditions). However, any
combination of the above described statuses of data transmission
can be implemented with any combination of paired sensor
electronics module and display device(s). For example, one or more
display devices can be configured for querying the sensor
electronics module database and for receiving alarm information
triggered by one or more alarm conditions being met. Additionally,
the sensor electronics module can be configured for periodic
transmission of sensor information to one or more display devices
(the same or different display devices as described in the previous
example), whereby a system can include display devices that
function differently with regard to how they obtain sensor
information.
[0147] In some embodiments, as described in more detail elsewhere
herein, a display device is configured to query the data storage
memory in the sensor electronics module for certain types of data
content, including direct queries into a database in the sensor
electronics module's memory and/or requests for configured or
configurable packages of data content therefrom; namely, the data
stored in the sensor electronics module is configurable, queryable,
predetermined, and/or pre-packaged, based on the display device
with which the sensor electronics module is communicating. In some
additional or alternative embodiments, the sensor electronics
module generates the displayable sensor information based on its
knowledge of which display device is to receive a particular
transmission. Additionally, some display devices are capable of
obtaining calibration information and wirelessly transmitting the
calibration information to the sensor electronics module, such as
through manual entry of the calibration information, automatic
delivery of the calibration information, and/or an integral
reference analyte monitor incorporated into the display device.
U.S. Pat. No. 7,774,145, U.S. Publ. No. 2007-0203966-A1, U.S. Publ.
No. 2007-0208245-A1, and U.S. Pat. No. 7,519,408, each of which is
incorporated herein by reference in its entirety, describe systems
and methods for providing an integral reference analyte monitor
incorporated into a display device and/or other calibration methods
that can be implemented with the preferred embodiments.
[0148] In general, a plurality of display devices (e.g., a small
(key fob) display device, a larger (hand-held) display device, a
mobile phone, a reference analyte monitor, a drug delivery device,
a medical device and a personal computer) are configured to
wirelessly communicate with the sensor electronics module, wherein
the one or more display devices are configured to display at least
some of the displayable sensor information wirelessly communicated
from the sensor electronics module, wherein displayable sensor
information includes sensor data, such as raw data and/or
transformed sensor data, such as analyte concentration values, rate
of change information, trend information, alert information, sensor
diagnostic information and/or calibration information, for
example.
Small (Key Fob) Display Device
[0149] In some embodiments, one the plurality of display devices is
a small (e.g., key fob) display device 14 (FIG. 1) that is
configured to display at least some of the sensor information, such
as an analyte concentration value and a trend arrow. In general, a
key fob device is a small hardware device with a built-in
authentication mechanism sized to fit on a key chain. However, any
small display device 14 can be configured with the functionality as
described herein with reference to the key fob device 14, including
a wrist band, a hang tag, a belt, a necklace, a pendent, a piece of
jewelry, an adhesive patch, a pager, an identification (ID) card,
and the like, all of which are included by the phrase "small
display device" and/or "key fob device" herein.
[0150] In general, the key fob device 14 includes electronics
configured to receive and display displayable sensor information
(and optionally configured to query the sensor electronics module
for the displayable sensor information). In one embodiment, the
electronics include a RAM and a program storage memory configured
at least to display the sensor data received from the sensor
electronics module. In some embodiments, the key fob device 14
includes an alarm configured to warn a host of a triggered alert
(e.g., audio, visual and/or vibratory). In some embodiments, the
key fob device 14 includes a user interface, such as an LCD 602 and
one or more buttons 604 that allows a user to view data, such as a
numeric value and/or an arrow, to toggle through one or more
screens, to select or define one or more user parameters, to
respond to (e.g., silence, snooze, turn off) an alert, and/or the
like.
[0151] In some embodiments, the key fob display device has a memory
(e.g., such as in a gig stick or thumb drive) that stores sensor,
drug (e.g., insulin) and other medical information, enabling a
memory stick-type function that allows data transfer from the
sensor electronics module to another device (e.g., a PC) and/or as
a data back-up location for the sensor electronics module memory
(e.g., data storage memory). In some embodiments, the key fob
display device is configured to be automatically readable by a
network system upon entry into a hospital or other medical
complex.
[0152] In some embodiments, the key fob display device includes a
physical connector, such as USB port 606, to enable connection to a
port (e.g., USB) on a computer, enabling the key fob to function as
a data download device (e.g., from the sensor electronics module to
a PC), a telemetry connector (e.g., Bluetooth adapter/connector for
a PC), and/or enables configurable settings on the key fob device
(e.g., via software on the PC that allows configurable parameters
such as numbers, arrows, trend, alarms, font, etc.) In some
embodiments, user parameters associated with the small (key fob)
display device can be programmed into (and/or modified) by a
display device such as a personal computer, personal digital
assistant, or the like. In one embodiment, user parameters include
contact information, alert/alarms settings (e.g., thresholds,
sounds, volume, and/or the like), calibration information, font
size, display preferences, defaults (e.g., screens), and/or the
like. Alternatively, the small (key fob) display device can be
configured for direct programming of user parameters. In some
embodiments, wherein the small (key fob) display device comprises a
telemetry module, such as Bluetooth, and a USB connector (or the
like), such that the small (key fob) display device additionally
functions as telemetry adapter (e.g., Bluetooth adapter) enabling
direct wireless communication between the sensor electronics module
and the PC, for example, wherein the PC does not include the
appropriate telemetry adapter therein.
Large (Hand-held) Display Device
[0153] In some embodiments, one the plurality of display devices is
a hand-held display device 16 (FIG. 1) configured to display sensor
information including an analyte concentration and a graphical
representation of the analyte concentration over time. In general,
the hand-held display device comprises a display 608 sufficiently
large to display a graphical representation 612 of the sensor data
over a time period, such as a previous 1, 3, 5, 6, 9, 12, 18, or
24-hours of sensor data. In some embodiments, the hand-held device
16 is configured to display a trend graph or other graphical
representation, a numeric value, an arrow, and/or to alarm the
host. U.S. Publ. No. 2005-0203360-A1, which is incorporated herein
by reference in its entirety, describes and illustrates some
examples of display of data on a hand-held display device. Although
FIG. 1 illustrates one embodiment of a hand-held display device,
the hand-held device can be any single application device or
multi-application device, such as mobile phone, a palm-top
computer, a PDA, portable media player (e.g., iPod, MP3 player), a
blood glucose meter, an insulin pump, and/or the like.
[0154] In some embodiments, a mobile phone (or PDA) is configured
to display (as described above) and/or relay sensor information,
such as via a voice or text message to the host and/or the host's
care provider. In some embodiments, the mobile phone further
comprises an alarm configured to warn a host of a triggered alert,
such as in response to receiving a data package indicating
triggering of the alert. Depending on the embodiment, the data
package may include displayable sensor information, such as an
on-screen message, text message, and/or pre-generated graphical
representation of sensor data and/or transformed sensor data, as
well as an indication of an alarm, such as an auditory alarm or a
vibratory alarm, that should be activated by the mobile phone.
[0155] In some embodiments, one of the display devices is a drug
delivery device, such as an insulin pump and/or insulin pen,
configured to display sensor information. In some embodiments, the
sensor electronics module is configured to wirelessly communicate
sensor diagnostic information to the drug delivery device in order
to enable to the drug delivery device to consider (include in its
calculations/algorithms) a quality, reliability and/or accuracy of
sensor information for closed loop and/or semi-closed loop systems,
which are described in more detail in U.S. Pat. No. 7,591,801,
which is incorporated herein by reference in its entirety. In some
alternative embodiments, the sensor electronic module is configured
to wirelessly communicate with a drug delivery device that does not
include a display, for example, in order to enable a closed loop
and/or semi-closed loop system as described above.
[0156] In some embodiments, one of the display devices is a drug
delivery device is a reference analyte monitor, such as a blood
glucose meter, configured to measure a reference analyte value
associated with an analyte concentration in a biological sample
from the host.
Personal Computer Display Device
[0157] In some embodiments, one of the display devices is personal
computer (PC) 20 (FIG. 1) configured to display sensor information.
Preferably, the PC 20 has software installed, wherein the software
enables display and/or performs data analysis (retrospective
processing) of the historic sensor information. In some
embodiments, a hardware device can be provided (not shown), wherein
the hardware device (e.g., dongle/adapter) is configured to plug
into a port on the PC to enable wireless communication between the
sensor electronics module and the PC. In some embodiments, the PC
20 is configured to set and/or modify configurable parameters of
the sensor electronics module 12 and/or small (key fob device) 14,
as described in more detail elsewhere herein.
Other Display Devices
[0158] In some embodiments, one of the display devices is an
on-skin display device that is splittable from, releasably attached
to, and/or dockable to the sensor housing (mounting unit, sensor
pod, or the like). In some embodiments, release of the on-skin
display turns the sensor off; in other embodiments, the sensor
housing comprises sufficient sensor electronics to maintain sensor
operation even when the on-skin display is released from the sensor
housing.
[0159] In some embodiments, one of the display devices is a
secondary device, such as a heart rate monitor, a pedometer, a
temperature sensor, a car initialization device (e.g., configured
to allow or disallow the car to start and/or drive in response to
at least some of the sensor information wirelessly communicated
from the sensor electronics module (e.g., glucose value above a
predetermined threshold)). In some alternative embodiments, one of
the display devices is designed for an alternative function device
(e.g., a caller id device), wherein the system is configured to
communicate with and/or translate displayable sensor information to
a custom protocol of the alternative device such that displayable
sensor information can be displayed on the alternative function
device (display of caller id device).
Exemplary Configurations
[0160] FIG. 1 is a diagram illustrating one embodiment of a
continuous analyte sensor system 8 including a sensor electronics
module 12. In the embodiment of FIG. 1, the system includes a
continuous analyte sensor 10 physically connected to a sensor
electronics module 12, which is in direct wireless communication
with a plurality of different display devices 14, 16, 18, and/or
20.
[0161] In one embodiment, the sensor electronics module 12 includes
electronic circuitry associated with measuring and processing the
continuous analyte sensor data, including prospective algorithms
associated with processing and calibration of the sensor data. The
sensor electronics module 12 may be physically connected to the
continuous analyte sensor 10 and can be integral with
(non-releasably attached to) or releasably attachable to the
continuous analyte sensor 10. The sensor electronics module 12 may
include hardware, firmware, and/or software that enables
measurement of levels of the analyte via a glucose sensor, such as
an analyte sensor. For example, the sensor electronics module 12
can include a potentiostat, a power source for providing power to
the sensor, other components useful for signal processing and data
storage, and a telemetry module for transmitting data from the
sensor electronics module to one or more display devices.
Electronics can be affixed to a printed circuit board (PCB), or the
like, and can take a variety of forms. For example, the electronics
can take the form of an integrated circuit (IC), such as an
Application-Specific Integrated Circuit (ASIC), a microcontroller,
and/or a processor. The sensor electronics module 12 includes
sensor electronics that are configured to process sensor
information, such as sensor data, and generate transformed sensor
data and displayable sensor information. Examples of systems and
methods for processing sensor analyte data are described in more
detail herein and in U.S. Pat. No. 7,310,544, U.S. Pat. No.
6,931,327, U.S. Pat. No. 8,010,174, U.S. Pat. No. 8,233,959, U.S.
Publ. No. 2007-0032706-A1, U.S. Publ. No. 2008-0033254-A1, U.S.
Publ. No. 2005-0203360-A1, U.S. Publ. No. 2005-0154271-A1, U.S.
Publ. No. 2005-0192557-A1, U.S. Publ. No. 2006-0222566-A1, U.S.
Publ. No. 2007-0203966-A1, and U.S. Publ. No. 2007-0208245, each of
which is incorporated herein by reference in their entirety.
[0162] Referring again to FIG. 1, a plurality of display devices
(14, 16, 18, and/or 20) are configured for displaying (and/or
alarming) the displayable sensor information that has been
transmitted by the sensor electronics module 12 (e.g., in a
customized data package that is transmitted to the display devices
based on their respective preferences). For example, the display
devices are configured to display the displayable sensor
information as it is communicated from the sensor electronics
module (e.g., in a data package that is transmitted to respective
display devices), without any additional prospective processing
required for calibration and real-time display of the sensor
data.
[0163] In the embodiment of FIG. 1, the plurality of display
devices includes a small (key fob) display device 14, such as a
wrist watch, a belt, a necklace, a pendent, a piece of jewelry, an
adhesive patch, a pager, a key fob, a plastic card (e.g., credit
card), an identification (ID) card, and/or the like, wherein the
small display device comprises a relatively small display (e.g.,
smaller than the large display device) and is configured to display
certain types of displayable sensor information (e.g., a numerical
value and an arrow, in some embodiments). In some embodiments, one
of the plurality of display devices is a large (hand-held) display
device 16, such as a hand-held receiver device, a palm-top computer
and/or the like, wherein the large display device comprises a
relatively larger display (e.g., larger than the small display
device) and is configured to display a graphical representation of
the continuous sensor data (e.g., including current and historic
data). Other display devices can include other hand-held devices,
such as a cell phone or PDA 18, an insulin delivery device, a blood
glucose meter, and/or a desktop or laptop computer 20.
[0164] Because different display devices provide different user
interfaces, content of the data packages (e.g., amount, format,
and/or type of data to be displayed, alarms, and the like) can be
customized (e.g., programmed differently by the manufacture and/or
by an end user) for each particular display device. Accordingly, in
the embodiment of FIG. 1, a plurality of different display devices
are in direct wireless communication with the sensor electronics
module (e.g., such as an on-skin sensor electronics module 12 that
is physically connected to the continuous analyte sensor 10) during
a sensor session to enable a plurality of different types and/or
levels of display and/or functionality associated with the
displayable sensor information, which is described in more detail
elsewhere herein.
Continuous Sensor
[0165] In some embodiments, a glucose sensor comprises a continuous
sensor, for example a subcutaneous, transdermal (e.g.,
transcutaneous), or intravascular device. In some embodiments, the
device can analyze a plurality of intermittent blood samples. The
glucose sensor can use any method of glucose-measurement, including
enzymatic, chemical, physical, electrochemical, spectrophotometric,
polarimetric, calorimetric, iontophoretic, radiometric,
immunochemical, and the like.
[0166] A glucose sensor can use any known method, including
invasive, minimally invasive, and non-invasive sensing techniques
(e.g., fluorescent monitoring), to provide a data stream indicative
of the concentration of glucose in a host. The data stream is
typically a raw data signal, which is converted into a calibrated
and/or filtered data stream that is used to provide a useful value
of glucose to a user, such as a patient or a caretaker (e.g., a
parent, a relative, a guardian, a teacher, a doctor, a nurse, or
any other individual that has an interest in the wellbeing of the
host).
[0167] A glucose sensor can be any device capable of measuring the
concentration of glucose. One exemplary embodiment is described
below, which utilizes an implantable glucose sensor. However, it
should be understood that the devices and methods described herein
can be applied to any device capable of detecting a concentration
of glucose and providing an output signal that represents the
concentration of glucose.
[0168] In one embodiment, the analyte sensor is an implantable
glucose sensor, such as described with reference to U.S. Pat. No.
6,001,067 and U.S. Publ. No. 2005-0027463-A1. In another
embodiment, the analyte sensor is a transcutaneous glucose sensor,
such as described with reference to U.S. Publ. No. 2006-0020187-A1.
In some alternative embodiments, an optical, non-invasive,
"continuous or quasi-continuous" glucose measurement device such as
described by U.S. Pat. No. 6,049,727, which is incorporated by
reference herein in its entirety, can be implanted in the body for
optically measuring analyte levels.
[0169] In still other embodiments, the sensor is configured to be
implanted in a host vessel or extracorporeally, such as is
described in U.S. Publ. No. 2007-0027385-A1, U.S. Publ. No.
2008-0119703-A1, U.S. Publ. No. 2008-0108942-A1, and U.S. Publ. No.
2007-0197890-A1, the contents of each of which is hereby
incorporated by reference in its entirety. In one alternative
embodiment, the continuous glucose sensor comprises a
transcutaneous sensor such as described in U.S. Pat. No. 6,565,509
to Say et al., for example. In another alternative embodiment, the
continuous glucose sensor comprises a subcutaneous sensor such as
described with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et
al. or U.S. Pat. No. 6,484,046 to Say et al., for example. In
another alternative embodiment, the continuous glucose sensor
comprises a refillable subcutaneous sensor such as described with
reference to U.S. Pat. No. 6,512,939 to Colvin et al., for example.
In another alternative embodiment, the continuous glucose sensor
comprises an intravascular sensor such as described with reference
to U.S. Pat. No. 6,477,395 to Schulman et al., for example. In
another alternative embodiment, the continuous glucose sensor
comprises an intravascular sensor such as described with reference
to U.S. Pat. No. 6,424,847 to Mastrototaro et al., for example.
[0170] FIGS. 2A and 2B are perspective and side views of a sensor
system including a mounting unit 214 and sensor electronics module
12 attached thereto in one embodiment, shown in its functional
position, including a mounting unit and a sensor electronics module
matingly engaged therein. In some embodiments, the mounting unit
214, also referred to as a housing or sensor pod, comprises a base
234 adapted for fastening to a host's skin. The base can be formed
from a variety of hard or soft materials, and can comprises a low
profile for minimizing protrusion of the device from the host
during use. In some embodiments, the base 234 is formed at least
partially from a flexible material, which is believed to provide
numerous advantages over conventional transcutaneous sensors,
which, unfortunately, can suffer from motion-related artifacts
associated with the host's movement when the host is using the
device. The mounting unit 214 and/or sensor electronics module 12
can be located over the sensor insertion site to protect the site
and/or provide a minimal footprint (utilization of surface area of
the host's skin).
[0171] In some embodiments, a detachable connection between the
mounting unit 214 and sensor electronics module 12 is provided,
which enables improved manufacturability, namely, the relatively
inexpensive mounting unit 214 can be disposed of when replacing the
sensor system after its usable life, while the relatively more
expensive sensor electronics module 12 can be reusable with
multiple sensor systems. In some embodiments, the sensor
electronics module 12 is configured with signal processing
(programming), for example, configured to filter, calibrate and/or
other algorithms useful for calibration and/or display of sensor
information. However, an integral (non-detachable) sensor
electronics module can be configured.
[0172] In some embodiments, the contacts 238 are mounted on or in a
subassembly hereinafter referred to as a contact subassembly 236
configured to fit within the base 234 of the mounting unit 214 and
a hinge 248 that allows the contact subassembly 236 to pivot
between a first position (for insertion) and a second position (for
use) relative to the mounting unit 214. The term "hinge" as used
herein is a broad term and is used in its ordinary sense,
including, without limitation, to refer to any of a variety of
pivoting, articulating, and/or hinging mechanisms, such as an
adhesive hinge, a sliding joint, and the like; the term hinge does
not necessarily imply a fulcrum or fixed point about which the
articulation occurs. In some embodiments, the contacts 238 are
formed from a conductive elastomeric material, such as a carbon
black elastomer, through which the sensor 10 extends.
[0173] In certain embodiments, the mounting unit 214 is provided
with an adhesive pad 208, disposed on the mounting unit's back
surface and includes a releasable backing layer. Thus, removing the
backing layer and pressing the base portion 234 of the mounting
unit onto the host's skin adheres the mounting unit 214 to the
host's skin. Additionally or alternatively, an adhesive pad can be
placed over some or all of the sensor system after sensor insertion
is complete to ensure adhesion, and optionally to ensure an
airtight seal or watertight seal around the wound exit-site (or
sensor insertion site) (not shown). Appropriate adhesive pads can
be chosen and designed to stretch, elongate, conform to, and/or
aerate the region (e.g., host's skin). The embodiments described
with reference to FIGS. 2A and 2B are described in more detail with
reference to U.S. Pat. No. 7,310,544, which is incorporated herein
by reference in its entirety. Configurations and arrangements can
provide water resistant, waterproof, and/or hermetically sealed
properties associated with the mounting unit/sensor electronics
module embodiments described herein.
[0174] Various methods and devices that are suitable for use in
conjunction with aspects of some embodiments are disclosed in U.S.
Publ. No. 2009-0240120-A1, which is incorporated herein by
reference in its entirety.
Use of Standardized Data Communication Protocols
[0175] FIG. 3 is an exemplary block diagram illustrating various
elements of one embodiment of a continuous analyte sensor system 8
and display device 14, 16, 18, 20. The sensor system 8 may include
a sensor 312 (also designated 10 in FIG. 1) coupled to a processor
314 (part of item 12 in FIG. 1) for processing and managing sensor
data. The processor may be further coupled to a transceiver 316
(part of item 12 in FIG. 1) for sending sensor data and receiving
requests and commands from an external device, such as the display
device 14, 16, 18, 20, which is used to display or otherwise
provide the sensor data to a user. The sensor system 8 may further
include a memory 318 (part of item 12 in FIG. 1) and a real time
clock 320 (part of item 12 in FIG. 1) for storing and tracking
sensor data. Communication protocols and associated modulation
schemes such as Bluetooth, Zigbee.TM., or ANT.TM. for example may
be used to transmit and receive data between the sensor system 8
and the display device 14, 16, 18, 20.
[0176] The display device 14, 16, 18, 20 may be used for alerting
and providing sensor information to a user, and may include a
processor 330 for processing and managing sensor data. The display
device 14, 16, 18, 20 may include a display 332, a memory 334, and
a real time clock 336 for displaying, storing and tracking sensor
data respectively. The display device 14, 16, 18, 20 may further
include a transceiver 338 for receiving sensor data and for sending
requests, instructions, and data to the sensor system 8. The
transceiver 338 may further employ the communication protocols
described above including, but not limited to, radio frequency,
Bluetooth, BTLE, Zigbee.TM., ANT.TM., etc.
[0177] In some embodiments, when a standardized communication
protocol is used such as Bluetooth or ANT, commercially available
transceiver circuits may be utilized that incorporate processing
circuitry to handle low level data communication functions such as
the management of data encoding, transmission frequencies,
handshake protocols, and the like. In these embodiments, the
processor 314, 330 does not need to manage these activities, but
rather provides desired data values for transmission, and manages
high level functions such as power up or down, set a rate at which
messages are transmitted, and the like. Instructions and data
values for performing these high level functions can be provided to
the transceiver circuits via a data bus and transfer protocol
established by the manufacturer of the transceiver circuit.
[0178] The analyte sensor system 8 gathers analyte data that it
periodically sends to the display device 14, 16, 18, 20. Rather
than having the transmission and receiving circuitry continuously
communicating, the analyte sensor system 8 and display device 14,
16, 18, 20 periodically establish a communication channel between
them. Thus, sensor system 8 can communicate via wireless
transmission (e.g., ANT+, low power Bluetooth, etc.) with display
device 14, 16, 18, 20 (e.g., a hand-held computing device) at
predetermined time intervals. In some embodiments, the duration of
the predetermined time interval can be selected to be long enough
so that the sensor system 8 does not consume too much power by
transmitting data more frequently than needed, yet frequent enough
to provide substantially real-time sensor information (e.g.,
measured glucose values) to the display device 14, 16, 18, 20 for
output (e.g., display) to a user. The predetermined time interval
may be every five minutes, for example. It will be appreciated that
this schedule can be varied to be any desired time interval between
data transfer activity. Those times when the communication channel
is established and sensor data is being transmitted may be referred
to as sensor packet transmission sessions.
[0179] In between these data transfer procedures, the transceiver
316 of the analyte sensor system 8 can be powered down or in a
sleep mode to conserve battery life. To establish a communication
channel, the analyte sensor system 8 may send one or more message
beacons every five minutes. Each message beacon may be considered
an invitation for a display device 14, 16, 18, 20 to establish a
communication channel with the sensor system 8. During initial
system set up, the display device 14, 16, 18, 20 may listen
continuously until such a message beacon is received. When the
beacon is successfully received, the display device 14, 16, 18, 20
can acknowledge the reception to establish communication between
the devices. When the desired data communication is complete, the
channel can be broken, and the transceiver 316 of the analyte
sensing system 8 (and possibly the transceiver 338 of the display
device 14, 16, 18, 20 as well) can be powered down. After a five
minute period, the transceivers 316, 338 can be powered up again
substantially simultaneously, and establish a new communication
channel using the same process to exchange any new data. This
process may continue, with new communication channels being
established at the pre-determined intervals. To allow for some loss
of synchronization between the two devices in between
transmissions, the analyte sensor system 8 may be configured to
send a series of message beacons in a window of time around the
scheduled transmission time (e.g., 8 message beacons per second for
4 seconds). Any one of the message beacons can be used to initiate
the establishment of a new communication channel when it is
received by the display device 14, 16, 18, 20.
Pattern Recognition
[0180] The process of detecting patterns based on raw sensor data
according to some embodiments will be described with reference to
FIG. 4. The term "pattern" as applied to glucose data measurements
and used herein refers generally to repeated relationships between
glucose data and some additional variable. The additional variable
is often times an occurrence of an action or condition, but can be
a non-occurrence of an action or condition, where an action can be
eating or exercising for example, and a condition can be the value
of a physiological parameter such as body temperature, or the like.
A "pattern" in glucose data exists when similar glucose values tend
to be associated with similar times, events, or conditions. In some
embodiments described herein, patterns are detected and the user is
made aware of their existence in a user friendly way that has not
been heretofore available. A pattern may indicate a recurring event
based on factors such as time of day, overcorrection of events by a
user, and/or user activity, as will be discussed in greater detail
below.
[0181] FIG. 4 illustrates a flowchart of a method 400 of detecting
patterns according to some embodiments. The method 400 includes
receiving data from inputs as represented by block 401. The inputs
provided may include, for example, data from a continuous glucose
monitor (CGM data). The CGM data may be both real-time and
historical data over a period of time. For example, the data may
include historical data from a period covering a four week interval
of monitored analyte detection levels. Additionally, or
alternatively, the inputs can also include a variety of other
inputs relevant to monitored analyte, such as food intake (e.g.
time, amount of carbohydrates, other food related information),
exercise, time of day, awake/sleep timer intervals, medications
ingested, etc. Inputs may also be entered by a user or can be
received from external devices (e.g., mobile phone, personal
computer, dedicated CGM receiver, etc.) or derived from analysis of
sensor data.
[0182] The method 400 may proceed by analyzing the input data for
patterns as represented by block 402. For example, patterns can be
recognized based on predefined criteria or a set of rules defined
in a data analysis application. Additionally, the predefined
criteria or rules may be variable and adjustable based on user
input. For example, some types of patterns and criteria defining
patterns can be selected, turned off and on, and/or modified by a
user, a user's physician, a user's guardian, etc.
[0183] Some examples of the types of relationships that can be
considered a pattern include hypoglycemic events by time of day.
Generally, these patterns may be identified in situations where the
user tends to have low glucose concentrations around the same time
in the day. Another type of pattern which may be identified is a
"rebound high" situation. For example, a rebound high may be
defined as a situation where a user overcorrects a hypoglycemic
event by overly increasing glucose intake, thereby going into a
hyperglycemic event or near hyperglycemic event. These events may
be detected based on a predefined set of criteria or rules, as will
be discussed in greater detail below with reference to FIGS. 5A-5E,
and identified as patterns.
[0184] Patterns that may be detected include, but are not limited
to, a hyperglycemic pattern, hypoglycemic pattern, patterns
associated with a time of day or week, a weighted scoring for
different patterns based on frequency, sequence, and severity.
Patterns may also be based on a custom sensitivity of a user, a
transition from a hypoglycemic to hyperglycemic pattern, an amount
of time spent in a severe event, and a combination of glucose
change and time information. Detected patterns may also be patterns
of high variability of glucose data. Further, a pattern may be
based on a combination of previous pattern data and a currently
detected situation, whereby the combined information generates a
predictive alert.
[0185] The method 400 may proceed by outputting information based
on the determined patterns as represented by block 403. For
example, the output can include a real-time analysis of the inputs
and determined patterns or may be a retrospective analysis of the
data. The output may be provided by a device which is external to
the sensor device, such as display device 14, 16, 18 or 20
discussed, for example, with respect to FIG. 1 above. The output
may include a detailed report of the determined pattern information
and possible corrective actions for the user. The output may also
include alerts in the form of text message, audible alert, or the
like.
Pattern Detection
[0186] As discussed above, pattern detection may be based on a
number of factors. In the following discussion, pattern detection
will be discussed with reference to detection of patterns based on
time of day and rebound highs. However, one of ordinary skill in
the art will recognize that pattern detection is not limited to
these two types of patterns. Rather, these patterns serve as
examples for the following description of some aspects of the
disclosed embodiments.
[0187] For purposes of the following discussion, a "hypoglycemic
episode" is a period of time with at least some relatively low
glucose concentration measurements. A "hypoglycemic event" is a
hypoglycemic episode that is determined to have clinical
significance based on one or more characteristics of the episode
such as duration or lowest measured values. A "hypoglycemic
pattern" is a set of hypoglycemic events that have a relationship
to one another, such as tending to be related in a time, action, or
condition. For the purposes of pattern detection, several
alternative definitions for a hypoglycemic event are possible.
Prior to defining hypoglycemic events, it is relevant to examine
the characteristics that are indicative of a hypoglycemic event
which have clinical significance. Events of clinical significance
include falling to a very low glucose level, even where the dip in
glucose level occurs only for a short amount of time. Further,
depending on the predefined threshold level, a glucose level which
remains below the threshold level, even for a long period of time,
may not be considered a clinically relevant event. For example, if
such a pattern fails to repeat over a period of several days, it
may indicate that the user has reasonable control of the glucose
level, and no corrective action may be necessary. Additionally, a
glucose level which remains substantially below a first threshold
level, while not reaching what one would consider dangerous levels,
for substantially long periods may indicate a need for corrective
action.
[0188] The above described glucose pattern detection methods will
be described in greater detail with reference to FIGS. 5A-5E below.
FIGS. 5A-5E illustrate example graphs of glucose levels over a time
period according to some embodiments. For example, the graphs of
FIGS. 5A-5E plot estimated glucose values (EGV) over several hours.
As used herein an estimated glucose value (EGV) means a glucose
value estimated by a glucose monitoring system. The glucose
monitoring systems described herein are typically continuous
glucose monitoring systems, but the disclosed embodiment are not
limited to just continuous glucose monitoring systems. Also, an
estimated glucose value can be a raw data value, calibrated data
value, filtered data value or the like. Returning to FIGS. 5A-5E,
for the purposes of discussion, two threshold levels for evaluating
the EGV values are assumed. These thresholds include a first
threshold value TH1 and a second threshold TH2 which is lower than
the first threshold TH1. For example, TH2 may be a low EGV value,
such as about 55 and TH1 may correspond to a higher threshold such
as an EVG value of about 80.
[0189] With reference to FIGS. 5A-5E, analysis of what may be
considered a "hypoglycemic event" will be discussed. Based on this
analysis, a definition of terms that that are helpful in specifying
a clinically relevant hypoglycemic event may be developed.
[0190] For example, the definition of the start point and end point
of an episode may be defined with reference to FIGS. 5A-5E. The
start of an episode may be considered to occur when the EGV value
falls below the first threshold TH1 for the first time. The end of
an episode may be considered to occur when the EGV has been come
back up to a normal level. However, the end of an episode may not
necessarily correspond to the time at which the EGV value reaches a
level above the first threshold value TH1. As will be discussed
below, there can be a few choices for deciding when the episode has
ended.
[0191] These choices include, for example, a time at which the EGV
has been above TH1 for more than a certain period of time (e.g.
about 45 minutes). This may be referred to as "sufficient time" end
point determination. Additionally or alternatively, the end of an
episode may be defined as the time at which the EGV reaches a
certain high value. This can be defined as a point at which the EGV
reaches a value of TH1+D1 for some predetermined offset value D1.
This may be referred to as "sufficient value" end point
determination.
[0192] Further, as another example, the end of an episode may be
determined relative to a nadir value. As one example, a nadir value
may be calculated by computing, at a target point, the average EGV
using an interval (e.g. 30 minute window of data) around the target
point. The calculation may be performed at every point of an EGV
graph. The nadir can be defined as the first point in the episode
where the average is the lowest. The end of an episode may be
determined if the EGV rises by more than a predetermined value D2
from a calculated nadir value while also being above the first
threshold value TH1. This may be referred to as "nadir offset" end
point determination.
[0193] The latter two criteria, namely, the sufficient time end
point determination and the nadir offset end point determination
criteria, are based on the assumption that if the EGV rises
sufficiently, then it is likely that the user took some action
which resulted in the increase of EGV. Therefore, it may be
concluded that any subsequent low is due to a different cause and
corresponds to a different event.
Qualifying an Episode as an "Event"
[0194] The above definition of an episode is a general one and it
is possible that not all such episodes are clinically significant.
The following discussion focuses on detecting episodes that are
clinically significant.
[0195] First, however, a definition of "average difference" in an
episode is provided. The average difference may be defined as the
average value of a period in an episode which identifies how far
the EGV has been below the threshold value of TH1 on average during
the episode. More precisely, the average difference (AD) may be
defined by Equation 1 below:
AD=(.SIGMA.TH1-EGV(P))/X Eq. (1)
where P corresponds to the subset of points that include all points
during the episode that are below the first threshold value TH1,
and X corresponds to the total number of points in the subset
P.
[0196] The following description identifies those episodes that may
be of clinical value, and may be identified as a hypoglycemic
event. According to a first example, with reference to FIG. 5A, the
EGV value falls below the first threshold TH1 but remains above the
second threshold TH2. Since the EGV remains near the first
threshold value TH1, the average difference AD is relatively small.
A set of predetermined average difference thresholds AD.sub.TH1 and
AD.sub.TH2 may be defined for determining whether a calculated
average difference value for a given episode qualifies as a
hypoglycemic even. The first average difference threshold
AD.sub.TH1 may be greater than AD.sub.TH2. In addition, For
example, AD.sub.TH1 may be equal to a value of 15 and AD.sub.TH2
may be equal to a value of 10. In addition, a first time interval
TP.sub.1 may be predefined for comparison with a time at which the
EGV remains below the first threshold value TH1. For example,
TP.sub.1 may be set to a value of 10 hours, but is not limited
thereto.
[0197] If the average difference AD of an episode is more than
AD.sub.TH2 (e.g. 10) and the total time that the EGV is below TH1
is more than the time period TP.sub.1, then the episode may be
considered a hypoglycemic event. Also, in one implementation, if
the average difference AD is more than the first average difference
threshold AD.sub.TH1 (e.g. 15), then the episode may be considered
a hypoglycemic event regardless of the total time the EGV is below
TH1. That is, in the latter case, the total time that an EGV is
below TH1 may be disregarded because the average difference AD is
so large. However, in another implementation, a total time the EVG
remains below the first threshold value TH1 may be a factor in
determining whether or not an episode is considered a hypoglycemic
event when the average difference AD is more than the first average
difference AD.sub.TH1. For example, an episode may be considered a
hypoglycemic event if the average difference AD is more than the
first average difference threshold AD.sub.TH1 (e.g. 15) and the EVG
remains below the first threshold value TH1 and/or second threshold
value TH2 for more than a predetermined threshold amount of time
TP.sub.2, which may be less than TP.sub.1.
[0198] As a non-limiting example with reference to FIG. 5A, the
average difference AD may correspond to a value of 5, while the
period of time that the EGV value remains below the first threshold
value TH1 may be equal to about 1 hour. In this example, even
though the time period of FIG. 5A may be long, the episode of FIG.
5A is not classified as a hypoglycemic event since the average
difference AD is low. That is, since the EGV remains near the first
threshold TH1, from a clinical perspective, no corrective action
may be necessary by the user.
[0199] As a non-limiting example with reference to FIG. 5B, the
average difference AD may correspond to a value of about 12, and
the time period that the EGV is below TH1 may be equal to about 2
hours. As a result, in this example the episode of FIG. 5B may be
classified as a hypoglycemic event since the average difference AD
is greater than AD.sub.TH2 and the time period is greater than
TP.sub.1.
[0200] According to another non-limiting example, with reference to
FIG. 5C, if the EGV falls below TH2 even for a very short period of
time, then the episode may be classified as a hypoglycemic event.
In this case the average difference may be disregarded.
[0201] As a non-limiting example with reference to FIG. 5D, the
average difference AD may be equal to a high value (e.g. about 20)
and the time period that the EGV is below the first threshold TH1
may be relatively short (e.g. about 20 minutes). As a result, the
episode may be classified as a hypoglycemic event since the average
difference AD is greater than AD.sub.TH1, even though the time that
the EGV is below TH1 is less than TP.sub.1.
[0202] Equation 1 above defines the average difference AD as a
simple sum of differences TH1--EGV over a subset of points below
TH1. According to some embodiments, the average difference may be
amplified by using a power of individual differences rather than
the simple difference calculation of Equation 1 above. Further, one
of ordinary skill in the art will recognize that another function
may be used to calculate the average difference AD. For example, a
function which maps the difference to a weight value and bases the
detection criterion on the average weight may be used.
[0203] In calculating the differences, the following choices may
also be considered: first, an average over all the points of the
episode, assuming a zero value for the difference at points where
EGV is above TP.sub.1; second, an average over only those points
where the EGV was below TP.sub.1.
Segment in an Episode
[0204] The concept of a segment in an episode may also be used in
analyzing whether an episode may qualify as an event. Since an
episode may have many points that are close to TH1 (or even above
TH1), identifying parts of the episode that are significant may be
desirable. A segment may be defined as a portion of an episode that
corresponds to the most clinically significant data. For example, a
segment may be defined as a set of consecutive points in an episode
where the EGV is below TH1. Further, if within the set of such
points, the difference (TH1-EGV) is less than (TH1-TH2)*0.1, then
such points may be excluded from the segment.
[0205] When the average difference is computed, the average
difference over each of the segments may be used and the maximum of
the average difference values may be used to characterize an
episode.
[0206] To illustrate the concept of a segment, an example will now
be described with reference to FIG. 5E. Here, a segment is defined
as the portion between time point A and the time point B as
identified by the vertical lines in FIG. 5E. The segment bounded by
lines A and B in this example corresponds to the portion of the
episode where the difference (TH1-EGV) is less than (TH1-TH2)*0.1.
As illustrated, the segment can be considered to include the most
severe portion of the episode and disregard the data points where
the EGV is close to the first threshold value TH1. As a result, the
analysis of the episode may focus on the most clinically relevant
data points. The episode may then qualify as an event if the
segment portions of the episode qualify the episode as an event
using, for example, any of the event qualifying conditions
discussed above.
[0207] In the foregoing description, one of ordinary skill in the
art will recognize that the number of threshold values is not
limited to two threshold values as discussed above with respect to
FIGS. 5A-5E. Rather, any number of threshold values TH may be
defined for analyzing the data. Further, the number of average
difference values AD and time periods TP may also include any
number of predefined values. One of ordinary skill in the art will
recognize that the above number of thresholds, number of time
periods, and predetermined values for each threshold and time
period are illustrative in nature and are used for ease of
description of some aspects of the disclosure.
[0208] Further, any of the above described threshold values and
time periods are variables that may be user configurable (e.g. by
manufacturer, patients, health care providers, guardians, etc.).
These include, for example, the first time period TP.sub.1, first
threshold value TH1, the second threshold value TH2, the first
average difference value AD.sub.TH1, and the second average
difference value AD.sub.TH2.
Distance Between Two Events
[0209] The distance, or the difference in time of modal day,
between two events may also present clinically important data.
There can be several definitions for the distance. For example, the
distance may be defined as the distance between events H1 and H2 as
the time span between the mid-points of events H1 and H2, or the
time span between the closest points of H1 and H2. Using the latter
definition, if the two events H1 and H2 are overlapping, then the
distance may be defined as zero. If the events do not overlap, the
distance may be defined as the time between the end of one event H1
and the beginning of the other event H2.
[0210] The distance may also be defined using the nadirs in H1 and
H2. With the concept of a segment defined above, a particular time
in an event may be at the midpoint of the deepest segment in the
event. The distance between H1 and H2 may then be defined as the
time span between the mid-points of the deepest segments in H1 and
H2.
Repetitive Pattern Identification
[0211] Given a time interval TS, a pattern may be defined as a set
S of events such that the distance between any two events in the
set S is less than a time interval TS. Further, in some
embodiments, no two events from the same day may be selected for
inclusion in the set. In some embodiments, if H1 and H2 are two
events such that the actual time difference between the two events
H1 and H2 (not the modal day time difference) is less than 12
hours, then the events are not added to set of patterns S.
[0212] With these baselines, once TS and the number of events
needed to recognize a pattern of events is specified, all the
events may be scanned to identify repetitive patterns. This initial
grouping may then provide a few candidate sets for patterns. These
candidate sets may then be subsequently filtered for identifying
patterns to output to the user.
[0213] For example, a set of events A, B, C, D, E, and F may occur
on different days such that the events are each one hour apart on
respective days. Further assuming TS is 2 hours and the number of
events required for a pattern is three, the following sets of
potential candidates may result: {A, B, C}, {B, C, D}, {C, D, E},
and {D, E, F}. From these initial sets, the sets which should be
identified as patterns may be selected.
[0214] One or more of the following filters may be applied for
selecting the final patterns or reducing the number of patterns
prior to selecting final patterns from candidate sets: [0215] 1.
Selecting the earliest set: Here, the sets may be sorted based on
the earliest modal time of an event in each set. The earliest set
is selected and all the events included the earliest set are
deleted from the remaining candidate sets. The filter then
iteratively proceeds in a likewise fashion with the next earliest
remaining candidate set until every remaining candidate set has
been selected. Note that a candidate set that no longer qualifies
as a set under the predefined rules may be eliminated. [0216] 2.
Prioritized by time of day: Using this filter, a certain priority
can be assigned to the candidate sets based on the time of day. For
example, the modal day can be divided into eight intervals of three
hours each. Each interval is assigned a weight or score. Based on
the assigned weight or score, the candidate sets may be prioritized
by the weight or score of the events in the pattern, such as a sum,
an average, a median or the like of the weights or scores in a
candidate set. The highest priority set is then selected, and the
events included in the selected set are deleted from the remaining
sets. The next highest priority set may then be selected based on
the assigned weights of the events remaining in its set, and all
events in the currently selected set that are also present in other
candidate sets are eliminated. The process may be iteratively
performed until all candidate sets are processed in this fashion.
Note that a candidate set that no longer qualifies as a set under
the predefined rules may be eliminated. [0217] 3. Prioritized by
type of events: Another filter may assign a weight or score to each
event based on the severity of the event. For example, a deep event
may be assigned a high weight or score. For example, an event based
on the average difference may be assigned a weight that is
proportional to the average difference. Additionally, or
alternatively, the weight or score may also take into account the
duration of the event. The candidate sets get prioritized by
summing, averaging or taking the median value of the assigned
weights or scores events in each set. These priorities may then be
used to sort the candidate sets and eliminate candidate sets as
discussed in filters 1 and 2, above.
[0218] In some embodiments, each of the above filters are
iteratively applied (e.g., filter 1, then filter 2 and finally
filter 3) until either a predetermined threshold amount or fewer
number of pattern sets remain or each of the filters have been
completely applied. If more than a predetermined threshold number
of pattern sets remain after applying all of the filters, then a
threshold number of pattern sets can be selected as a final set
based on a priority, such as a priority by time or priority based
on type of event.
Pattern Analysis by Event Detection
[0219] The previous sections define and outline exemplary methods
for detecting patterns of events, with much of the description
describing hypoglycemic events for illustrative purposes. This
section further elaborates on an implementation on a computing
device to detect patterns based on the above-described methods. The
process will be explained with reference to FIG. 6.
[0220] FIG. 6 illustrates a flowchart of a pattern detection
process 600 according to some embodiments. The various tasks
performed in connection with process 600 may be performed by
hardware, software, firmware, or any combination thereof
incorporated into one or more of computing devices, such as one or
more of sensor system 8 and display devices 14, 16, 18 and 20. It
should be appreciated that process 600 may include any number of
additional or alternative tasks. The tasks shown in FIG. 6 need not
be performed in the illustrated order, and process 600 may be
incorporated into a more comprehensive procedure or process having
additional functionality not described in detail herein. For
illustrative purposes, the following description of process 600 may
refer to elements mentioned above in connection with FIGS. 1-5.
[0221] The pattern detection process 600 may be performed based on
user request, or automatically. For automatic pattern detection,
the process may be performed based on any number of predetermined
or user defined variables. For example, according to some
embodiments, the process 600 is performed automatically (without
user interaction triggering the process) when one or more of the
following conditions have been satisfied: (i) it has been at least
one day since the process was last performed, (ii) no alerts are
pending at this point, and (iii) the glucose monitoring system is
currently between sensor packet transmission sessions (e.g.,
between sensor system 8 and display device 14, 16, 18 20).
Additionally or alternatively, a restriction that the process 600
is only performed at a particular time of the day (e.g., at night
time), or that that at least a certain number of days' worth of
data (e.g., 7 days of data) is stored for analysis may be applied.
Any of these restrictions may be user configurable (e.g., allow or
disallow the restriction) depending upon user preference. In some
embodiments, a user can configure the restrictions using user
interface of display device 14, 16, 18, or 20 discussed above with
reference to FIG. 1.
[0222] There may be additional optimizations provided while
performing the pattern detection process 600 based on automatic
triggering. For example, based on the performance of the process
600, the process may be performed in several installments over a
period of time. Further, when the process is performed upon user
request, a display to the user of status of the analysis or other
information may occur while the data is being processed.
[0223] As illustrated in FIG. 6, the process 600 begins by
selecting a starting point (or total timeframe) of data to be
analyzed in block 601. The start point may correspond to the
earliest point where EGV data exists or is available. For example,
this time may be as old as one month from the time that the process
begins, or even older, depending on the amount of stored data. The
start time may also be set to optimize efficiency, and reduce the
analysis of excessive data, by setting the start time to a time
later than the earliest point where EVG data exists, such as one
week, two weeks, three weeks, one month, two months, six months,
one year or other time period, when EGV data to be analyzed is
available.
[0224] If the process 600 has previously run, the start time may be
set based on the oldest episode or event used in a prior iteration
of the process. For example, if during the last iteration of the
process, there are any episodes or events that were detected but
not fully processed, the starting point of the oldest such episode
or event may be used as the starting point. If there are no
unprocessed episodes or events, the time that the process was run
last may be set as the starting point.
[0225] The process proceeds to block 603, where new episodes are
detected. Episodes may be detected as discussed above, such as with
respect to FIGS. 5A-5E. As discussed above, new episodes may be
detected by scanning the EGV records and using predefined criteria
to detect the starting and ending of episodes. For example, an
episode may be determined to have started when the EGV falls below
TH1 and stays low for at least 15 minutes (e.g. three data points
at five minute intervals). Because the first data point in the data
being analyzed may be part of an episode under the definitions for
an episode being used, the time of the available EVG data point may
be considered as the start time of an episode. An episode may be
considered to end when the EGV remains above TH1 for at least 45
minutes, or when the EGV reaches TH1+40 mg/dL.
[0226] At block 604, the detected episodes are filtered based on
their characteristics to arrive at a set of events. As discussed
above, only the events that are clinically relevant for identifying
patterns are used for pattern detection. According to one example
identifying hypoglycemic episodes, a value of average difference
thresholds AD.sub.TH1 and AD.sub.TH2 may be set for comparison with
an average difference AD of the episode. AD.sub.TH2 may defined as
(TH1-TH2)*0.33 and AD.sub.TH1 may defined to be (TH1-TH2)*0.66. If,
in an episode, the EGV reaches TH2 the episode is characterized as
a hypoglycemic event. If, in an episode, there is at least one
segment where the average difference is greater than AD.sub.TH2 and
the segment is longer than a predetermine time period (e.g. 40
minutes), then the episode may be characterized as hypoglycemic
event. If in an episode, there is at least one segment where the
average difference is greater than AD.sub.TH1, then the episode may
be characterized as a hypoglycemic event.
[0227] At block 605, all available events to be analyzed are
identified and grouped. In one implementation, an event is
determined to be available if it was not previously identified as
part of a pattern, even if the event was previously identified as
an event during a prior iteration of process 600. Thus, both "old"
and new events may be considered.
[0228] In some implementations, once all available events are
gathered, sets of events may be formed and patterns determined
based on the sets of events at block 606. Events may be grouped to
form one or more sets of events based any qualifying criteria
discussed herein.
[0229] In one implementation, each set of events may be based on a
distance between events. The distance based on segments may be used
to define the distance between two events. For example, the
distance may be the time span between the mid-points of the deepest
segment in each event. The distances may then be used to determine
the events that are close to each other. Since the expected number
of hypoglycemic events is generally small, this determination may
be performed by iteratively identifying all events that are within
a predefined distance of each event.
[0230] For example, if an event A is between 11 PM and 6 AM, then
any event that is within a 4 hour window of event A will be
included in the set for event A as long as the event is within 11
PM and 6 AM. The window of time may be variable based on the time
of the event to be analyzed. For example, for an event A occurring
between 6 AM and 11 PM, only those events within predetermined
window of time, such as 2 hours, of event A may be included in the
pattern set around event A. A set that has more than a
predetermined number of member events (e.g., 2, 3, 4 or more events
in a set) may be considered a pattern.
[0231] In some implementations, process 600 can identify and delete
duplicate pattern sets that may have been formed in the previous
step. For instance, given sets {A, B, C} and {B, A, C}, then the
latter set may be deleted.
[0232] Based on the remaining pattern sets, patterns are selected
for display in step 607. It should be appreciated that there are a
myriad of approaches that can be implemented to select patterns for
display, outputting or further processing. One approach can be to
simply select all patterns. Another approach is to select all
patterns if the total number of patterns is less than a
predetermined threshold number, and filter the patterns using one
of the filters discussed herein, for example, until less than the
predetermined threshold number remain. A further approach is to
select only patterns that satisfy certain predetermined conditions,
such as patterns falling within a particular time of day, patterns
that follow a particular occurrence (e.g., a meal, exercise or
medication administration) or strength of a pattern based on a
strength metric (e.g., using a weighting or scoring of events in a
pattern).
[0233] In some implementations, the patterns which are selected may
be based on particular preferences indicated by user. In this way,
a user can define how many patterns the user wants to view, the
strength or weakness of patterns to view, patterns occurring at
particular times of day or after particular occurrences, and the
like.
[0234] To illustrate, in one implementation, priorities for the
remaining sets are determined and pattern sets filtered based on
the priorities. The priorities may be assigned according to the
time of the events, severity of the events or some other clinical
relevance criterion, as discussed elsewhere herein. For example, a
hypoglycemic event between 11 PM and 6 AM may be given a priority
of 2. A hypoglycemic event that crosses TH2 or with average
difference greater than AD.sub.TH1 may be given an additional
priority of 1. Further, an event that occurred within a
predetermined amount of time, such as within the past day, may be
given an additional priority of 1. The priority of an entire
pattern set may be defined as the sum of the priorities of all the
evens within the pattern set.
[0235] A predetermined number of the highest priority pattern sets
for display to the user may be selected. If there are more than one
pattern sets that have the same priority, then the pattern set
having the earliest event may be selected. Alternatively, the set
having the most recent event may be selected. In accordance with
some embodiments, once an event is selected for a pattern set that
is shown to the user, it is taken out of the pool of events that
can be used for later pattern detection in a subsequent iteration
of process 600. That is, the event is no longer available for
selection in step 605 in a subsequent iteration of process 600.
[0236] At block 608, the selected patterns are outputted for
display or further processing. For example, the patterns may be
displayed on a user interface, such as one of the user interfaces
described in FIGS. 9-11. In some implementations, alerts can be
triggered based on the selected patterns instead of or in addition
to displaying the selected patterns. And is some implementations,
the selected patterns are processed further to modify some other
process, such as a medication administration routine (e.g., insulin
administration routine) and the like. Further, the selected
patterns may be logged or stored in memory of the system for
subsequent access or display to a user.
[0237] In accordance with some embodiments, after events are
identified as discussed above in step 604, patterns may be detected
at step 606 by sorting the identified events in increasing order of
a time of day (hour, minute, second of the time of day) associated
with the event. The time of day can be any way of associating a
time of day with an event discussed herein, including start time of
the event, end time of the event, mid-point time of the event, time
corresponding to a nadir point of the event, and the like. Once
sorted by time of day, the events may be scanned in order of the
first event to the last event. At each event, all other events that
are within a predetermined amount of time (e.g., 2 hours) may be
added together to form an event group that defines a pattern. In
this way, a plurality of patterns may be identified from a
corresponding plurality of event groups. Information related to all
or some of the patterns can then be outputted to a user or another
computing device for further processing. In one embodiment,
information associated with each pattern is displayed on a user
interface. The information can include the times associated with
one or more members of the event group forming the pattern, number
of members in each group, an indication of clinical relevance of
one or more of the patterns, glucose value associated with each
member of an event group, an average or mean glucose value of the
members in an event group, and the like. Further, in some
embodiments, the patterns can be analyzed using clinical relevance
criteria and outputted by ranking the patterns from most clinically
relevant to least clinically relevant.
Exemplary Pattern Reporting User Interface
[0238] A user interface 1000 displaying a report that includes a
chart 1002 of glucose data points over a six day period and a chart
1004 of individual events that were determined to form a pattern is
illustrated in FIG. 10. In the chart 1004, the data points of each
event in a pattern are indicated by the same symbol so that the
behavior of each event in a group can be visualized. Further, the
user interface 1000 provides a menu 1006 listing a plurality of
patterns identified in the glucose data being analyzed. The
patterns in the list can correspond to patterns selected using
process 600, discussed above, for example. Each pattern can be
selected by a user using, for example, a pointing device of user
interface 1000. The selected pattern may then be displayed in chart
1004. This way, a user can view the glucose data points associated
with each event that forms the selected pattern.
[0239] In some embodiments, the threshold values and time periods
discussed herein are initially set by a manufacturer, but can be
modified by a user using, for example, one of the display devices
14, 16, 18 or 20 discussed above with respect to FIG. 1.
Pattern Analysis by Aggregating EGV by Time of Day
[0240] According to some embodiments, another approach for
detecting patterns in glucose data (e.g., EGVs) over a period of
time is to aggregate the glucose values by time of the day.
According to some embodiments, the method may include dividing the
24 hours in the day into epochs, each epoch spanning a
predetermined interval of time, such as a 1, 5, 10, 15 or 20 minute
interval. The EGVs for a given epoch from all days over a
predetermined range are aggregated to derive a value for the
particular epoch. If the aggregated value for the epoch shows that
the EGV for that epoch exceeds a predetermined threshold (e.g.,
falls below a predetermined value), the epoch may be flagged as a
match for pattern analysis purposes.
[0241] An epoch may be chosen to have a duration equal to the
periodicity of the sampled data or as small or as large as the
granularity desired for detection/reporting of pattern occurrences.
If the sampled values have a periodicity much smaller than the
epoch duration chosen, then the sampled data can be averaged over
the epoch duration as a single value or, alternatively, the mean
value of the sampled data over the epoch duration can be used as
the value of the epoch.
[0242] Multiple contiguous epochs may be chosen to represent the
desired duration/span to detect patterns. According to some
embodiments, all the epochs that span an entire day may be chosen
and then, if desired, a filter may be applied on the timestamps of
the sampled values to limit which epochs are used in occurrence
pattern detection analysis.
[0243] Epochs may also associate (or contain) the sample values
that match the pattern and that match the epochs time period.
[0244] According to some aspects, the aggregate method may be used
to detect patterns of low or high glucose occurring around the same
time of day. Further, patterns of rapidly falling or rising changes
in glucose around the same time of day may be detected.
[0245] In some implementations, epochs can be scored to facilitate
detection of particular patterns. As mentioned above, a 24 hour
period of a modal day may be divided into equal intervals (e.g. 5
minutes intervals). Each such interval is defined as an epoch. For
an analysis of EGV data over N days, for every epoch T, a function
Score (T) is defined as follows:
Score ( T ) = ( 0 - N TH 1 - EGV ( T ) ) Eq . ( 2 )
##EQU00001##
where TH1 corresponds to a predetermined threshold. In some
embodiments, the value of TH1 can be defined so values that fall
above the predetermined threshold are given a score of zero. In
this manner, all EGVs that are above the TH1 threshold are given
the same weight (i.e. 0), thereby emphasizing EGVs that fall below
the threshold TH1. Scores for each epoch interval are computed. For
example, for a 5 minute interval, scores for all the 288 epochs are
calculated over N days. Assuming a TH1 of 70 and N=7, for example,
and the epoch to be analyzed is for a time period between 10:15 AM
and 10:20 AM the following values for EGV may be detected: EGV (Day
1)=65, EGV (Day 2)=72, EGV (Day 3)=68, EGV (Day 4)=69, EGV (Day
5)=71, EGV (Day 6)=64, and EGV (Day 7)=66. The epoch score for this
time interval is then calculated as:
Score (T)=5+0+2+1+0+6+4=18
[0246] In the above example, a summation of the simple differences
between the threshold TH1 and EGVs are used in defining the score
above. However, a function of the EGV may be used instead. For
example, each EGV can be assigned a weight, and the sum or mean of
the weights may then be used to calculate the score of the
epoch.
[0247] Epochs may be flagged as a match based on their scores. For
example, given a predetermined threshold score D, an epoch T may be
flagged as a matching epoch if Score (T)>D. As discussed above,
the simple difference value of Equation 2 above is not the
exclusive way for defining a score. Accordingly, a match may also
be based on other methods depending upon how a score is
defined.
[0248] In some embodiments, a criterion that can be used for
identifying matches is the number of days that contribute to the
score at a given epoch. For example, while performing a seven day
data analysis, if an epoch includes two days for which the EGV was
very low then it may be considered a match. However, if an epoch
had an EGV that was closer to TH1, then it will be considered a
match only if a majority of days (e.g. 5 days) contributed to that
epoch.
[0249] Patterns may be identified by scanning all the epochs for a
modal day starting at, for example, the beginning of a day (e.g.,
at time 00:00). At any point of time, the scanner can be in one of
two states: "In event" or "Out of event". The scan may start in an
"Out of event" state and as soon as the first epoch is encountered
that is flagged as a match, the scan may then enter the "In event"
state. In some implementations, the scan then transitions to an
"Out of event" state only after the scan counts a predetermined
number of epochs (e.g. nine epochs) that are not a match, or if a
predetermined number of continuous epochs (e.g., 3) are not a
match). Should epochs that fall on the end of a group of "In event"
epochs not be a match, the "in event" state can be moved back to
the last flagged epoch. Once all the epochs are scanned, any group
of consecutive epochs that are "In event" may form one pattern.
[0250] It is appreciated that there may be patterns that start very
late in one day and cross over to the beginning of the next day. To
account for such patterns, the same data may be juxtaposed twice
when the scanning is performed on the data to account for patterns
spanning over more than one day.
Pattern Calculator Using Data Aggregation
[0251] In some embodiments, a pattern calculator, or pattern
detector, may be used to aggregate data in a set of epochs to
detect patterns in the data that match a pattern definition. The
glucose data can be weighted according to a weighted assignment map
or function, or can be the actual glucose concentration value of
the data point. The pattern calculator result can be a set of
patterns detected. Each pattern can be a sequence of one or more
epochs that contain contributing values that meet pattern bound
criteria and pattern threshold criteria.
[0252] FIG. 7 illustrates a process 700 for pattern detection using
weighted epochs in accordance with some embodiments. The following
algorithm may also be used to detect a pattern in a host's glucose
concentration over time. The various tasks performed in connection
with process 700 may be performed by hardware, software, firmware,
or any combination thereof incorporated into one or more of
computing devices, such as one or more of sensor system 8 and
display devices 14, 16, 18 and 20. It should be appreciated that
process 700 may include any number of additional or alternative
tasks. The tasks shown in FIG. 7 need not be performed in the
illustrated order, and process 700 may be incorporated into a more
comprehensive procedure or process having additional functionality
not described in detail herein. For illustrative purposes, the
following description of process 700 may refer to elements
mentioned above in connection with FIGS. 1-6.
[0253] At step 702, process 700 obtains glucose data for a
designated date range. In some embodiments, the glucose data
includes sampled data generated by sensor 10 that has been
processed (e.g., filtered, averaged, and the like) and calibrated.
In addition to glucose concentration values (e.g., EGV), the
glucose data can include information associated with the glucose
data, such as the time when a glucose concentration value was
present in a subject. This time may be a measurement time, although
there may be a time lag between a measurement time and the
associated time that a given concentration is present in a target
area of the subject. For example, there may be a time lag between a
measurement of a subcutaneous glucose concentration of a subject
and a blood glucose concentration (target area) of the subject.
[0254] The glucose sensor data can be stored in computer memory of
a system implementing process 700. The designated date range can be
all glucose data available to the system (e.g., stored in memory of
the system) implementing process 700 or a portion of the available
data. For example, the glucose data can be all glucose data stored
in the system spanning the past week, month, several months or
year.
[0255] At step 704, process 700 applies bounds or filters to the
glucose data to determine which data points may be considered
contributors to the pattern detection. The bounds or filters may be
defined by any number of criteria used to determine if a data point
is a contributor to a pattern. For example, a data point can be
determined to be a contributor using one or more of the following
criteria: (i) a value range criteria defined by upper and/or lower
bounds (e.g. below a value of 70 or within a range of 70 to 39),
wherein a data point's value can satisfy the value range criteria
if the data point's value falls within the upper and/or lower
bounds, for example; (ii) a time range criteria defined by starting
and/or ending times in a day (e.g. 4 AM to 10 AM), wherein a data
point's value does not satisfy this criteria if the data point
correlates to a time (e.g., was measured at a time or is determined
to indicate a user's glucose concentration at a time) that falls
outside of the time range criteria; (iii) a day of the week
criteria defined by a subset of days of the week to be analyzed
(e.g. Monday through Friday or Saturday and Sunday), wherein a data
point does not satisfy this criteria if the data point is
correlates to a day of the week that falls outside of the day of
the week criteria. Any other criteria can be applied in combination
with or separately from any of the examples provided above. For
example, the filter may analyze only data points that correlate to
a time that falls within a time frame (e.g., 3 hours) of the
occurrence of a particular event when information is available
indicating when the event has occurred. An occurrence of event can
be a time when a user exercised, consumed a meal, administered
insulin or other medication, slept or the like.
[0256] In some embodiments, a data point is considered to be a
contributor only if the data point satisfies all of the criteria
applied in step 704. In other embodiments, a data point need not
satisfy all criteria to be considered a contributor. For example, a
data point can be considered a contributor if it satisfies criteria
associated with the occurrence of one event (e.g., consumption of a
meal), but does not satisfy criteria associated with the occurrence
of a different event (e.g., exercise).
[0257] Data points not considered to be contributors are discarded
from process 700 at step 706.
[0258] Data points considered to be contributors in step 704 are
then assigned weighted values in step 708. In some embodiments,
each glucose value of the contributor data points has a
corresponding weighted value as defined by a weighted assignment
function or map. Accordingly, each contributor can be assigned a
weighted value as defined by a weighted assignment function or map
(also referred to herein as "mapping a weighted value to a
contributor data point"). In some embodiments, the weighted
assignment function or map can be embodied electronically in a
lookup table.
[0259] Exemplary weighted assignment maps are illustrated in FIGS.
8A-8D. In FIGS. 8A-8D, the x-axis is a range of glucose values and
the y-axis are corresponding assigned weighted values. The range of
glucose values along the x-axis need only encompass the range of
glucose values of value criteria applied in step 704, should a
value criteria be applied in the bounds step 704, because values
outside of the value criteria range should not be considered
contributors and hence not be considered in the weighted assignment
step 708 of process 700. In some implementations, the weighted
values are scaled from 0 to about 1, where a higher weight is given
to values considered to be more clinically relevant. However, it is
appreciated that there are a myriad of different ways to assign
weighted values.
[0260] While FIGS. 8A-8D illustrate weighted maps in column chart
format, it is understood that the weighted maps can be in other
formats, such as a line chart format. Regardless of the format of a
weighted assignment map, a data point determined to be a
contributor in step 704 can be assigned the weighted value
associated with the glucose concentration value as defined in the
weighted assignment map. It should also be appreciated that a
mathematical function can be used to describe a desired weights to
corresponding values, and such a mathematical function can be used
instead of a map.
[0261] FIG. 8A illustrates a weighted assignment map which is
designed to be sensitive to sever hypoglycemic events. As
illustrated, the map exhibits an exponential chart of the weighted
glucose values, where lower glucose values are assigned
exponentially higher weighted values than the higher glucose
values. Indeed, values 50 and above in the map of FIG. 8A are
assigned a weighted value of 0.
[0262] FIG. 8B is an exemplary weighted map according to an all or
nothing approach. In FIG. 7B, all values that are below a glucose
value of 55 are given a weight of 1, while any glucose values above
a value of 55 are given a 0 or weight.
[0263] FIG. 8C is a weighted assignment map designed to have a
non-liner polynomial approximation of clinical importance. The
polynomial function may be predefined to identify glucose data of
clinical importance. In the example of FIG. 8C, data points having
values in the range of about 60 to 70 are assigned low weighted
values to indicate that those values have relatively low clinical
importance--albeit still some clinical importance. Data points are
assigned increasingly higher weighted values along the range of
about 60 to 50 to indicate that glucose concentration changes in
this range are clinically important. That is, for example, a
glucose value of 60 is notably, clinically different than a glucose
value of 50. Further, data points having a value from about 50 to
39 are assigned large weighted values, although the values change
relatively little across this range. This reflects a presumption
that while these data values are determined to be of high clinical
importance, the clinical relevance across this range is relatively
the same.
[0264] FIG. 8D illustrates a combination of three exemplary
weighted assignment maps: a Low 802, Target 804, and High 806 map.
The combined map 8D has an x-axis that ranges from 39 to 401 to
illustrate that some embodiments can recognize patterns across a
wide spectrum of glucose data, not just in low glucose ranges.
Although maps 802, 804 and 806 could be used together in process
700, it should be understood that the exemplary process 700 would
not be able to distinguish between data points that contribute to
the respective low, target and high patterns. Instead, it is
intended that process 700 be repeated using each map 802, 804 and
806 separately so that separate low, target and high patterns can
be identified.
[0265] Further to FIG. 7, process 700 continues with assigning each
contributor to a corresponding epoch in step 710. As discussed
above, an epoch spans a predetermined amount of time of a day, such
as a 5, 10, 20 or 30 minute interval of time. While the interval of
each epoch can correspond to the sample interval of the glucose
data being applied in process 700, it is appreciated that the epoch
interval need not be limited by the sample interval.
[0266] As an illustrative, a non-limiting example of process 700, a
contributor data point that is representative of a host's glucose
concentration at 10:27 a.m. (as indicated by its timestamp, for
example) would be assigned to an epoch that spans that time, such
as an epoch spanning from 10:25 a.m. to 10:30 a.m. in the example
of epochs spanning 5 minute intervals. In addition, all other
contributor data points representative of a host's glucose
concentration measured between 10:25 a.m. to 10:30 a.m. are
assigned to that same epoch.
[0267] Thus, step 710 can be said to effectively group contributors
according to times of the day being analyzed.
[0268] Next, in step 712, pattern thresholds can be applied to the
epochs. Pattern thresholds can include one or more or the
following: a threshold minimum number of contributors in an epoch,
a threshold average weighted value of the contributors in the
epoch, a threshold medium weighted value of the contributors in the
epoch, a threshold sum of the weighted values of the contributors
in the epoch, a threshold average difference of the weighted values
of the contributors in the epoch, a threshold standard deviation
value of the weighted values of the contributors in the epoch, and
a threshold correlation value. The pattern thresholds can also be
defined in terms of percentages, such that the pattern thresholds
are defined based on the bounds applied in step 704. For example,
should the bounds applied in step 704 of process 700 include
criteria of data points spanning one week, a pattern threshold of
55% of the time can correspond to a minimum threshold of 4
contributors.
[0269] In step 714, process 700 matches epochs based on the pattern
thresholds applied at step 712. In some embodiments, an epoch is
determined to be a match only if all of the pattern thresholds are
satisfied. In other embodiments, all pattern thresholds need not be
satisfied for an epoch to be considered a match; for example only
one or some number less than all of a plurality of pattern
thresholds need be satisfied.
[0270] As an illustrative example of step 714, pattern thresholds
can be defined as a minimum of three contributors in an epoch with
the average value of the contributors in the epoch exceeding 0.50.
In such an example, an epoch would be determined to be a match if
it has three or more contributors and the average weighted value of
the contributors in that epoch exceeds 0.50.
[0271] Process 700 can then identify pattern occurrences in step
716. A variety of ways can be used to identify a pattern. In some
implementations, each matching epoch can be considered a pattern.
In some implementations, a pattern is identified when there is a
predetermined minimum number of contiguous matching epochs; for
example, two or more contiguous matching epochs. In some
implementations, a pattern does not need to have a strictly
contiguous set of epochs. Instead, a predetermined number of
non-matching epochs or a ratio of matching to non-matching epochs
may be allowed within a pattern or before pattern is determined to
end. In some implementations, a first threshold number (e.g., two,
three, or more) contiguous epochs starts a pattern and the pattern
is determined to end only after a second threshold number (which
can be the same of different from the first threshold number)
contiguous non-matching epochs are identified. Some implementations
apply a combination of any of the above criteria for determining a
pattern.
[0272] Step 718 outputs information associated with the identified
pattern(s). For example, start and end times of an identified
pattern may be reported to a user as the assigned starting and
ending times of the first and last epoch in the pattern sequence.
Attributes or properties of a pattern can also be outputted. The
attributes or properties can be defined by any calculation or
statistic related to the contributors in the occurrence. These
calculations may be performed on the sampled values and/or weighted
values of the contributors and then be used to compare or describe
the resulting patterns.
[0273] Further, information associated with the patterns can be
outputted to a user interface of a computing device, such as one or
more of devices 14, 16, 18 and 20 of FIG. 1, in the form of alerts
or reports. Information associated with the identified patterns can
also be outputted to a medication administration device that can
use the pattern information to form or modify a medication
administration routine. For example, pattern information can be
sent to an insulin pump controller that forms or modifies an
insulin administration routine based on the pattern information,
which can include administering more or less insulin during certain
times of the day.
[0274] While the above described example of process 700 assigns
weighted values to contributor data points at step 708, in some
embodiments, contributor data points are not weighted, but rather
retain their glucose value. Pattern thresholds at step 712 can then
be defined based on glucose values rather than weighted values.
[0275] Based on the above described aggregation process 700, a
significant reduction in complexity can be achieved by allowing
each value to have a weighted contribution before being excluded
from analysis and thus allowing multiple contributors to have a
significant aggregate value where any single value may not have
been previously considered significant on its own. Consideration of
which values are near in time to each other may also be delayed
until after all values have contributed. The resulting weighted
aggregation can be more easily interpreted when displayed to a user
and can be more easily scanned for occurrences above a desired
threshold for a pattern match.
[0276] Furthermore, based on the above methods, the strength of a
glucose value's contribution to a pattern may be reduced to a
simple weighted numeric assignment. The weighted assignment map can
be tuned specifically to match clinical relevance as determined by
trained professionals or desired by a user, and where the map can
support a complex non-linear curve that more closely matches
clinical relevance and/or user specific factors and preferences.
Different weighted assignment maps can be chosen for different
glucose ranges to match each range's unique and different clinical
needs. Weighted assignment maps can be scaled or amplified to
increase or decrease their sensitivity to detecting a particular
pattern.
[0277] According to some embodiments, the aggregate contribution of
glucose values within an epoch of time is easily displayed and can
be easily compared or ranked with other epochs. This concept of
condensed volume of information can be more understandably
presented to users.
[0278] Further, the above methods may eliminate the need to define
or detect individual episodes within a single day when the goal is
only to detect coincidental patterns of glucose over time (e.g.
multiple days). However, the techniques described above can also be
used to detect individual episodes, e.g. a single day pattern of
glucose values. Some potential advantages can be realized when
using the weighted values to determine inclusion in the episode and
using the accumulated values as a volume when determining an
episode's rank or importance.
Example User Interface for Reporting and Displaying Patterns
[0279] In some embodiments, a user interface of secondary display
device 14, 16, 18, 20 may be used to configure and modify settings
of process 700 and output results of process 700. For instance, not
only can a user view results of the patterns detected using process
700 in a convenient fashion, but the user can use the user
interface to select or modify bound criteria, select of modify
weighted assignment map(s) and select or modify pattern
thresholds.
[0280] FIG. 9 illustrates an example of a modal day chart 900 and
different weighted aggregation charts 902, 904 and 906 that can be
displayed on a user interface according to some embodiments.
[0281] The modal day chart 900 and weighted aggregation charts 902,
904 and 906 each superimpose a plurality of days (seven days shown
in FIG. 9) of glucose value measurements onto a single modal day 24
hour period with the same times of day aligned for each of the days
of the charts. The modal day chart 900 illustrates traces of seven
different days of glucose monitoring measurements, each data point
in a trace indicative of a host's glucose concentration at that
time. Charts 902, 904 and 906 illustrate aggregated data and
patterns based on the three different weighted assignment maps of
FIG. 8D. Chart 902 is associated with the High assignment map 806,
chart 904 is associated with the Target assignment map 804 and
chart 906 is associated with the Low assignment map 802.
[0282] In some embodiments, each epoch may be plotted in a bar
graph format, where the length of each bar represents the weighted
sum of the contributors (aggregation) at a given time. In some
implementations, other statistical algorithms can be applied to the
contributors instead of a summation, such as averaging, applying a
mean, determining a variation, and the like, with the result of the
statistical analysis being displayed in chart 902, 904 or 906.
[0283] Charts 902, 904 and 906 of FIG. 9 illustrate the aggregated
weighted contributors in bar graph form. The length of the bar
graphs can indicate a relative strength of the contributors at the
respective times of day. For example, chart 902 illustrates that a
user's glucose concentration tends to be relatively high around
2:40 to 3:00 a.m., chart 904 illustrates that a user's blood
glucose concentration tends to fall within a target range from
about 7:30 a.m. to 10:40 a.m., although the glucose concentration
can be relatively low from 9:00 a.m. to 10:20 a.m. as illustrated
in chart 906. Further, a user's blood glucose concentration tends
to be relatively low from about 12:00 p.m. to 3:00 p.m.
[0284] Charts 902 and 906 also highlight detected patterns 908 and
910. The patterns may be detected using process 700, for example.
The patterns 908 and 910 are illustrated as darker sections of the
bar graphs, with lines outlining a border of the bars falling
within the pattern. In this manner, the user interface of FIG. 9
highlights the detected patterns in the charts 902, 904 and
906.
[0285] In some implementations, the bars in charts 902, 904, 906
may be displayed as sedimentary layers where each day's
contributors are stacked on top of each other in a different color.
In this way, a user can visually discriminate the contributions to
the bars by day.
Using Response Curves to Improve CGM
[0286] A response curve detection method may be performed in
accordance with some embodiments. Specifically, the detection of
the start of a significant event may be performed by monitoring a
user's EGV over time. This may also include finding a plateau of
the event to indicate when the event is over, and how to cope with
some amount of fluctuation/noise that would still be considered
part of the event.
[0287] Similar to a rate of change calculation, a computer device,
such as one or more of devices 8, 14, 16, 18, 20 of FIG. 1, may
calculate the current response curve. The computer device may have
a response curve alert letting the user know that an event or
response is taking too long to stabilize. The computer device may
display the last few response curves, displaying start time, delta
duration, delta EGV, and EGV rate. The patient may use the response
curve results to make decisions about how to alter their
medication, food intake, or behavior for the next event. For
example, the curve may indicate to the user that certain behaviors,
e.g. exercise or caloric intake, trigger a particular response. As
a result, the user may alter their behavior based on the indicated
pattern information. In addition, the user may provide as input
details regarding the behavior which may be analyzed and displayed
alongside the detected patterns. For example, the user may provide
the number of calories consumed, the type of food consumed, and/or
the type of activity performed by the user at various time periods
in a day. These inputs may be used for pattern analysis and
indicate patterns in EGV levels to the user based on the user's
behavior.
[0288] Further, the computer device may prompt the user to enter an
event whenever the computing system detects a significant response
curve. For example, following a period of exercise, the user's EGV
levels may fall below a particular threshold or may exhibit a
particular response curve which is detected by the computing
system. The user may be prompted to enter the activity performed
and time of day information in order to analyze and detect pattern
data.
[0289] The response curves (and entered event data) can be
displayed on charts and graphs to aid users in quickly identifying
areas of responses that need better control. For example, the
computing system could use the onset of response curves to
automatically detect pre/post meal times and automatically
calculate pre/post meal statistics. The user may then make long
term decisions, rather than reactionary decisions, regarding
correction of EGV levels based on the statistics and detected
pattern data.
[0290] A computing system may maintain state information regarding
pattern analysis process iterations. For example, the information
may include a list of events that have been detected, including the
start time and end time of each event. Further, the state
information may include the patterns that were selected for
display, including a list of events that make up the pattern. The
state information may also include a pattern analysis log, which
includes the time the analysis was performed, and the time at which
next analysis should start. For example, the time at which the next
analysis should start may correspond to the start time of an
episode that did not end by the time the last iteration was
performed.
[0291] Embodiments discussed herein may assist in training a user
to make better decisions within a few hours of a prior problem.
Rather than constantly requiring the user to enter event data,
embodiments discussed herein may allow a user to focus on entering
information about problem areas that need more attention as opposed
to needing to enter a myriad of information. Further, embodiments
discussed herein may use response curves to create a more accurate
analysis of times of the day instead of only using fixed times
throughout the day.
[0292] It is further believed that some embodiments can improve
diabetes management, improve trend detection, improve glucose
control, reduce hyperglycemic and hypoglycemic events, improve
information for the user to take action upon in a more timely
matter, and improve a patient's recognition of patterns.
[0293] Applying response curves may also introduce retrospective
information over a longer time frame than rate of change
information alone and may be more quantitative than a trend
line.
Further Implementations of User Interface for Displaying Results of
Pattern Detection
[0294] As discussed herein, a user interface may be provided to
allow a user to access and define parameters for pattern detection
and analysis. The user interface can be incorporated into a
graphical user interface of one or more of devices 14, 16, 18, or
20 of FIG. 1, for example.
[0295] In some implementations, the graphical user interface may
enable a user to change the parameters for any of the pattern
detection processes described herein. The following is a
non-exclusive list of example parameters that the user can change:
threshold values (e.g. TH1, TH2, TP1) discussed with reference to
FIG. 6; and bounds criteria, weighted assignment maps, pattern
thresholds, and pattern occurrence criteria (discussed with
reference to FIG. 7).
[0296] In some implementations, the graphical user interface may
allow a user to initiate the pattern analysis at any point in time
and select when the pattern analysis will be run.
[0297] In some implementations, the graphical user interface may
allow a user to view a list of patterns that have been found so
far. For example, the list may display the last five patterns that
have been found. Each item in the list may show, for example, the
following information: ID of the pattern (for example, each pattern
may be coded with a pattern number and may be displayed as Pnn,
where nn corresponds to the ID number of the pattern); and the
dates over which the pattern spans: For example: Mar. 21, 2011 to
Apr. 5, 2011; the time of day where the pattern was found (for
example: 2:00 AM to 5:00 AM).
[0298] In some implementations, the graphical user interface may
allow a user to access a view that displays the trend of the first
day in the pattern (e.g. a 6 hour graph around the pattern). The
user interface may also include user-selectable up and down buttons
to scroll to the next and previous day. This view of the interface
may also have a window displaying trend graphs from all days,
overlapped.
[0299] When the pattern analysis method runs automatically and
finds a pattern, the user interface my trigger a notification to
the user. When the user acknowledges the notification, the list of
patterns may be displayed with the latest pattern highlighted. The
user can then press select the highlighted pattern and view further
details about the selected pattern. The menu showing the list of
patterns may also have a menu item to transition the device such
that the trend screen is displayed to the user.
[0300] The user interface can also include a menu for selecting a
pattern analysis profile from a plurality of user selectable
pattern analysis profiles. Each pattern analysis profile can define
the values of some or all of the thresholds and time periods used
for pattern detection and/or weighted algorithms (e.g., weighted
algorithm maps) depending upon the pattern analysis method being
used. Each profile can also be associated with detection of
particular pattern characteristics, such as identifying a
particular type of episode (e.g., hypoglycemic or hyperglycemic
episode), a sensitivity associated with considering an event an
episode for pattern analysis, and time periods for analyzing data
(e.g., particular times of the data, such as morning, day, evening
or night). As an example, one exemplary profile can be associated
with settings that have a high sensitivity for determining
hypoglycemic patterns in the morning, and another exemplary profile
can be associated with a low sensitivity for determining
hypoglycemic events during the day. A profile associated with a
high sensitivity for detecting hypoglycemic events may define some
or all of the thresholds with higher values and/or time periods
between episodes with lower values than a different profile
associated with a lower sensitivity so to be more likely to
consider an event a hypoglycemic episode and a plurality of
episodes a pattern to alert a user.
[0301] In some implementations, a user can use the user interface
to turn on or off a hypoglycemia reoccurrence risk alert that, when
turned on, triggers an alert if it is determined that there is a
risk of the host reoccurring into hypoglycemia within a
predetermined time of having already detected a hypoglycemia event
or episode. The hypoglycemic event can be characterized by any of
the manners discussed herein or can simply be characterized as the
user's glucose concentration falling below a threshold, such as 55
mg/dL. The predetermined amount of time can be user configurable
and can be 48 hours, for example. In some embodiments, the
hypoglycemia risk alert can be triggered regardless as to whether
the rate of change of the host's glucose concentration is falling
and regardless as to whether the user has another alert set to
trigger an alarm at a low glucose concentration level. In some
embodiments, the hypoglycemia reoccurrence risk alert includes
highlighting a portion of a trend graph displayed on the user
interface that corresponds to the predetermined amount of time
after the detected severe hypoglycemia event. Although not wishing
to be bound by theory, it is believed that a person with diabetes
is at risk for recurring hypoglycemia within 48 hours of a severe
hypoglycemic event and, thus, the hypoglycemia reoccurrence risk
alert can notify a user of this risk.
[0302] A graphical user interface 1100 is illustrated in FIG. 11 in
accordance with one implementation. The user interface 1100 can be
implemented on a display of one or more of devices 14, 16, 18, or
20 of FIG. 1, for example. That is, user interface 1100 can be
embodied as instructions stored in computer memory and executed by
one or more computer processors of device 14, 16, 18, or 20 to
cause display of the graphical user interface on the display
device.
[0303] As illustrated in FIG. 11, graphical user interface 1100
includes a plurality of tabs 1102a-1102k. A user can select one of
tabs 1102a-1102k to cause user interface 1100 to switch to a view
corresponding to the tab. The tabs 1102a-1102k of user interface
1100 include a home tab 1102a, a pattern tab 1102b, a hourly stats
tab 1102c, a daily trends tab 1102d, and distribution tab 1102e, a
glucose trend tab 1102f, a daily stats tab 1102g, and success
report tab 1102h an A1c records tab 1102i, a patients tab 1102j and
an options tab 1102k. User interface 1100 in FIG. 11 illustrates
the view corresponding to the patterns tab 1102. The patterns tab
1002 is selected in FIG. 11.
[0304] User interface 1100 includes a pattern chart 1104. In this
implementation, the pattern chart 1104 includes a modal day trend
chart 1110 section showing day over day estimated glucose value
(EGV) trend lines, a high (above target) pattern plot chart 1112
section that highlights significant pattern ranges (none shown in
FIG. 11), and a low (below target) pattern plot chart 1114 section
with highlighted significant pattern ranges 1116a-1116e, a pattern
insights summary table 1118 and a statistics table 1120. The model
day trend chart 1110, high pattern plot chart 1112 and low pattern
plot chart 1114 can be implemented as discussed above with respect
to FIG. 7 and FIG. 9. Further, as illustrated in FIG. 11, the model
day trend chart 1110, high pattern plot chart 1112 and low pattern
plot chart 1114 each have the same x-axis scale, which can
facilitate comparing the data shown in each of the charts.
[0305] Pattern chart 1104 includes a title 1106 and a legend 1108
located at the top of the chart. The title 1106 can be a textual
description describing a type or types of patterns to be detected
in the pattern plot chart 1112. The legend 1108 include markers
associated with each different EGV trace in the model day chart
1110, where each marker can be differentiated from the markers
making up the other traces by color and/or shape. Further, the
legend 1108 can indicate the day and date associated with each of
the markers.
[0306] Graphical user interface 1100 also includes a user
selectable nighttime time range button 1122. In some
implementations, each patient can have a specific range of time
that indicates times of day when it is considered nighttime, such
as 10 PM to 6 AM as illustrated in FIG. 11, although any other
range can be used instead. Graphical user interface 1100 displays
the currently defined nighttime time range for the patient as
vertical light grey strip lines 1124a and 1124b that coincide with
the start and end of the times, respectively, of the nighttime
range. The strip lines 1124a and 1124b can be shown on the model
day chart 1110 and both pattern charts 1112 and 1114.
[0307] Selecting the nighttime range button 1122 can initiate a
time of day range editor window to be displayed on graphical user
interface 1100. This editor window can allow the user to change the
nighttime range. Changing to the patient's nighttime range can
cause user interface 1100 to automatically update to reflect the
change in nighttime range, such as changing the strip lines 1124a
and 1124b to reflect the change and any insights related to the
nighttime range reflected in pattern insight summary 1118 and
statistics summary 1120.
[0308] Graphical user interface 1100 can also include a display
setting text box 1126 that displays a timeframe (i.e. the range of
dates and/or times of patient data) presently being analyzed and
displayed using the user interface. In some implementations, text
box 1126 can also include an indication if the patient data is
blinded.
[0309] Graphical user interface 1100 also includes a user
selectable target glucose range button 1128. In some
implementation, the target glucose range defines a range of glucose
values in which a patient desires to maintain his or her glucose
concentration. A glucose concentration above the target range can
be considered a "high," and a glucose concentration below the
target range can be considered a "low." Graphical user interface
1100 can display a high line 1130a and a low line 1130b on the
model chart 1110 representing the high of the target range and the
low of the target range, respectively. The sections of model chart
1110 separated by the high line 1130a and low line 1130b can be
distinctively displayed from one another, such as each section
having a different shading or color. In one implementation, the
section of the model chart 1110 above the high line 1130a is a
yellow color, the section between lines 1130a and 1130b is a green
color and the section below line 1130b is a red color. These colors
associated with high, target and low sections can also be used in
other parts of graphical user interface 1100 that relate to
respective high, target or low glucose values. For example, the
text "Night time Low" insight in pattern insight summary 1118 can
be also displayed in a red color because it relates to a low
glucose value as defined by the target glucose range.
[0310] Selecting the target glucose range button 1128 can cause a
target glucose range editor window to be displayed (e.g., a pop up
window) on graphical user interface 1100. This editor window can
allow the user to select of modify the target glucose range.
Changing to the patient's target glucose range can cause user
interface 1100 to automatically update to reflect the change in
target glucose range, such as changing the lines 1130a and 1130b to
reflect the target range change, any insights and statistics in
pattern summary 1118 and statistics 1120 determined based on how
the target range is defined.
[0311] As discussed above, graphical user interface 1100 can
indicate if the user is in a blinded mode (e.g., user interface
1100 can display "Blinded" in the text box 1126). If the user is in
a blinded mode, then graphical user interface 1100 does not display
certain information in accordance with some implementations, such
any view showing a user's actual EGV values. In one implementation,
when in the blinded mode, graphical user interface still displays
the Sensor Usage, Calibrations/day, Target Range, and Nighttime
Range in the Statistics table 1120, but does not display one or
more of the model chart 1110, pattern charts 112 and 1114, and
Glucose Average in the statistics table 1120.
[0312] The high and low pattern plot charts 1112 and 1114 can be
implemented in any manner described above with respect to FIGS.
7-9. To further describe use of high and low pattern plot charts
1112 and 1114, the following illustrates some exemplary
implementations.
[0313] The high and low pattern plot charts 1112 and 1114 can
display a line graph across all 288 of the 5 minute intervals of a
day. Each 5 minute interval's plotted value can be the sum of all
the EGV contributions that were above/below the target range limits
in that interval and where the contribution amount is a weighted
value as function of how far away from the target limits the EGV
value was. In one implementation, the further away from target, the
larger the weighted contribution value is for that EGV in that
interval, but other weighted functions can be used, such as any of
the weight maps or functions described above.
[0314] The high and low pattern plot charts 1112 and 1114 can be
useful to highlight significant time ranges where a set of
intervals have exceeded thresholds for both frequency (n out of m
days) and severity (the average of weighted contributions). In some
implementations, a significant set of intervals is defined as
having at least 3 intervals (15 minutes) and coalesce with an
adjoining significant interval if less than 9 intervals (45
minutes) away. An interval can also be defined in any of the
manners discussed herein with reference to defining a segment or
pattern.
[0315] FIG. 11 illustrates significant time ranges 1116a-1116e in
the Low pattern chart 1114. No significant time ranges were
identified in the High pattern chart 1112. Accordingly, a user can
discern that they may want to try to modify his or her behavior to
better hypoglycemic control.
[0316] In some implementations, the detection of significant time
ranges on the high and low pattern plot charts 1112 and 1114 have
increased sensitivity (i.e. a lower threshold on the average of
weighted contributions) during the nighttime range, which can be
defined using nighttime range button 1122, as discussed above.
[0317] In some implementations of user interface 1100, the
detection of significant time ranges on the high and low pattern
plot charts 1112 and 1114 is automatically disabled if the current
number of days viewed is only one or two days. That is, in some
implementations, at least three possible days of contributing EGV
values are needed for the frequency threshold to be considered.
[0318] In some implementations, the y-axis scale of the high and
low pattern plot charts 1112 and 1114 can be adaptive and relative
to the timeframe of data chosen to be analyzed and displayed. The
scale can be adjusted to be approximately 80% of the maximum
possible sum of weighted glucose contribution values.
[0319] The pattern summary table 1118 of user interface 1100 can
indicate a total number of significant patterns and a description
(e.g., time range) of one or more of the most significant patterns.
The patterns can be grouped by four categories: Nighttime Lows
1132a, Daytime Lows 1132b, Nighttime Highs 1132c, and Daytime Highs
1132d. The most significant pattern in each of the four groups can
be determined by a volume; that is, a total sum of all contributed
values within the pattern interval. A pattern can be considered in
a nighttime group if the patterns start time or nadir point is in
the patient's defined nighttime range, and, conversely, a pattern
can be considered in a daytime group if the pattern's start time or
nadir point is in the patient's defined nighttime range.
[0320] In some implementations, when hovering a computer cursor
(e.g., the pointer controlled by a computer mouse) or finger, in
the case of user interface 1100 having touch-sensitive screen
capabilities, over one of the rows 1132a-1132d of the pattern
summary table 1118, the user interface 1100 can highlight the time
interval corresponding to the selected pattern using, for example,
a shaded vertical strip extending across each of the charts 1104,
1112, and 1114 (not shown).
[0321] The statistics table 1120 can display a variety of
statistical values based on data falling within the selected
timeframe being viewed on user interface 1100. The statistics table
1120 can include: a glucose average 1134a (i.e. the mean of all
glucose values found in the currently selected days being viewed);
sensor usage 1134b (e.g. determined based on the number of days
that contain at least one glucose value out of the number of days
currently selected); calibrations per day 1134c (e.g., determined
based on the total number of meter values found in the currently
selected days divided by the sensor usage days); standard deviation
1134d (e.g., the standard deviation based on all of the glucose
values found in the currently selected days being viewed);
percentage of high values 1134e (e.g., determined based on the
percentage of glucose values above the patient's defined target
range); percentage of low values 1134f (e.g., determined based on
the percentage of glucose values below the patient's defined target
range found in the currently selected days being viewed);
percentage target 1134g (e.g., determined based on the percentage
of glucose values within (or equal to) the patient's defined target
range found in the currently selected days being viewed);
High/Low/Target pie chart 1134h representative of the percentage of
high, low and target values; the currently defined patient's target
glucose range 1134i (defined using target glucose range button
1128, for example); and the currently defined patient's nighttime
range 1134j (defined using nighttime range button 1122, for
example).
[0322] Graphical user interface 1100 also allows a user to select
the timeframe of data to be analyzed and displayed on graphical
user interface 1100. In some implementations, a user can select a
timeframe in the range of 1 to 15 days, 1 to 18-28 days, 1 to 30
days, 1 to 60 days and 1 to 90 days.
[0323] To facilitate user selection and modification of the
timeframe, the user interface 1100 includes a date slider control
1136. The date slider control 1136 can be used for choosing the
timeframe of displayed information in any of the different views
associated with tabs 1102a-1102j.
[0324] In implementation of the date slider control 1136 is
described further with respect to FIG. 12. Date slider control 1136
includes a user-selectable size control button 1202 to choose the
number of days to display (i.e. the number of days in the
timeframe). A user can select this section of the slider 1136 to
cause a drop down menu to be displayed for a user to select a
number of days desired to be used in the timeframe. The size
control button 1202 can also display the current number of days in
the current timeframe. In some implementations, the number of days
is always "whole" days from 12:00 am to midnight.
[0325] Date slider control 1136 also includes start date button
1204. When a user selects the start date button 1204, the timeframe
jumps to the first date of available data to be used with user
interface 1100 (e.g., first date of data stored on device 16, 16,
18 or 20 implementing user interface 1100). Further, slider control
1136 can indicate to the user the first date of available data by
hovering over the start date button 1204, which causes display of a
tool tip 1206 popup that indicates the date of the first data
available.
[0326] Date slider control 1136 also includes end date button 1208.
When a user selects the end date button 1208, the timeframe jumps
to the last date of available data to be used with user interface
1100 (e.g., last date of data stored on device 16, 16, 18 or 20
implementing user interface 1100). Further, slider control 1136 can
indicate to the user the last date of available data by hovering
over the end date button 1208, which displays a tool tip 1210 popup
that indicates the date of the last data available.
[0327] Date slider control 1136 includes a slider bar 1212 that a
user can drag horizontally to modify the date selection timeframe.
Dragging the slider bar 1212 in a right-hand direction (i.e. toward
the start date button 1204) causes the timeframe to increment back
in time from a current date selection, and dragging the slider bar
in a left-hand direction (i.e. toward the end date button 1208)
causes the timeframe to increment forward in time from a current
date selection. Slider bar 1212 also displays the currently
selected date range (start to end, inclusive) of the timeframe. To
illustrate, a currently selected time frame can be seven dates
spanning from June 10 to June 16. Sliding the bar 1121 in a
left-hand direction causes the timeframe to increment back in time,
such as to June 3-June 9. Note that in this implementation, the
number of days in a timeframe remains constant unless the view size
1202 is modified. Thus, the number of days in the timeframe remains
the same as the slider is dragged.
[0328] Date slider control 1136 includes day earlier button 1214
and day later button 1216, which increments the timeframe one day
forward or one day later, respectively, when selected.
[0329] Date slider control 1136 can also allow a user to increment
n number of days forward or backward by selecting portions 1218 and
1220 of slider control 1136 located before and after the slider bar
1212, respectively. The number n can correspond to the number of
days selected in size control button 1202. To illustrate, if the
number of days selected in the size control 1202 is seven days,
then selecting portion 1218 of slider control 1136 causes the
slider bar 1212 to slide toward the start date button 1204 and
cause the timeframe (both start and end date) to increment back in
time seven days.
[0330] In accordance with some embodiments, a user can use a device
(e.g., display device 14, 16, 18, 20) to implement one or more of
the pattern detection techniques described herein to identify and
display patterns useful for a user to monitor so to manage a
chronic disease, such as diabetes. For example, the pattern
analysis methods described herein can be tailored to detect
hyperglycemic and/or hypoglycemic patterns occurring at particular
times of the day or on particular days of the week. Patterns can be
scored or weighted to identify patterns based on frequency,
sequence and severity, including time spent in severe episodes.
Alerts can also be triggered based on previous patterns analysis
and current data indicating that the current data is following a
similar enough pattern as the past identified patterns. Devices
disclosed herein can also be used to input a comment regarding what
a user did in response to an alert triggered in response to
detection of a pattern.
[0331] While the disclosure has been illustrated and described in
detail in the drawings and foregoing description, such illustration
and description are to be considered illustrative or exemplary and
not restrictive. The disclosure is not limited to the disclosed
embodiments. Variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed disclosure, from a study of the drawings, the
disclosure and the appended claims.
[0332] All references cited herein are incorporated herein by
reference in their entirety. To the extent publications and patents
or patent applications incorporated by reference contradict the
disclosure contained in the specification, the specification is
intended to supersede and/or take precedence over any such
contradictory material.
[0333] Unless otherwise defined, all terms (including technical and
scientific terms) are to be given their ordinary and customary
meaning to a person of ordinary skill in the art, and are not to be
limited to a special or customized meaning unless expressly so
defined herein. It should be noted that the use of particular
terminology when describing certain features or aspects of the
disclosure should not be taken to imply that the terminology is
being re-defined herein to be restricted to include any specific
characteristics of the features or aspects of the disclosure with
which that terminology is associated. Terms and phrases used in
this application, and variations thereof, especially in the
appended claims, unless otherwise expressly stated, should be
construed as open ended as opposed to limiting. As examples of the
foregoing, the term `including` should be read to mean `including,
without limitation,` `including but not limited to,` or the like;
the term `comprising` as used herein is synonymous with
`including,` `containing,` or `characterized by,` and is inclusive
or open-ended and does not exclude additional, unrecited elements
or method steps; the term `having` should be interpreted as `having
at least;` the term `includes` should be interpreted as `includes
but is not limited to;` the term `example` is used to provide
exemplary instances of the item in discussion, not an exhaustive or
limiting list thereof; adjectives such as `known`, `normal`,
`standard`, and terms of similar meaning should not be construed as
limiting the item described to a given time period or to an item
available as of a given time, but instead should be read to
encompass known, normal, or standard technologies that may be
available or known now or at any time in the future; and use of
terms like `preferably,` `preferred,` `desired,` or `desirable,`
and words of similar meaning should not be understood as implying
that certain features are critical, essential, or even important to
the structure or function of the invention, but instead as merely
intended to highlight alternative or additional features that may
or may not be utilized in a particular embodiment of the invention.
Likewise, a group of items linked with the conjunction `and` should
not be read as requiring that each and every one of those items be
present in the grouping, but rather should be read as `and/of`
unless expressly stated otherwise. Similarly, a group of items
linked with the conjunction `or` should not be read as requiring
mutual exclusivity among that group, but rather should be read as
`and/of` unless expressly stated otherwise.
[0334] Where a range of values is provided, it is understood that
the upper and lower limit, and each intervening value between the
upper and lower limit of the range is encompassed within the
embodiments.
[0335] With respect to the use of substantially any plural and/or
singular terms herein, those having skill in the art can translate
from the plural to the singular and/or from the singular to the
plural as is appropriate to the context and/or application. The
various singular/plural permutations may be expressly set forth
herein for sake of clarity. The indefinite article "a" or "an" does
not exclude a plurality. A single processor or other unit may
fulfill the functions of several items recited in the claims. The
mere fact that certain measures are recited in mutually different
dependent claims does not indicate that a combination of these
measures cannot be used to advantage. Any reference signs in the
claims should not be construed as limiting the scope.
[0336] It will be further understood by those within the art that
if a specific number of an introduced claim recitation is intended,
such an intent will be explicitly recited in the claim, and in the
absence of such recitation no such intent is present. For example,
as an aid to understanding, the following appended claims may
contain usage of the introductory phrases "at least one" and "one
or more" to introduce claim recitations. However, the use of such
phrases should not be construed to imply that the introduction of a
claim recitation by the indefinite articles "a" or "an" limits any
particular claim containing such introduced claim recitation to
embodiments containing only one such recitation, even when the same
claim includes the introductory phrases "one or more" or "at least
one" and indefinite articles such as "a" or "an" (e.g., "a" and/or
"an" should typically be interpreted to mean "at least one" or "one
or more"); the same holds true for the use of definite articles
used to introduce claim recitations. In addition, even if a
specific number of an introduced claim recitation is explicitly
recited, those skilled in the art will recognize that such
recitation should typically be interpreted to mean at least the
recited number (e.g., the bare recitation of "two recitations,"
without other modifiers, typically means at least two recitations,
or two or more recitations). Furthermore, in those instances where
a convention analogous to "at least one of A, B, and C, etc." is
used, in general such a construction is intended in the sense one
having skill in the art would understand the convention (e.g., "a
system having at least one of A, B, and C" would include but not be
limited to systems that have A alone, B alone, C alone, A and B
together, A and C together, B and C together, and/or A, B, and C
together, etc.). In those instances where a convention analogous to
"at least one of A, B, or C, etc." is used, in general such a
construction is intended in the sense one having skill in the art
would understand the convention (e.g., "a system having at least
one of A, B, or C" would include but not be limited to systems that
have A alone, B alone, C alone, A and B together, A and C together,
B and C together, and/or A, B, and C together, etc.). It will be
further understood by those within the art that virtually any
disjunctive word and/or phrase presenting two or more alternative
terms, whether in the description, claims, or drawings, should be
understood to contemplate the possibilities of including one of the
terms, either of the terms, or both terms. For example, the phrase
"A or B" will be understood to include the possibilities of "A" or
"B" or "A and B."
[0337] All numbers expressing quantities of ingredients, reaction
conditions, and so forth used in the specification are to be
understood as being modified in all instances by the term `about.`
Accordingly, unless indicated to the contrary, the numerical
parameters set forth herein are approximations that may vary
depending upon the desired properties sought to be obtained. At the
very least, and not as an attempt to limit the application of the
doctrine of equivalents to the scope of any claims in any
application claiming priority to the present application, each
numerical parameter should be construed in light of the number of
significant digits and ordinary rounding approaches.
[0338] It will be appreciated that, for clarity purposes, the above
description has described embodiments with reference to different
functional units. However, it will be apparent that any suitable
distribution of functionality between different functional units
may be used without detracting from the invention. For example,
functionality illustrated to be performed by separate computing
devices may be performed by the same computing device. Likewise,
functionality illustrated to be performed by a single computing
device may be distributed amongst several computing devices. Hence,
references to specific functional units are only to be seen as
references to suitable means for providing the described
functionality, rather than indicative of a strict logical or
physical structure or organization.
[0339] Furthermore, although the foregoing has been described in
some detail by way of illustrations and examples for purposes of
clarity and understanding, it is apparent to those skilled in the
art that certain changes and modifications may be practiced.
Therefore, the description and examples should not be construed as
limiting the scope of the invention to the specific embodiments and
examples described herein, but rather to also cover all
modification and alternatives coming with the true scope and spirit
of the invention.
* * * * *