U.S. patent application number 11/081857 was filed with the patent office on 2005-10-20 for collecting activity and sleep quality information via a medical device.
Invention is credited to Heruth, Kenneth T., Miesel, Keith A..
Application Number | 20050234518 11/081857 |
Document ID | / |
Family ID | 34963063 |
Filed Date | 2005-10-20 |
United States Patent
Application |
20050234518 |
Kind Code |
A1 |
Heruth, Kenneth T. ; et
al. |
October 20, 2005 |
Collecting activity and sleep quality information via a medical
device
Abstract
A device, such as an implantable medical device (IMD),
programming device, or other computing device determines when a
patient is attempting to sleep. When the device determines that the
patient is attempting to sleep, the device determines values for
one or more metrics that indicate the quality of a patient's sleep
based on at least one physiological parameter of the patient. When
the device determines that the patient is not attempting to sleep,
the device periodically determines activity levels of the patient.
Activity metric values may be determined based on the determined
activity levels. A clinician may use sleep quality information and
patient activity information presented by a programming device to,
for example, evaluate the effectiveness of therapy delivered to the
patient by a medical device.
Inventors: |
Heruth, Kenneth T.; (Edina,
MN) ; Miesel, Keith A.; (St. Paul, MN) |
Correspondence
Address: |
SHUMAKER & SIEFFERT, P. A.
8425 SEASONS PARKWAY
SUITE 105
ST. PAUL
MN
55125
US
|
Family ID: |
34963063 |
Appl. No.: |
11/081857 |
Filed: |
March 16, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11081857 |
Mar 16, 2005 |
|
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|
10825955 |
Apr 15, 2004 |
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60553785 |
Mar 16, 2004 |
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Current U.S.
Class: |
607/6 ; 600/301;
600/509; 600/544 |
Current CPC
Class: |
A61N 1/36135 20130101;
A61B 5/6826 20130101; A61B 5/4815 20130101; A61B 5/1118 20130101;
A61B 5/4519 20130101; A61B 5/686 20130101; A61N 1/36542 20130101;
A61B 5/1116 20130101 |
Class at
Publication: |
607/006 ;
600/301; 600/509; 600/544 |
International
Class: |
A61N 001/18; A61N
001/24; A61N 001/36; A61N 001/38 |
Claims
1. A method comprising: monitoring a plurality of physiological
parameters of a patient, wherein the plurality of physiological
parameters includes at least one parameter indicative of patient
physical activity; determining when the patient is attempting to
sleep; determining values of at least one metric that is indicative
of sleep quality based on at least one of the physiological
parameters and a determination that the patient is attempting to
sleep; and periodically determining an activity level of the
patient based on at least one of the physiological parameters and a
determination that the patient is not attempting to sleep, wherein
monitoring a plurality of physiological parameters comprises
monitoring at least one of electrocardiogram morphology,
subcutaneous temperature, muscular tone, brain electrical activity,
or eye motion.
2. A medical system comprising: a device that monitors a plurality
of physiological parameters of a patient, wherein the plurality of
physiological parameters includes at least one physiological
parameter indicative of patient physical activity; and a processor
that determines when the patient is attempting to sleep, determines
values of at least one metric that is indicative of sleep quality
based on at least one of the physiological parameters and a
determination that the patient is attempting to sleep, and
periodically determines an activity level of the patient based on
at least one of the physiological parameters and a determination
that the patient is not attempting to sleep.
3. The medical system of claim 2, wherein the device monitors at
least one of electrocardiogram morphology, subcutaneous
temperature, muscular tone, brain electrical activity, or eye
motion.
4. The medical system of claim 2, wherein the device that monitors
a plurality of physiological parameters of a patient comprises a
first device, the system further comprising a second device that
delivers a therapy to the patient according to a plurality of
therapy parameter sets, wherein the processor associates each of
the determined sleep quality metric value and each of the
determined activity levels with a current therapy parameter set,
wherein, for each of the plurality of therapy parameter sets, the
processor determines a representative value of each of the at least
one sleep quality metric based on the sleep quality metric values
associated with the therapy parameter set, and wherein, for each of
the plurality of therapy parameter sets, the processor determines
at least one activity metric value based on the activity levels
associated with the therapy parameter set.
5. The medical system of claim 4, further comprising a computing
device including a display that presents a list of the therapy
parameter sets, associated representative sleep quality metric
values, and associated activity metric values.
6. The medical system of claim 5, wherein the computing device
receives user selection of one of the sleep quality metrics and
activity metric, and orders the list of therapy parameter sets
according to values of the user selected one of the sleep quality
metrics and activity metrics.
7. The medical system of claim 4, wherein the first and second
device comprise a single device.
8. The medical system of claim 2, wherein the processor comprises a
processor of the device that monitors a plurality of physiological
parameters of the patient.
9. The medical system of claim 2, further comprising a computing
device, wherein the processor comprises a processor of the
computing device.
10. The medical system of claim 2, wherein the device that monitors
a plurality of physiological parameters of the patient comprises an
implantable medical device.
11. The medical system of claim 10, wherein the implantable medical
device comprises at least one of an implantable neurostimulator and
an implantable drug pump.
12. The medical system of claim 2, wherein the device that monitors
a plurality of physiological parameters of the patient comprises at
least one of a trial neurostimulator and a trial pump.
13. A medical system comprising: means for monitoring a plurality
of physiological parameters of a patient, wherein the plurality of
physiological parameters includes at least one parameter indicative
of patient physical activity; means for determining when the
patient is attempting to sleep; means for determining values of at
least one metric that is indicative of sleep quality based on at
least one of the physiological parameters and a determination that
the patient is attempting to sleep; and means for periodically
determining an activity level of the patient based on at least one
of the physiological parameters and a determination that the
patient is not attempting to sleep wherein means for monitoring a
plurality of physiological parameters comprises means for
monitoring at least one of electrocardiogram morphology,
subcutaneous temperature, muscular tone, brain electrical activity,
or eye motion.
14. A medical system comprising: an implantable medical device that
delivers a therapy to a patient based on a plurality of therapy
parameter sets, monitors a plurality of physiological parameters of
the patient including at least one parameter indicative of patient
physical activity, determines when the patient is attempting to
sleep, determines values of at least one metric that is indicative
of sleep quality based on at least one of the physiological
parameters and a determination that the patient is attempting to
sleep, periodically determines an activity level of the patient
based on at least one of the physiological parameters and a
determination that the patient is not attempting to sleep,
associates each determined sleep quality metric value and each
determined activity level with a current therapy parameter set,
determines a representative value of each of the at least one sleep
quality metrics for each of the plurality of therapy parameter sets
based on the sleep quality metric values associated with the
therapy parameter set, and determines at least one activity metric
value for each of the plurality of therapy parameter sets based on
the activity levels associated with the therapy parameter set; and
an external programming device including a display that receives
information identifying the plurality of therapy parameter sets and
the sleep quality metric values and activity metric values
associated with the therapy parameter sets from the implantable
medical device, and presents a list of the therapy parameter sets
and the associated sleep quality metric values and activity metric
values to a user, wherein the implantable medical device monitors
at least one of electrocardiogram morphology, subcutaneous
temperature, muscular tone, brain electrical activity, or eye
motion.
15. A computer-readable medium comprising instructions that cause a
programmable processor to: monitor a plurality of physiological
parameters of a patient, wherein the plurality of physiological
parameters includes at least one parameter indicative of patient
physical activity; determine when the patient is attempting to
sleep; determine values of at least one metric that is indicative
of sleep quality based on at least one of the physiological
parameters and a determination that the patient is attempting to
sleep; and periodically determine an activity level of the patient
based on at least one of the physiological parameters and a
determination that the patient is not attempting to sleep wherein
the instructions that cause a programmable processor to monitor a
plurality of physiological parameters of a patient comprise
instructions that cause the programmable processor to monitor at
least one of electrocardiogram morphology, subcutaneous
temperature, muscular tone, brain electrical activity, or eye
motion.
Description
[0001] This application is a continuation-in-part of U.S. patent
application Ser. No. 10/825,955, filed Apr. 15, 2004, which claims
the benefit of U.S. Provisional Application No. 60/553,785, filed
Mar. 16, 2004. The entire content of both applications is
incorporated herein by reference.
TECHNICAL FIELD
[0002] The invention relates to medical devices and, more
particularly, to medical devices that monitor physiological
parameters.
BACKGROUND
[0003] In some cases, an ailment may affect the quality of a
patient's sleep and/or affect the patient's activity level. For
example, chronic pain may cause a patient to have difficulty
falling asleep, disturb the patient's sleep, e.g., cause the
patient to wake, and prevent the patient from achieving deeper
sleep states, such as one or more of the nonrapid eye movement
(NREM) sleep states. Chronic pain may also cause a patient to avoid
particular activities, or activity in general, where such
activities increase the pain experienced by the patient. Other
ailments that may negatively affect patient sleep quality and
patient activity level include movement disorders, and congestive
heart failure. In some cases, these ailments are treated via an
implantable medical device (IMD), such as an implantable stimulator
or drug delivery device.
[0004] Further, in some cases, poor sleep quality may increase the
symptoms experienced by a patient due to an ailment. For example,
poor sleep quality has been linked to increased pain symptoms in
chronic pain patients. The link between poor sleep quality and
increased symptoms is not limited to ailments that negatively
impact sleep quality, such as those listed above. Nonetheless, the
condition of a patient with such an ailment may progressively
worsen when symptoms disturb sleep quality, which in turn increases
the frequency and/or intensity of symptoms. The increased symptoms
may, in turn, limit patient activity during the day, and further
disturb sleep quality.
SUMMARY
[0005] In general, the invention is directed to techniques for
collecting information that relates to patient activity and the
quality of patient sleep via a medical device, such as an
implantable medical device (IMD). The medical device, or another
device, determines whether to collect activity or sleep quality
information by determining whether the patient is attempting to
sleep. Activity and sleep quality information collected by the
device may be presented to a user, such as a clinician, and used
to, for example, evaluate the effectiveness of a therapy delivered
to the patient by the medical device. For example, the activity and
sleep quality information may be associated with different therapy
parameter sets used by the medical device to deliver therapy to the
patient, permitting a user to evaluate relative efficacy of the
therapy parameter sets.
[0006] The device may determine that the patient is attempting to
sleep in a variety of ways. For example, the device may receive an
indication from the patient that the patient is trying to fall
asleep, e.g., via a patient programming device in embodiments in
which the medical device determines whether the patient is
attempting to sleep and is an implantable medical device. In other
embodiments, the device may monitor the activity level of the
patient, and identify the time that the patient is attempting to
sleep by determining whether the patient has remained inactive for
a threshold period of time and identifying the time at which the
patient became inactive. In still other embodiments, the device may
monitor patient posture, and identify the time when the patient is
recumbent, e.g., lying down, as the time when the patient is
attempting to fall asleep. In these embodiments, the device may
also monitor patient activity, and confirm that the patient is
attempting to sleep based on the patient's activity level.
[0007] As another example, the device may determine the time at
which the patient begins attempting to fall asleep based on the
level of melatonin within one or more bodily fluids, such as the
patient's blood, cerebrospinal fluid (CSF), or interstitial fluid.
The device may also determine a melatonin level based on
metabolites of melatonin located in the saliva or urine of the
patient. Melatonin is a hormone secreted by the pineal gland into
the bloodstream and the CSF as a function of exposure of the optic
nerve to light, which synchronizes the patient's circadian rhythm.
In particular, increased levels of melatonin during evening hours
may cause physiological changes in the patient, which, in turn, may
cause the patient to attempt to fall asleep. The device may, for
example, detect an increase in the level of melatonin, and estimate
the time that the patient will attempt to fall asleep based on the
detection.
[0008] When the device determines that the patient is attempting to
sleep, the device may determine values for one or more metrics that
indicate the quality of a patient's sleep based on at least one
monitored physiological parameter of the patient. Example
physiological parameters that the device may monitor to determine
sleep quality metric values include activity level, posture, heart
rate, electrocardiogram (ECG) morphology, respiration rate,
respiratory volume, blood pressure, blood oxygen saturation,
partial pressure of oxygen within blood, partial pressure of oxygen
within cerebrospinal fluid, muscular activity and tone, core
temperature, subcutaneous temperature, arterial blood flow, brain
electrical activity, eye motion, and galvanic skin response. In
order to monitor one or more of these parameters, the device may
include, or be coupled to, one or more sensors, each of which
generates a signal as a function of one or more of these
physiological parameters. The device may determine a value of one
or more sleep quality metrics based on the monitored physiological
parameters, and/or the variability of one or more of the monitored
physiological parameters.
[0009] Sleep efficiency and sleep latency are example sleep quality
metrics for which a device may determine values. Sleep efficiency
may be measured as the percentage of time while the patient is
attempting to sleep that the patient is actually asleep, or
actually within one of the different sleep states. Sleep latency
may be measured as the amount of time between a first time when the
patient begins attempting to fall asleep and a second time when the
patient falls asleep, and thereby indicates how long a patient
requires to fall asleep.
[0010] The time when the patient begins attempting to fall asleep
may be determined in any of the variety of ways identified above.
The time at which the patient has fallen asleep may be determined
based on any one or more of the other physiological parameters that
may be monitored by the medical device as indicated above. For
example, a discemable change, e.g., a decrease, in one or more
physiological parameters, or the variability of one or more
physiological parameters, may indicate that the patient has fallen
asleep. In some embodiments, the device determines a sleep
probability metric value based on a value of a physiological
parameter. In such embodiments, the device compares the sleep
probability metric value to a threshold to identify when the
patient has fallen asleep. In some embodiments, the medical device
determines a plurality of sleep probability metric values based on
a value of each of a plurality of physiological parameters,
averages or otherwise combines the plurality of sleep probability
metric values to provide an overall sleep probability metric value,
and compares the overall sleep probability metric value to a
threshold to identify the time that the patient falls asleep.
[0011] Other sleep quality metrics that the device may determine
include total time sleeping per day, the amount or percentage of
time sleeping during nighttime or daytime hours per day, and the
number of apnea and/or arousal events per night. In some
embodiments, the device may determine which sleep state the patient
is in, e.g., rapid eye movement (REM), or one of the nonrapid eye
movement (NREM) states (S1, S2, S3, S4) based on monitored
physiological parameters, and the amount of time per day spent in
these various sleep states may be determined by the medical device
as a sleep quality metric. Because they provide the most
"refreshing" type of sleep, the amount of time spent in one or both
of the S3 and S4 sleep states, in particular, may be determined as
a sleep quality metric. In some embodiments, the device may
determine average or median values of one or more sleep quality
metrics over greater periods of time, e.g., a week or a month, as
the value of the sleep quality metric. Further, in embodiments in
which values for a plurality of the sleep quality metrics are
determined, the device may determine a value for an overall sleep
quality metric based on the values for the plurality of individual
sleep quality metrics.
[0012] When the device determines that the patient is not
attempting to sleep, the device periodically determines activity
levels of the patient. For example, the device may monitor a signal
generated by an accelerometer, a bonded piezoelectric crystal, a
mercury switch, or a gyro. In some embodiments, the device may
monitor a signal that indicates a physiological parameter of the
patient, which in turn varies as a function of patient activity.
For example, the device may monitor a signal that indicates the
heart rate, ECG morphology, respiration rate, respiratory volume,
core temperature, subcutaneous temperature, or muscular activity
level of the patient.
[0013] The device may periodically determine an activity level of
the patient based on the one or more signals. In some embodiments,
the device periodically determines a number of activity counts
based on the one or more signals, and the number of activity counts
is stored as the activity level. The number of activity counts may
be a number of threshold crossings by a signal generated by an
accelerometer or piezoelectric crystal during a sample period, or a
number of switch contacts indicated by the signal generated by a
mercury switch during a sample period.
[0014] In some embodiments, the device may periodically determine a
heart rate, value of an ECG morphological feature, respiration
rate, respiratory volume, and/or muscular activity level of the
patient based on one or more signals. The determined values of
these parameters may be mean or median values. The device may
compare a determined value of such a physiological parameter to one
or more thresholds to determine a number of activity counts, which
may be stored as a determined activity level. In other embodiments,
the device may store the determined physiological parameter value
as a determined activity level.
[0015] The use of activity counts, however, may allow the device to
determine an activity level based on a plurality of signals. For
example, the device may determine a first number of activity counts
based on a sample of an accelerometer signal and a second number of
activity counts based on a heart rate determined at the time the
accelerometer signal was sampled. The device may determine an
activity level by calculating the sum or average, which may be a
weighted sum or average, of first and second activity counts.
[0016] The device may determine a value of one or more activity
metrics based on determined activity levels. An activity metric
value may be, for example, a mean or median activity level, such as
an average number of activity counts per unit time. In other
embodiments, an activity metric value may be chosen from a
predetermined scale of activity metric values based on comparison
of a mean or median activity level to one or more threshold values.
The scale may be numeric, such as activity metric values from 1-10,
or qualitative, such as low, medium or high activity.
[0017] In some embodiments, a number of collected activity levels
are compared with one or more thresholds, and percentages of time
above and/or below the thresholds are determined as one or more
activity metric values. In other embodiments, a number of collected
activity levels are compared with one or more thresholds, and an
average length of time that consecutively determined activity
levels remain above the threshold is determined as an activity
metric value.
[0018] In some embodiments, the device that collects sleep quality
and activity information is a medical device delivers a therapy to
the patient. At any given time, the medical device delivers the
therapy according to a current set of therapy parameters. For
example, in embodiments in which the medical device is a
neurostimulator, a therapy parameter set may include a pulse
amplitude, a pulse width, a pulse rate, a duty cycle, and an
indication of active electrodes. Different therapy parameter sets
may be selected, e.g., by the patient via a programming device or a
the medical device according to a schedule, and parameters of one
or more therapy parameter sets may be adjusted by the patient to
create new therapy parameter sets. In other words, over time, the
medical device delivers the therapy according to a plurality of
therapy parameter sets.
[0019] When the medical device determines a sleep quality metric
value or an activity level, the medical device may identify the
current therapy parameter set when the value or level is
determined, and may associate that value or level with the therapy
parameter set. For each available therapy parameter set, the
medical device may store a representative value of each of one or
more sleep quality metrics in a memory with an indication of the
therapy parameter set with which that representative value is
associated. A representative value of sleep quality metric for a
therapy parameter set may be the mean or median of collected sleep
quality metric values that have been associated with that therapy
parameter set. For each available therapy parameter set, the
medical device may also store one or more associated activity
metric values that are determined based on activity levels
associated with that therapy parameter set.
[0020] A programming device according to the invention may be
capable of wireless communication with the medical device, and may
receive from the medical device information identifying the therapy
parameter set, representative sleep quality metric values
associated with the plurality of therapy parameter sets, and
activity metric values associated with the therapy parameter sets.
The programming device may display a list of the therapy parameter
sets, which may be ordered according to any of the associated
representative sleep quality metric values or activity metric
values. A user may select the metric by which the list is ordered.
Such a list may be used by a clinician to, for example, identify
effective or ineffective therapy parameter sets.
[0021] In some embodiments, the medical device does not determine
whether the patient is attempting to sleep, determine values for
sleep quality metrics, determine activity metric values, and/or
periodically determine activity levels. Instead, in some
embodiments, a computing device, such as a programming device
performs one or more of these functions. For example, a programming
device may be used to program a medical device, and also receive
physiological parameter values, activity levels, and/or samples of
an activity signal from the medical device, and determine activity
metric values and sleep quality metric values based on the
information received from the medical device using any of the
techniques described herein with reference to a medical device.
[0022] In some embodiments, the medical device may associate
recorded physiological parameter values, signal samples, and/or
activity levels with a current therapy parameter set, and may
provide information identifying a plurality of therapy parameter
sets and collected information associated with the therapy
parameter sets to a programming device or other computing device.
In such embodiments, the programming device may determine
representative sleep quality metric values and activity metric
values associated with the various therapy parameter sets using any
of techniques described herein with reference to a medical device.
The programming device may receive such information from the
medical device in real time, or may interrogate the medical device
for information recorded by the medical device over a period of
time.
[0023] In other embodiments, a system according to the invention
does not include a programming device or other computing device.
For example, an external medical device according to the invention
may include a display, collect sleep quality and activity
information as described herein, and display sleep quality and
activity information to a user via the display.
[0024] In one embodiment, the invention is directed to a method in
which a plurality of physiological parameters of a patient are
monitored. The plurality of physiological parameters includes at
least one physiological parameter indicative of patient physical
activity. The method includes a determination of when the patient
is attempting to sleep. Values of at least one metric that is
indicative of sleep quality are determined based on at least one of
the physiological parameters and a determination that the patient
is attempting to sleep. An activity level of the patient is
periodically determined based on at least one of the physiological
parameters and a determination that the patient is not attempting
to sleep.
[0025] In another embodiment, the invention is directed to a
medical system including a device and a processor. The device
monitors a plurality of physiological parameters of a patient, and
the plurality of physiological parameters includes at least one
physiological parameter indicative of patient physical activity.
The processor determines when the patient is attempting to sleep,
determines values of at least one metric that is indicative of
sleep quality based on at least one of the physiological parameters
and a determination that the patient is attempting to sleep, and
periodically determines an activity level of the patient based on
at least one of the physiological parameters and a determination
that the patient is not attempting to sleep.
[0026] In another embodiment, the invention is directed to a
medical system including means for monitoring a plurality of
physiological parameters of a patient, wherein the plurality of
physiological parameters includes at least one physiological
parameter indicative of patient physical activity, means for
determining when the patient is attempting to sleep, means for
determining values of at least one metric that is indicative of
sleep quality based on at least one of the physiological parameters
and a determination that the patient is attempting to sleep, and
means for periodically determining an activity level of the patient
based on at least one of the physiological parameters and a
determination that the patient is not attempting to sleep.
[0027] In another embodiment, the invention is directed to a
medical system including an implantable medical device and an
external programming device that includes a display. The
implantable medical device delivers a therapy to a patient based on
a plurality of therapy parameter sets, monitors a plurality of
physiological parameters, of the patient including at least one
physiological parameter indicative of patient physical activity,
determines when the patient is attempting to sleep, determines
values of at least one metric that is indicative of sleep quality
based on at least one of the physiological parameters and a
determination that the patient is attempting to sleep, periodically
determines an activity level of the patient based on at least one
of the physiological parameters and a determination that the
patient is not attempting to sleep, associates each determined
sleep quality metric value and each determined activity level with
a current therapy parameter set, determines a representative value
of each of the at least one sleep quality metrics for each of the
plurality of therapy parameter sets based on the sleep quality
metric values associated with the therapy parameter set, and
determines at least one activity metric value for each of the
plurality of therapy parameter sets based on the activity levels
associated with the therapy parameter set. The external programming
device receives information identifying the plurality of therapy
parameter sets and the sleep quality metric values and activity
metric values associated with the therapy parameter sets from the
implantable medical device, and presents a list of the therapy
parameter sets and the associated sleep quality metric values to a
user.
[0028] In another embodiment, the invention is directed to a
computer-readable medium comprising program instructions. The
program instructions cause a programmable processor to monitor a
plurality of physiological parameters of a patient, wherein the
plurality of physiological parameters includes at least one
physiological parameter indicative of patient physical activity,
determine when the patient is attempting to sleep, determine values
of at least one metric that is indicative of sleep quality based on
at least one of the physiological parameters and a determination
that the patient is attempting to sleep, and periodically determine
an activity level of the patient based on at least one of the
physiological parameters and a determination that the patient is
not attempting to sleep.
[0029] The invention may be capable of providing one or more
advantages. For example, by providing information related to
patient activity and the quality of a patient's sleep to a
clinician and/or the patient, a system according to the invention
can improve the course of treatment of an ailment of the patient,
such as chronic pain. For example, using activity and sleep quality
information provided by the system, the clinician could evaluate a
plurality of therapy parameter sets to identify those which are, or
are not, efficacious.
[0030] The details of one or more embodiments of the invention are
set forth in the accompanying drawings and the description below.
Other features, objects, and advantages of the invention will be
apparent from the description and drawings, and from the
claims.
BRIEF DESCRIPTION OF DRAWINGS
[0031] FIG. 1 is a conceptual diagram illustrating an example
system that includes an implantable medical device that collects
sleep quality information and activity information according to the
invention.
[0032] FIG. 2 is a block diagram further illustrating the example
system and implantable medical device of FIG. 1.
[0033] FIG. 3 is a block diagram illustrating an example memory of
the implantable medical device of FIG. 1.
[0034] FIG. 4 is a flow diagram illustrating an example method for
collecting sleep quality information and activity information that
may be employed by an implantable medical device.
[0035] FIG. 5 is a flow diagram illustrating an example method for
collecting sleep quality information that may be employed by an
implantable medical device.
[0036] FIG. 6 is a flow diagram illustrating an example method for
collecting activity information that may be employed by an
implantable medical device.
[0037] FIG. 7 is a flow diagram illustrating an example method for
associating sleep quality information and activity information with
therapy parameter sets that may be employed by an implantable
medical device.
[0038] FIG. 8 is a block diagram illustrating an example clinician
programmer.
[0039] FIG. 9 illustrates an example list of therapy parameter sets
and associated sleep quality information and activity information
that may be presented by a clinician programmer.
[0040] FIG. 10 is a flow diagram illustrating an example method for
displaying a list of therapy parameter sets and associated sleep
quality information and activity information that may be employed
by a clinician programmer.
[0041] FIG. 11 is a conceptual diagram illustrating a monitor that
monitors values of one or more physiological parameters of the
patient.
DETAILED DESCRIPTION
[0042] FIG. 1 is a conceptual diagram illustrating an example
system 10 that includes an implantable medical device (IMD) 14 that
collects information relating to the quality of sleep experienced
by a patient 12 and the activity of patient 12 according to the
invention. Sleep quality information and activity information
collected by IMD 14 is provided to a user, such as a clinician or
the patient. Using the sleep quality information and activity
information collected by IMD 14, a current course of therapy for an
ailment of patient 12 may be evaluated, and an improved course of
therapy for the ailment may be identified.
[0043] In the illustrated example system 10, IMD 14 takes the form
of an implantable neurostimulator that delivers neurostimulation
therapy in the form of electrical pulses to patient 12. However,
the invention is not limited to implementation via an implantable
neurostimulator. For example, in some embodiments of the invention,
an implantable pump or implantable cardiac rhythm management
device, such as a pacemaker may collect sleep quality information
and activity information. Further, the invention is not limited to
implementation via an IMD. In other words, any implantable or
external medical device may collect sleep quality and activity
information according to the invention.
[0044] In the example of FIG. 1, IMD 14 delivers neurostimulation
therapy to patient 12 via leads 16A and 16B (collectively "leads
16"). Leads 16 may, as shown in FIG. 1, be implanted proximate to
the spinal cord 18 of patient 12, and IMD 14 may deliver spinal
cord stimulation (SCS) therapy to patient 12 in order to, for
example, reduce pain experienced by patient 12. However, the
invention is not limited to the configuration of leads 16 shown in
FIG. 1 or the delivery of SCS therapy. For example, one or more
leads 16 may extend from IMD 14 to the brain (not shown) of patient
12, and IMD 14 may deliver deep brain stimulation (DBS) therapy to
patient 12 to, for example, treat tremor or epilepsy. As further
examples, one or more leads 16 may be implanted proximate to the
pelvic nerves (not shown) or stomach (not shown), and IMD 14 may
deliver neurostimulation therapy to treat incontinence, sexual
dysfunction or gastroparesis.
[0045] IMD 14 delivers therapy according to a set of therapy
parameters, i.e., a set of values for a number of parameters that
define the therapy delivered according to that therapy parameter
set. In embodiments where IMD 14 delivers neurostimulation therapy
in the form of electrical pulses, the parameters for each therapy
parameter set may include voltage or current pulse amplitudes,
pulse widths, pulse rates, and the like. Further, each of leads 16
includes electrodes (not shown in FIG. 1), and a therapy parameter
set may include information identifying which electrodes have been
selected for delivery of pulses, and the polarities of the selected
electrodes. Therapy parameter sets used by IMD 14 may include a
number of parameter sets programmed by a clinician (not shown), and
parameter sets representing adjustments made by patient 12 to these
preprogrammed sets.
[0046] System 10 also includes a clinician programmer 20. A
clinician (not shown) may use clinician programmer 20 to program
therapy for patient 12, e.g., specify a number of therapy parameter
sets and provide the parameter sets to IMD 14. The clinician may
also use clinician programmer 20 to retrieve information collected
by IMD 14. The clinician may use clinician programmer 20 to
communicate with IMD 14 both during initial programming of IMD 14,
and for collection of information and further programming during
follow-up visits.
[0047] Clinician programmer 20 may, as shown in FIG. 1, be a
handheld computing device. Clinician programmer 20 includes a
display 22, such as a LCD or LED display, to display information to
a user. Clinician programmer 20 may also include a keypad 24, which
may be used by a user to interact with clinician programmer 20. In
some embodiments, display 22 may be a touch screen display, and a
user may interact with clinician programmer 20 via display 22. A
user may also interact with clinician programmer 20 using
peripheral pointing devices, such as a stylus or mouse. Keypad 24
may take the form of an alphanumeric keypad or a reduced set of
keys associated with particular functions.
[0048] System 10 also includes a patient programmer 26, which also
may, as shown in FIG. 1, be a handheld computing device. Patient 12
may use patient programmer 26 to control the delivery of therapy by
IMD 14. For example, using patient programmer 26, patient 12 may
select a current therapy parameter set from among the therapy
parameter sets preprogrammed by the clinician, or may adjust one or
more parameters of a preprogrammed therapy parameter set to arrive
at the current therapy parameter set.
[0049] Patient programmer 26 may include a display 28 and a keypad
30, to allow patient 12 to interact with patient programmer 26. In
some embodiments, display 28 may be a touch screen display, and
patient 12 may interact with patient programmer 26 via display 28.
Patient 12 may also interact with patient programmer 26 using
peripheral pointing devices, such as a stylus, mouse, or the
like.
[0050] However, clinician and patient programmers 20, 26 are not
limited to the hand-held computer embodiments illustrated in FIG.
1. Programmers 20, 26 according to the invention may be any sort of
computing device. For example, a programmer 20, 26 according to the
invention may be a tablet-based computing device, a desktop
computing device, or a workstation.
[0051] IMD 14, clinician programmer 20 and patient programmer 26
may, as shown in FIG. 1, communicate via wireless communication.
Clinician programmer 20 and patient programmer 26 may, for example,
communicate via wireless communication with IMD 14 using radio
frequency (RF) or infrared telemetry techniques known in the art.
Clinician programmer 20 and patient programmer 26 may communicate
with each other using any of a variety of local wireless
communication techniques, such as RF communication according to the
802.11 or Bluetooth specification sets, infrared communication
according to the IRDA specification set, or other standard or
proprietary telemetry protocols.
[0052] Clinician programmer 20 and patient programmer 26 need not
communicate wirelessly, however. For example, programmers 20 and 26
may communicate via a wired connection, such as via a serial
communication cable, or via exchange of removable media, such as
magnetic or optical disks, or memory cards or sticks. Further,
clinician programmer 20 may communicate with one or both of IMD 14
and patient programmer 26 via remote telemetry techniques known in
the art, communicating via a local area network (LAN), wide area
network (WAN), public switched telephone network (PSTN), or
cellular telephone network, for example.
[0053] As mentioned above, IMD 14 collects information that relates
to the quality of sleep experienced by patient 12 and the activity
of patient 12. In particular, as will be described in greater
detail below, IMD 14 determines whether patient 12 is attempting to
sleep, determines values for one or more sleep quality metrics when
patient 12 is attempting to sleep, and periodically determines
activity levels of patient 12 when patient 12 is not attempting to
sleep, i.e., is more likely to be active. In some embodiments, IMD
14 determines values for one or more activity metrics based on the
determined activity levels. IMD 14 may include or be coupled to one
or more sensors (not shown in FIG. 1), each of which generates a
signal as a function of one or more of these physiological
parameters, and may determine sleep quality metrics and activity
levels based on the signals output by the sensors.
[0054] At any given time, as indicated above, IMD 14 delivers the
therapy according to a current set of therapy parameters. Different
therapy parameter sets may be selected, e.g., by patient 12 via
patient programmer 26 or IMD 14 according to a schedule, and
parameters of one or more therapy parameter sets may be adjusted by
patient 12 via patient programmer 26 to create new therapy
parameter sets. In other words, over time, IMD 14 delivers the
therapy according to a plurality of therapy parameter sets.
[0055] In some embodiments, as will be described in greater detail
below, IMD 14 identifies the therapy parameter set currently used
to deliver therapy to patient 12 when a value of a sleep quality
metric or an activity level is determined, and may associate the
determined values and levels with current therapy parameter sets.
For each of the plurality of therapy parameter sets, IMD 14 may
store a representative value of each of one or more sleep quality
metrics in a memory with an indication of the therapy parameter set
with which that representative value is associated. A
representative value of a sleep quality metric for a therapy
parameter set may be the mean or median of collected sleep quality
metric values that have been associated with that therapy parameter
set. For each available therapy parameter set, IMD 14 may also
store one or more associated activity metric values that are
determined based on activity levels associated with that therapy
parameter set.
[0056] A programming or other computing device, such as clinician
programmer 20, may receive information identifying the therapy
parameter set, representative sleep quality metric values
associated with the plurality of therapy parameter sets, and
activity metric values associated with the therapy parameter sets
from IMD 14. Clinician programmer 20 may display a list of the
therapy parameter sets, which may be ordered according to any of
the associated representative sleep quality metric values or
activity metric values. A clinician may select the metric by which
the list is ordered. Such a list may be used by the clinician to,
for example, identify effective or ineffective therapy parameter
sets.
[0057] FIG. 2 is a block diagram further illustrating system 10. In
particular, FIG. 2 illustrates an example configuration of IMD 14
and leads 16A and 16B. FIG. 2 also illustrates sensors 40A and 40B
(collectively "sensors 40") that generate signals as a function of
one or more physiological parameters of patient 12. As will be
described in greater detail below, IMD 14 monitors at least some of
the signals to determine values for one or more metrics that are
indicative of sleep quality when the patient is attempting to
sleep, and monitors at least some of the signals to determine
activity levels of patient 12 when the patient is not attempting to
sleep.
[0058] IMD 14 may deliver neurostimulation therapy via electrodes
42A-D of lead 16A and electrodes 42E-H of lead 16B (collectively
"electrodes 42"). Electrodes 42 may be ring electrodes. The
configuration, type and number of electrodes 42 illustrated in FIG.
2 are merely exemplary. For example, leads 16A and 16B may each
include eight electrodes 42, and the electrodes 42 need not be
arranged linearly on each of leads 16A and 16B.
[0059] Electrodes 42 are electrically coupled to a therapy delivery
module 44 via leads 16A and 16B. Therapy delivery module 44 may,
for example, include an output pulse generator coupled to a power
source such as a battery. Therapy delivery module 44 may deliver
electrical pulses to patient 12 via at least some of electrodes 42
under the control of a processor 46, which controls therapy
delivery module 44 to deliver neurostimulation therapy according to
to a current therapy parameter set. However, the invention is not
limited to implantable neurostimulator embodiments or even to IMDs
that deliver electrical stimulation. For example, in some
embodiments, a therapy delivery module 44 of an IMD may include a
pump, circuitry to control the pump, and a reservoir to store a
therapeutic agent for delivery via the pump.
[0060] Processor 46 may include a microprocessor, a controller, a
digital signal processor (DSP), an application specific integrated
circuit (ASIC), a field-programmable gate array (FPGA), discrete
logic circuitry, or the like. Memory 48 may include any volatile,
non-volatile, magnetic, optical, or electrical media, such as a
random access memory (RAM), read-only memory (ROM), non-volatile
RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash
memory, and the like. In some embodiments, memory 48 stores program
instructions that, when executed by processor 46, cause IMD 14 and
processor 46 to perform the functions attributed to them
herein.
[0061] Each of sensors 40 generates a signal as a function of one
or more physiological parameters of patient 12. IMD 14 may include
circuitry (not shown) that conditions the signals generated by
sensors 40 such that they may be analyzed by processor 46. For
example, IMD 14 may include one or more analog to digital
converters to convert analog signals generated by sensors 40 into
digital signals usable by processor 46, as well as suitable filter
and amplifier circuitry. Although shown as including two sensors
40, system 10 may include any number of sensors.
[0062] Further, as illustrated in FIG. 2, sensors 40 may be
included as part of IMD 14, or coupled to IMD 14 via leads 16.
Sensors 40 may be coupled to IMD 14 via therapy leads 16A and 16B,
or via other leads 16, such as lead 16C depicted in FIG. 2. In some
embodiments, a sensor 40 located outside of IMD 14 may be in
wireless communication with processor 46. Wireless communication
between sensors 40 and IMD 14 may, as examples, include RF
communication or communication via electrical signals conducted
through the tissue and/or fluid of patient 12.
[0063] Exemplary physiological parameters of patient 12 that may be
monitored by IMD 14 include activity, posture, heart rate, ECG
morphology, respiration rate, respiratory volume, blood pressure,
blood oxygen saturation, partial pressure of oxygen within blood,
partial pressure of oxygen within cerebrospinal fluid (CSF),
muscular activity and tone, core temperature, subcutaneous
temperature, arterial blood flow, the level of melatonin within one
or more bodily fluids, brain electrical activity, and eye motion.
Further, as discussed above, in some external medical device
embodiments of the invention, galvanic skin response may
additionally or alternatively be monitored. Sensors 40 may be of
any type known in the art capable of generating a signal as a
function of one or more of these parameters.
[0064] Processor 46 may identify when patient 12 is attempting to
sleep in a variety of ways. For example, processor 46 may identify
the time that patient begins attempting to fall asleep based on an
indication received from patient 12, e.g., via clinician programmer
20 and a telemetry circuit 50. In other embodiments, processor 46
identifies the time that patient 12 begins attempting to fall
asleep based on the activity level of patient 12.
[0065] In such embodiments, IMD 14 may include one or more sensors
40 that generate a signal as a function of patient activity. For
example, sensors 40 may include one or more accelerometers, gyros,
mercury switches, or bonded piezoelectric crystals that generates a
signal as a function of patient activity, e.g., body motion,
footfalls or other impact events, and the like. Additionally or
alternatively, sensors 40 may include one or more electrodes that
generate an electromyogram (EMG) signal as a function of muscle
electrical activity, which may indicate the activity level of a
patient. The electrodes may be, for example, located in the legs,
abdomen, chest, back or buttocks of patient 12 to detect muscle
activity associated with walking, running, or the like. The
electrodes may be coupled to IMD 14 wirelessly or by leads 16 or,
if IMD 14 is implanted in these locations, integrated with a
housing of IMD 14.
[0066] However, bonded piezoelectric crystals located in these
areas generate signals as a function of muscle contraction in
addition to body motion, footfalls or other impact events.
Consequently, use of bonded piezoelectric crystals to detect
activity of patient 12 may be preferred in some embodiments in
which it is desired to detect muscle activity in addition to body
motion, footfalls, or other impact events. Bonded piezoelectric
crystals may be coupled to IMD 14 wirelessly or via leads 16, or
piezoelectric crystals may be bonded to the can of IMD 14 when the
IMD is implanted in these areas, e.g., in the back, chest, buttocks
or abdomen of patient 12.
[0067] Processor 46 may identify a time when the activity level of
patient 12 falls below a threshold activity level value stored in
memory 48, and may determine whether the activity level remains
substantially below the threshold activity level value for a
threshold amount of time stored in memory 48. In other words,
patient 12 remaining inactive for a sufficient period of time may
indicate that patient 12 is attempting to fall asleep. If processor
46 determines that the threshold amount of time is exceeded,
processor 46 may identify the time at which the activity level fell
below the threshold activity level value as the time that patient
12 began attempting to fall asleep.
[0068] In some embodiments, processor 46 determines whether patient
12 is attempting to fall asleep based on whether patient 12 is or
is not recumbent, e.g., lying down. In such embodiments, sensors 40
may include a plurality of accelerometers, gyros, or magnetometers
oriented orthogonally that generate signals which indicate the
posture of patient 12. In addition to being oriented orthogonally
with respect to each other, each of sensors 40 used to detect the
posture of patient 12 may be generally aligned with an axis of the
body of patient 12. In exemplary embodiments, IMD 14 includes three
orthogonally oriented posture sensors 40.
[0069] When sensors 40 include accelerometers, for example, that
are aligned in this manner, processor 46 may monitor the magnitude
and polarity of DC components of the signals generated by the
accelerometers to determine the orientation of patient 12 relative
to the Earth's gravity, e.g., the posture of patient 12. In
particular, the processor 46 may compare the DC components of the
signals to respective threshold values stored in memory 48 to
determine whether patient 12 is or is not recumbent. Further
information regarding use of orthogonally aligned accelerometers to
determine patient posture may be found in a commonly assigned U.S.
Pat. No. 5,593,431, which issued to Todd J. Sheldon.
[0070] Other sensors 40 that may generate a signal that indicates
the posture of patient 12 include electrodes that generate an
electromyogram (EMG) signal, or bonded piezoelectric crystals that
generate a signal as a function of contraction of muscles. Such
sensors 40 may be implanted in the legs, buttocks, abdomen, or back
of patient 12, as described above. The signals generated by such
sensors when implanted in these locations may vary based on the
posture of patient 12, e.g., may vary based on whether the patient
is standing, sitting, or laying down.
[0071] Further, the posture of patient 12 may affect the thoracic
impedance of the patient. Consequently, sensors 40 may include an
electrode pair, including one electrode integrated with the housing
of IMD 14 and one of electrodes 42, that generates a signal as a
function of the thoracic impedance of patient 12, and processor 46
may detect the posture or posture changes of patient 12 based on
the signal. The electrodes of the pair may be located on opposite
sides of the patient's thorax. For example, the electrode pair may
include one of electrodes 42 located proximate to the spine of a
patient for delivery of SCS therapy, and IMD 14 with an electrode
integrated in its housing may be implanted in the abdomen of
patient 12.
[0072] Additionally, changes of the posture of patient 12 may cause
pressure changes with the cerebrospinal fluid (CSF) of the patient.
Consequently, sensors 40 may include pressure sensors coupled to
one or more intrathecal or intracerebroventricular catheters, or
pressure sensors coupled to IMD 14 wirelessly or via lead 16. CSF
pressure changes associated with posture changes may be
particularly evident within the brain of the patient, e.g., may be
particularly apparent in an intracranial pressure (ICP)
waveform.
[0073] In some embodiments, processor 46 considers both the posture
and the activity level of patient 12 when determining whether
patient 12 is attempting to fall asleep. For example, processor 46
may determine whether patient 12 is attempting to fall asleep based
on a sufficiently long period of sub-threshold activity, as
described above, and may identify the time that patient began
attempting to fall asleep as the time when patient 12 became
recumbent. Any of a variety of combinations or variations of these
techniques may be used to determine when patient 12 is attempting
to fall asleep, and a specific one or more techniques may be
selected based on the sleeping and activity habits of a particular
patient.
[0074] In other embodiments, processor 46 determines when patient
12 is attempting to fall asleep based on the level of melatonin in
a bodily fluid. In such embodiments, a sensor 40 may take the form
of a chemical sensor that is sensitive to the level of melatonin or
a metabolite of melatonin in the bodily fluid, and estimate the
time that patient 12 will attempt to fall asleep based on the
detection. For example, processor 46 may compare the melatonin
level or rate of change in the melatonin level to a threshold level
stored in memory 48, and identify the time that threshold value is
exceeded. Processor 46 may identify the time that patient 12 is
attempting to fall asleep as the time that the threshold is
exceeded, or some amount of time after the threshold is exceeded.
Any of a variety of combinations or variations of the
above-described techniques may be used to determine when patient 12
is attempting to fall asleep, and a specific one or more techniques
may be selected based on the sleeping and activity habits of a
particular patient.
[0075] When IMD 14 determines that patient 12 is attempting to
sleep, IMD 14 may determine values for one or more metrics that
indicate the quality of a patient's sleep based on at least one of
the above-identified physiological parameters of the patient. In
particular, in order to determine values for some sleep quality
metrics, IMD 14 determines when patient 12 is asleep, e.g.,
identify the times that patient 12 falls asleep and wakes up, in
addition to when patient 12 is attempting to fall asleep. The
detected values of physiological parameters of patient 12, such as
activity level, heart rate, ECG morphological features, respiration
rate, respiratory volume, blood pressure, blood oxygen saturation,
partial pressure of oxygen within blood, partial pressure of oxygen
within cerebrospinal fluid, muscular activity and tone, core
temperature, subcutaneous temperature, arterial blood flow, brain
electrical activity, eye motion, and galvanic skin response may
discernibly change when patient 12 falls asleep or awakes. Some of
these physiological parameters may be at low values when patient 12
is asleep. Further, the variability of at least some of these
parameters, such as heart rate and respiration rate, may be at a
low value when the patient is asleep.
[0076] Consequently, in order to detect when patient 12 falls
asleep and wakes up, processor 46 may monitor one or more of these
physiological parameters, or the variability of these physiological
parameters, and detect the discemable changes in their values
associated with a transition between a sleeping state and an awake
state. In some embodiments, processor 46 may determine a mean or
median value for a parameter based on values of a signal over time,
and determine whether patient 12 is asleep or awake based on the
mean or median value. Processor 46 may compare one or more
parameter or parameter variability values to thresholds stored in
memory 48 to detect when patient 12 falls asleep or awakes. The
thresholds may be absolute values of a physiological parameter, or
time rate of change values for the physiological parameter, e.g.,
to detect sudden changes in the value of a parameter or parameter
variability. In some embodiments, a threshold used by processor 46
to determine whether patient 12 is asleep may include a time
component. For example, a threshold may require that a
physiological parameter be above or below a threshold value for a
period of time before processor 46 determines that patient is awake
or asleep.
[0077] In some embodiments, in order to determine whether patient
12 is asleep, processor 46 monitors a plurality of physiological
parameters, and determines a value of a metric that indicates the
probability that patient 12 is asleep for each of the parameters
based on a value of the parameter. In particular, the processor 46
may apply a function or look-up table to the current, mean or
median value, and/or the variability of each of a plurality of
physiological parameters to determine a sleep probability metric
for each of the plurality of physiological parameters. A sleep
probability metric value may be a numeric value, and in some
embodiments may be a probability value, e.g., a number within the
range from 0 to 1, or a percentage value.
[0078] Processor 46 may average or otherwise combine the plurality
of sleep probability metric values to provide an overall sleep
probability metric value. In some embodiments, processor 46 may
apply a weighting factor to one or more of the sleep probability
metric values prior to combination. Processor 46 may compare the
overall sleep probability metric value to one or more threshold
values stored in memory 48 to determine when patient 12 falls
asleep or awakes. Use of sleep probability metric values to
determine when a patient is asleep based on a plurality of
monitored physiological parameters is described in greater detail
in a commonly-assigned and copending U.S. patent application Ser.
No. __________ by Ken Heruth and Keith Miesel, entitled "DETECTING
SLEEP," bearing Attorney Docket No. 1023-360US02 and filed on Mar.
16, 2005, which is incorporated herein by reference in its
entirety.
[0079] To enable processor 46 to determine when patient 12 is
asleep or awake, sensors 40 may include, for example, activity
sensors as described above. In some embodiments, the activity
sensors may include electrodes or bonded piezoelectric crystals,
which may be implanted in the back, chest, buttocks, or abdomen of
patient 12 as described above. In such embodiments, processor 46
may detect the electrical activation and contractions of muscles
associated with gross motor activity of the patient, e.g.,
walking,.running or the like via the signals generated by such
sensors. Processor 46 may also detect spasmodic or pain related
muscle activation via the signals generated by such sensors.
Spasmodic or pain related muscle activation may indicate that
patient 12 is not sleeping, e.g., unable to sleep, or if patient 12
is sleeping, may indicate a lower level of sleep quality.
[0080] As another example, sensors 40 may include electrodes
located on leads or integrated as part of the housing of IMD 14
that generate an electrogram signal as a function of electrical
activity of the heart of patient 12, and processor 46 may monitor
the heart rate of patient 12 based on the electrogram signal. In
other embodiments, a sensor may include an acoustic sensor within
IMD 14, a pressure or flow sensor within the bloodstream or
cerebrospinal fluid of patient 12, or a temperature sensor located
within the bloodstream of patient 12. The signals generated by such
sensors may vary as a function of contraction of the heart of
patient 12, and can be used by IMD 14 to monitor the heart rate of
patient 12.
[0081] In some embodiments, processor 46 may detect, and measure
values for one or more ECG morphological features within an
electrogram generated by electrodes as described above. ECG
morphological features may vary in a manner that indicates whether
patient 12 is asleep or awake. For example, the amplitude of the ST
segment of the ECG may decrease when patient 12 is asleep. Further,
the amplitude of QRS complex or T-wave may decrease, and the widths
of the QRS complex and T-wave may increase when patient 12 is
asleep. The QT interval and the latency of an evoked response may
increase when patient 12 is asleep, and the amplitude of the evoked
response may decrease when patient 12 is asleep.
[0082] In some embodiments, sensors 40 may include an electrode
pair, including one electrode integrated with the housing of IMD 14
and one of electrodes 42, that generates a signal as a function of
the thoracic impedance of patient 12, as described above, which
varies as a function of respiration by patient 12. In other
embodiments, sensors 40 may include a strain gauge, bonded
piezoelectric element, or pressure sensor within the blood or
cerebrospinal fluid that generates a signal that varies based on
patient respiration. An electrogram generated by electrodes as
discussed above may also be modulated by patient respiration, and
may be used as an indirect representation of respiration rate.
[0083] Sensors 40 may include electrodes that generate an
electromyogram (EMG) signal as a function of muscle electrical
activity, as described above, or may include any of a variety of
known temperature sensors to generate a signal as a function of a
core or subcutaneous temperature of patient 12. Such electrodes and
temperature sensors may be incorporated within the housing of IMD
14, or coupled to IMD 14 wirelessly or via leads. Sensors 40 may
also include a pressure sensor within, or in contact with, a blood
vessel. The pressure sensor may generate a signal as a function of
the a blood pressure of patient 12, and may, for example, comprise
a Chronicle Hemodynamic Monitor.TM. commercially available from
Medtronic, Inc. of Minneapolis, Minnesota. Further, certain muscles
of patient 12, such as the muscles of the patient's neck, may
discernibly relax when patient 12 is asleep or within certain sleep
states. Consequently, sensors 40 may include strain gauges or EMG
electrodes implanted in such locations that generate a signal as a
function of muscle tone.
[0084] Sensors 40 may also include optical pulse oximetry sensors
or Clark dissolved oxygen sensors located within, as part of a
housing of, or outside of IMD 14, which generate signals as a
function of blood oxygen saturation and blood oxygen partial
pressure respectively. In some embodiments, system 10 may include a
catheter with a distal portion located within the cerebrospinal
fluid of patient 12, and the distal end may include a Clark
dissolved oxygen sensor to generate a signal as a function of the
partial pressure of oxygen within the cerebrospinal fluid (CSF).
Embodiments in which an IMD comprises an implantable pump, for
example, may include a catheter with a distal portion located in
the cerebrospinal fluid.
[0085] In some embodiments, sensors 40 may include one or more
intraluminal, extraluminal, or external flow sensors positioned to
generate a signal as a function of arterial blood flow. A flow
sensor may be, for example, an electromagnetic, thermal convection,
ultrasonic-Doppler, or laser-Doppler flow sensor. Further, in some
external medical device embodiments of the invention, sensors 40
may include one or more electrodes positioned on the skin of
patient 12 to generate a signal as a function of galvanic skin
response.
[0086] Additionally, in some embodiments, sensors 40 may include
one or more electrodes positioned within or proximate to the brain
of patient, which detect electrical activity of the brain. For
example, in embodiments in which IMD 14 delivers stimulation or
other therapy to the brain, processor 46 may be coupled to
electrodes implanted on or within the brain via a lead 16. In other
embodiments, processor 46 may be wirelessly coupled to electrodes
that detect brain electrical activity.
[0087] For example, one or more modules may be implanted beneath
the scalp of the patient, each module including a housing, one or
more electrodes, and circuitry to wirelessly transmit the signals
detected by the one or more electrodes to IMD 14. In other
embodiments, the electrodes may be applied to the patient's scalp,
and electrically coupled to a module that includes circuitry for
wirelessly transmitting the signals detected by the electrodes to
IMD 14. The electrodes may be glued to the patient's scalp, or a
head band, hair net, cap, or the like may incorporate the
electrodes and the module, and may be worn by patient 12 to apply
the electrodes to the patient's scalp when, for example, the
patient is attempting to sleep. The signals detected by the
electrodes and transmitted to IMD 14 may be electroencephalogram
(EEG) signals, and processor 46 may process the EEG signals to
detect when patient 12 is asleep using any of a variety of known
techniques, such as techniques that identify whether a patient is
asleep based on the amplitude and/or frequency of the EEG
signals.
[0088] Also, the motion of the eyes of patient 12 may vary
depending on whether the patient is sleeping and which sleep state
the patient is in. Consequently, sensors 40 may include electrodes
place proximate to the eyes of patient 12 to detect electrical
activity associated with motion of the eyes, e.g., to generate an
electro-oculography (EOG) signal. Such electrodes may be coupled to
IMD 14 via one or more leads 16, or may be included within modules
that include circuitry to wirelessly transmit detected signals to
IMD 14. Wirelessly coupled modules incorporating electrodes to
detect eye motion may be worn externally by patient 12, e.g.,
attached to the skin of patient 12 proximate to the eyes by an
adhesive when the patient is attempting to sleep.
[0089] Processor 46 may also detect arousals and/or apneas that
occur when patient 12 is asleep based on one or more of the
above-identified physiological parameters. For example, processor
46 may detect an arousal based on an increase or sudden increase in
one or more of heart rate, heart rate variability, respiration
rate, respiration rate variability, blood pressure, or muscular
activity as the occurrence of an arousal. Processor 46 may detect
an apnea based on a disturbance in the respiration rate of patient
12, e.g., a period with no respiration.
[0090] Processor 46 may also detect arousals or apneas based on
sudden changes in one or more of the ECG morphological features
identified above. For example, a sudden elevation of the ST segment
within the ECG may indicate an arousal or an apnea. Further, sudden
changes in the amplitude or frequency of an EEG signal, EOG signal,
or muscle tone signal may indicate an apnea or arousal. Memory 48
may store thresholds used by processor 46 to detect arousals and
apneas. Processor 46 may determine, as a sleep quality metric
value, the number of apnea events and/or arousals during a
night.
[0091] Further, in some embodiments, processor 46 may determine
which sleep state patient 12 is in during sleep, e.g., REM, S1, S2,
S3, or S4, based on one or more of the monitored physiological
parameters. In some embodiments, memory 48 may store one or more
thresholds for each of sleep states, and processor 46 may compare
physiological parameter or sleep probability metric values to the
thresholds to determine which sleep state patient 12 is currently
in. Further, in some embodiments, processor 46 may use any of a
variety of known techniques for determining which sleep state
patient is in based on an EEG signal, which processor 46 may
receive via electrodes as described above, such as techniques that
identify sleep state based on the amplitude and/or frequency of the
EEG signals. In some embodiments, processor 46 may also determine
which sleep state patient is in based on an EOG signal, which
processor 46 may receive via electrodes as described above, either
alone or in combination with an EEG signal, using any of a variety
of techniques known in the art. Processor 46 may determine, as
sleep quality metric values, the amounts of time per night spent in
the various sleep states.
[0092] The S3 and S4 sleep states may be of particular importance
to the quality of sleep experienced by patient 12. Interruption
from reaching these states, or inadequate time per night spent in
these states, may cause patient 12 to not feel rested. For this
reason, the S3 and S4 sleep states are believed to provide the
"refreshing" part of sleep.
[0093] In some cases, interruption from reaching the S3 and S4
sleep states, or inadequate time per night spent in these states
has been demonstrated to cause normal subjects to exhibit some
symptoms of fibromyalgia. Also, subjects with fibromyalgia usually
do not reach these sleep states. For these reasons, in some
embodiments, IMD 14 may determine an amount or percentage of time
spent in one or both of the S3 and S4 sleep states as a sleep
quality metric.
[0094] When processor 46 determines that patient 12 is not
attempting to sleep, processor 46 periodically determines activity
levels of the patient. For example, a sensor 40 may be an
accelerometer, a bonded piezoelectric crystal, a mercury switch, or
a gyro, and processor 46 may determine an activity level based on a
signal generated by one of these types of sensors 40 by sampling
the signal and determining a number of activity counts during the
sample period. Processor 46 may then store the determined number of
activity counts in memory 48 as an activity level.
[0095] For example, processor 46 may compare the sample of a signal
generated by an accelerometer or piezoelectric crystal to one or
more amplitude thresholds stored within memory 48. Processor 46 may
identify each threshold crossing as an activity count. Where
processor 46 compares the sample to multiple thresholds with
varying amplitudes, processor 46 may identify crossing of higher
amplitude thresholds as multiple activity counts. Using multiple
thresholds to identify activity counts, processor 46 may be able to
more accurately determine the extent of patient activity for both
high impact, low frequency and low impact, high frequency
activities. In embodiments in which a sensor 40 takes the form of a
mercury switch, processor 46 may identify the number of switch
contacts indicated during the sample period as the number of
activity counts.
[0096] In embodiments in which a sensor 40 comprises an
accelerometer or piezoelectric crystal, IMD 14 may include a filter
(not shown), or processor 46 may apply a digital filter, that
passes a band from approximately 0.1 Hz to 10 Hz. The filter may
reduce noise in the signal, and pass the portion of the signal that
reflects patient activity.
[0097] In some embodiments, the processor 46 may monitor a signal
that indicates a physiological parameter of patient 12, which in
turn varies as a function of patient activity. For example, in some
embodiments, sensors 40 may includes one or more sensors that
generate a signal that indicates the heart rate, ECG morphology,
respiration rate, respiratory volume, or muscular activity of the
patient, as described above. In such embodiments, processor 46 may
periodically determine the heart rate, values of ECG morphological
features, respiration rate, respiratory volume, or muscular
activity level of patient 12 based on the signal. The determined
values of these parameters may be mean or median values.
[0098] In some embodiments, processor 46 compares a determined
value of such a physiological parameter to one or more thresholds
or a look-up table stored in memory to determine a number of
activity counts, and stores the determined number of activity
counts in memory 48 as a determined activity level. In other
embodiments, processor 46 may store the determined physiological
parameter value as a determined activity level. The use of activity
counts, however, may allow processor 46 to determine an activity
level based on a plurality of signals generated by a plurality of
sensors 40. For example, processor 46 may determine a first number
of activity counts based on a sample of an accelerometer signal and
a second number of activity counts based on a heart rate determined
from an electrogram signal at the time the accelerometer signal was
sampled. Processor 46 may determine an activity level by
calculating the sum or average, which may be a weighted sum or
average, of first and second activity counts.
[0099] Processor 46 may record activity levels continuously or
periodically, e.g., one sample every minute or continuously for ten
minutes each hour. Further processor 46 need not determine sleep
quality metrics each time patient 12 attempts to sleep, or record
activity levels each time patient 12 is not attempting to sleep. In
some embodiments, processor 46 may record activity levels and
determine sleep quality metric values in response to receiving an
indication from patient 12 via patient programmer 26. Patient 12
may provide the indication by depressing a button or otherwise
manipulating user input media on programmer 26. For example,
processor 46 may record activity levels and sleep quality metrics
during times when patient 12 believes the therapy delivered by IMD
14 is ineffective and/or the symptoms experienced by patient 12
have worsened. In this manner, processor 46 may limit data
collection to periods in which more probative data is likely to be
collected, and thereby conserve a battery and/or storage space
within memory 48.
[0100] In some embodiments, processor 46 determines a value of one
or more activity metrics based on determined activity levels and
stores the activity metric values within memory 48. For example,
processor 46 may determine a mean or median of activity levels, and
store the mean or median activity level as an activity metric
value. In embodiments in which activity levels comprise activity
counts, processor 46 may store, for example, an average number of
activity counts per unit time as an activity metric value.
[0101] In other embodiments, processor 46 may compare a mean or
median activity level to one or more threshold values, and may
select an activity metric value from a predetermined scale of
activity metric values based on the comparison. The scale may be
numeric, such as activity metric values from 1-10, or qualitative,
such as low, medium or high activity. The scale of activity metric
values may be, for example, stored as a look-up table within memory
48. Processor 46 stores the activity metric value selected from the
scale within memory 48.
[0102] In some embodiments, processor 46 compares a number of
activity levels to one or more threshold values. Based on the
comparison, processor 46 may determine percentages of time above
and/or below the thresholds, or within threshold ranges. Processor
46 may store the determined percentages within memory 48 as one or
more activity metric values. In other embodiments, processor 46
compares a number of activity levels to a threshold value, and
determines an average length of time that consecutively recorded
activity levels remained above the threshold as an activity metric
value.
[0103] FIG. 3 further illustrates memory 48 of IMD 14. As
illustrated in FIG. 3, memory 48 stores information describing a
plurality of therapy parameter sets 60. Therapy parameter sets 60
may include parameter sets specified by a clinician using clinician
programmer 20. Therapy parameter sets 60 may also include parameter
sets that are the result of patient 12 changing one or more
parameters of one of the preprogrammed therapy parameter sets.
[0104] Memory 48 also stores the activity levels 62, sleep quality
metric values 66, and activity metric values 68 determined by
processor 46, as described herein, and threshold values 64 used by
processor 46 to determine activity levels 62, sleep quality metric
values 66, and activity metric values 68, as described herein. In
some embodiments, memory 48 also stores one or more functions or
look-up tables (not shown) used by processor 46 to determine sleep
probability metric values, activity levels 62, sleep quality metric
values 66, and activity metric values 68, as described herein.
[0105] Processor 46 may store each sleep quality metric value
determined within memory 48 as a sleep quality metric value 66, or
may store mean or median sleep quality metric values over periods
of time such as days, weeks or months as sleep quality metric
values 66. Further, processor 46 may apply a function or look-up
table to a plurality of sleep quality metric values to determine
overall sleep quality metric value, and may store the overall sleep
quality metric values within memory 48. The application of a
function or look-up table by processor 46 for this purpose may
involve the use or weighting factors for one or more of the
individual sleep quality metric values.
[0106] Similarly, in some embodiments, processor 46 determines a
plurality of activity metric values, and determines an overall
activity metric value for a parameter set based on the values of
the individual activity metrics for that parameter set. For
example, processor 46 may use the plurality of individual activity
metric values as indices to identify an overall activity metric
value from a look-up table stored in memory 48. Processor 46 may
select the overall metric value from a predetermined scale of
activity metric values, which may be numeric, such as activity
metric values from 1-10, or qualitative, such as low, medium or
high activity.
[0107] In some embodiments, processor 46 identifies which of
therapy parameter sets 60 is currently selected for use in
delivering therapy to patient 12 when an activity level 62 or sleep
quality metric value 66 is collected, and may associate that value
or level with the current therapy parameter set. For example, for
each of the plurality of therapy parameter sets 60, processor 46
may store a representative value of each of one or more sleep
quality metrics within memory 48 as a sleep quality metric value 66
with an indication of the therapy parameter set with which that
representative value is associated. A representative value of sleep
quality metric for a therapy parameter set may be the mean or
median of collected sleep quality metric values that have been
associated with that therapy parameter set. Further, processor 46
may determine a value of one or more activity metrics for each of
therapy parameter sets 60 based on activity levels 62 associated
with that therapy parameter set, and may store the associated
activity metric values 68 within memory 48.
[0108] As shown in FIG. 2, IMD 14 also includes a telemetry circuit
50 that allows processor 46 to communicate with clinician
programmer 20 and patient programmer 26. Processor 46 may receive
information identifying therapy parameter sets 60 preprogrammed by
the clinician and threshold values 64 from clinician programmer 20
via telemetry circuit 50 for storage in memory 48. Processor 46 may
receive an indication of the therapy parameter set 60 selected by
patient 12 for delivery of therapy, or adjustments to one or more
of therapy parameter sets 60 made by patient 12, from patient
programmer 26 via telemetry circuit 50. Programmers 20, 26 may
receive sleep quality metric values 66 and activity metric values
68 from processor 46 via telemetry circuit 50.
[0109] FIG. 4 is a flow diagram illustrating an example method for
collecting sleep quality information and activity information that
may be employed by IMD 14. IMD 14 monitors the posture, activity
level, and/or melatonin level of patient 12, or monitors for an
indication from patient 12, e.g., via patient programmer 26 (70),
and determines whether patient 12 is attempting to fall asleep
based on the posture, activity level, and/or a patient indication,
as described above (72). When IMD 14 determines that patient 12 is
not attempting to fall asleep, IMD 14 collects activity
information, e.g., periodically determines activity levels 62 (74).
When IMD 14 determines that patient 12 is attempting to fall
asleep, IMD 14 collects sleep quality information, e.g., determines
sleep quality metric values, until patient 12 is determined to be
awake (76).
[0110] As discussed above, IMD 14 need not collect sleep
information each time patient 12 attempts to sleep, or record
activity levels each time patient 12 is not attempting to sleep. In
some embodiments, IMD 14 may record activity levels and determine
sleep quality metric values in response to receiving an indication
from patient 12 via patient programmer 26. For example, IMD 14 may
record activity levels and sleep quality metrics during times when
patient 12 believes the therapy delivered by IMD 14 is ineffective
and/or the symptoms experienced by patient 12 have worsened. In
this manner, IMD 14 may limit data collection to periods in which
more probative data is likely to be collected, and thereby conserve
a battery and/or storage space within memory 48.
[0111] FIG. 5 is a flow diagram illustrating an example method for
collecting sleep quality information that may be employed by IMD
14. When IMD 14 determines that patient 12 is attempting to fall
asleep (FIG. 4), IMD 14 identifies the time that patient 12 began
attempting to fall asleep using any of the techniques described
above (80), and monitors one or more of the various physiological
parameters of patient 12 discussed above to determine whether
patient 12 is asleep (82, 84).
[0112] In some embodiments, IMD 14 compares parameter values or
parameter variability values to one or more threshold values 64 to
determine whether patient 12 is asleep. In other embodiments, IMD
14 applies one or more functions or look-up tables to determine one
or more sleep probability metric values based on the physiological
parameter values, and compares the sleep probability metric values
to one or more threshold values 64 to determine whether patient 12
is asleep. While monitoring physiological parameters (82) to
determine whether patient 12 is asleep (84), IMD 14 may continue to
monitor the posture and/or activity level of patient 12 to confirm
that patient 12 is still attempting to fall asleep.
[0113] When IMD 14 determines that patient 12 is asleep, e.g., by
analysis of the various parameters contemplated herein, IMD 14 will
identify the time that patient 12 fell asleep (86). While patient
12 is sleeping, IMD 14 will continue to monitor physiological
parameters of patient 12 (88). As discussed above, IMD 14 may
identify the occurrence of arousals and/or apneas based on the
monitored physiological parameters (90). Further, IMD 14 may
identify the time that transitions between sleep states, e.g., REM,
S1, S2, S3, and S4, occur based on the monitored physiological
parameters (90).
[0114] Additionally, while patient 12 is sleeping, IMD 14 monitors
physiological parameters of patient 12 (88) to determine whether
patient 12 has woken up (92). When IMD 14 determines that patient
12 is awake, IMD 14 identifies the time that patient 12 awoke (94),
and determines sleep quality metric values based on the information
collected while patient 12 was asleep (96).
[0115] For example, one sleep quality metric value IMD 14 may
calculate is sleep efficiency, which IMD 14 may calculate as a
percentage of time during which patient 12 is attempting to sleep
that patient 12 is actually asleep. IMD 14 may determine a first
amount of time between the time IMD 14 identified that patient 12
fell asleep and the time IMD 14 identified that patient 12 awoke.
IMD may also determine a second amount of time between the time IMD
14 identified that patient 12 began attempting to fall asleep and
the time IMD 14 identified that patient 12 awoke. To calculate the
sleep efficiency, IMD 14 may divide the first time by the second
time.
[0116] Another sleep quality metric value that IMD 14 may calculate
is sleep latency, which IMD 14 may calculate as the amount of time
between the time IMD 14 identified that patient 12 was attempting
to fall asleep and the time IMD 14 identified that patient 12 fell
asleep. Other sleep quality metrics with values determined by IMD
14 based on the information collected by IMD 14 in the illustrated
example include: total time sleeping per day, at night, and during
daytime hours; number of apnea and arousal events per occurrence of
sleep; and amount of time spent in the various sleep states, e.g.,
the S3 and S4 sleep states. IMD 14 may store the determined values
as sleep quality metric values 66 within memory 48.
[0117] FIG. 6 is a flow diagram illustrating an example method for
collecting activity information that may be employed by IMD 14. IMD
14 monitors one or more signals that reflect patient activity
generated by sensors 40 (100). For example, IMD 14 may monitor a
signal generated by an accelerometer or piezoelectric crystal,
and/or a signal that indicates a physiological parameter that
varies as a function of patient activity, such as heart rate, ECG
morphology, respiration rate, respiratory volume, or muscle
activity.
[0118] IMD 14 determines an activity level 62 (102) based on the
one or more signals. For example, IMD 14 may determine a number of
activity counts based on the one or more signals, as described
above. IMD 14 may then update one or more activity metric values 66
based on the determined activity level (104).
[0119] IMD 14 may periodically perform the method illustrated in
FIG. 6, i.e., periodically determine activity levels 62. IMD 14
need not update activity metric values 66 each time an activity
level 62 is determined. In some embodiments, for example, IMD 14
may store activity levels 62 within memory, and may determine the
activity metric values 66 upon receiving a request for the values
from clinician programmer 20.
[0120] FIG. 7 is a flow diagram illustrating an example method for
associating sleep quality information and activity information with
therapy parameter sets that may be employed by IMD 14. IMD 14
determines a value 66 of a sleep quality metric or an activity
level 62 according to any of the techniques described above (110).
IMD 14 also identifies the current therapy parameter set 60, e.g.,
the therapy parameter set 60 used by IMD 14 to control delivery of
therapy when patient 12 was asleep or when the activity level was
determined (112), and associates the newly determined level or
value with the current therapy parameter set 60.
[0121] Among sleep quality metric values 66 within memory 48, IMD
14 stores a representative value of the sleep quality metric, e.g.,
a mean or median value, for each of the plurality of therapy
parameter sets 60. When IMD 14 determines a new sleep quality
metric value, IMD 14 updates the representative values for the
current therapy parameter set based on the newly determined sleep
quality metric value (114). For example, a newly determined sleep
efficiency value may be used to determine a new average sleep
efficiency value for the current therapy parameter set 60.
Similarly, among the activity metric values 68 within memory 48,
IMD 14 stores an associated activity metric value. When IMD 14
determines a new activity level 62, IMD 14 updates the activity
metric value 68 the current therapy parameter set based on the
newly determined activity level.
[0122] FIG. 8 is a block diagram further illustrating clinician
programmer 20. A clinician may interact with a processor 120 via a
user interface 122 in order to program therapy for patient 12,
e.g., specify therapy parameter sets. Processor 120 may provide the
specified therapy parameter sets to IMD 14 via telemetry circuit
124.
[0123] At another time, e.g., during a follow up visit, processor
120 may receive activity levels 62, sleep quality metric values 66,
and/or activity metric values 68 from IMD 14 via a telemetry
circuit 124, and may generate sleep quality information or activity
information for presentation to the clinician via user interface
122. For example, processor 120 may present a trend diagram of
activity levels 62 or sleep quality metric values 66 over time, or
a histogram, pie chart, or other illustration of percentages of
time that activity levels 62 or sleep quality metric values 66 were
within certain ranges. Processor 120 may generate such charts or
diagrams using activity levels 62 and sleep quality metric values
66 associated with a particular one of the therapy parameter sets
60, or all of the activity levels 62 and sleep quality metric
values 66 recorded by IMD 14.
[0124] Processor 120 may also receive information identifying a
plurality of therapy parameter sets 60, and representative sleep
quality metric values 66 and activity metric values associated with
the therapy parameter sets 60, from IMD 14 via telemetry circuit
124. The therapy parameter sets 60 may include the originally
specified parameter sets, and parameter sets resulting from
manipulation of one or more therapy parameters by patient 12 using
patient programmer 26. After receiving this information, processor
120 generates a list of the therapy parameter sets 60 and
associated sleep quality metric values 66 and activity metric
values 68, and presents the list to the clinician via user
interface 122.
[0125] User interface 112 may include display 22 and keypad 24, and
may also include a touch screen or peripheral pointing devices as
described above. Processor 110 may include a microprocessor, a
controller, a DSP, an ASIC, an FPGA, discrete logic circuitry, or
the like. Clinician programmer 20 also includes a memory 116.
Memory 116 may include program instructions that, when executed by
processor 110, cause clinician programmer 20 to perform the
functions ascribed to clinician programmer 20 herein. Memory 116
may include any volatile, non-volatile, fixed, removable, magnetic,
optical, or electrical media, such as a RAM, ROM, CD-ROM, hard
disk, removable magnetic disk, memory cards or sticks, NVRAM,
EEPROM, flash memory, and the like.
[0126] FIG. 9 illustrates an example list 130 of therapy parameter
sets 60, associated sleep quality metric values 66, and associated
activity metric values 68 that may be presented to a clinician by
clinician programmer 20. Each row of example list 130 includes an
identification of one of therapy parameter sets 60, the parameters
of the set, a representative value for one or more sleep quality
metrics associated with the identified therapy parameter set, and
an associated value of at least one activity metric.
[0127] List 130 may include values for any number of sleep quality
metrics and activity metrics. The illustrated example list 130
includes sleep efficiency, sleep latency and a percentage of time
active. IMD 14 may determine the percentage of time active for one
of parameter sets 60 by, for example, comparing each activity level
62 associated with the parameter set to an "active" threshold,
i.e., a threshold indicative of significant physical activity, and
determining the percentage of activity levels 62 above the
threshold. As illustrated in FIG. 9, IMD 14 may also compare each
activity level for the therapy parameter set to an additional,
"high activity" threshold, and determine a percentage of activity
levels 62 above that threshold.
[0128] FIG. 10 is a flow diagram illustrating an example method for
displaying a list 130 of therapy parameter sets and associated
sleep quality and activity information that may be employed by
clinician programmer 20. According to the example method, clinician
programmer 20 receives information identifying the plurality of
therapy parameter sets 60 stored in memory 48 of IMD 14, one or
more representative sleep quality metric values associated with
each of the therapy parameter sets, and one or more activity metric
values associated with each of the activity sets (140). Clinician
programmer 20 generates a list 130 of the therapy parameter sets
60, any associated representative sleep quality metric values, and
any associated activity metric values (142), and orders the list
according to a selected one of the sleep quality metrics or
activity metrics (144). For example, in the example list 130
illustrated in FIG. 9, the clinician may select whether list 130
should be ordered according to sleep efficiency, sleep latency, or
percentage of time active via user interface 122 of clinician
programmer 20.
[0129] Various embodiments of the invention have been described.
However, one skilled in the art will recognize that various
modifications may be made to the described embodiments without
departing from the scope of the invention. For example, a patient
programming device, such as patient programmer 26, may additionally
or alternatively receive sleep quality metric values and/or
activity metric values from IMD 14, and may provide sleep quality
or activity information to a user based on the sleep quality or
activity metric values. Further details regarding provision of
sleep quality information to a patient via a patient programming
device may be found in a commonly-assigned and copending U.S.
patent application Ser. No. _______ by Ken Heruth and Keith Miesel,
entitled "COLLECTING SLEEP QUALITY INFORMATION VIA A MEDICAL
DEVICE," bearing Attorney Docket No. 1023-350US02 and filed on Mar.
16, 2005, which is incorporated herein by reference in its
entirety.
[0130] As another example, although described herein primarily in
the context of treatment of pain with an implantable
neurostimulator, the invention is not so limited. The invention may
be embodied in any implantable medical device, such as a cardiac
pacemaker, an implantable pump, or an implantable monitor that does
not itself deliver a therapy to the patient. Further, the invention
may be implemented via an external, e.g., non-implantable, medical
device.
[0131] Additionally, the invention is not limited to embodiments in
which a programming device receives information from the medical
device, or presents information to a user. Other computing devices,
such as handheld computers, desktop computers, workstations, or
servers may receive information from the medical device and present
information to a user as described herein with reference to
programmers 20, 26. A computing device, such as a server, may
receive information from the medical device and present information
to a user via a network, such as a local area network (LAN), wide
area network (WAN), or the Internet. Further, in some embodiments,
the medical device is an external medical device, and may itself
include user interface and display to present activity information
to a user, such as a clinician or patient, for evaluation of
therapy parameter sets.
[0132] As another example, the invention may be embodied in a trial
neurostimulator, which is coupled to percutaneous leads implanted
within the patient to determine whether the patient is a candidate
for neurostimulation, and to evaluate prospective neurostimulation
therapy parameter sets. Similarly, the invention may be embodied in
a trial drug pump, which is coupled to a percutaneous catheter
implanted within the patient to determine whether the patient is a
candidate for an implantable pump, and to evaluate prospective
therapeutic agent delivery parameter sets. Sleep quality metric
values and activity metric values collected during use of the trial
neurostimulator or pump may be used by a clinician to evaluate the
prospective therapy parameter sets, and select parameter sets for
use by the later implanted non-trial neurostimulator or pump. For
example, a trial neurostimulator or pump may determine
representative values of one or more sleep quality metrics and
activity metric values for each of a plurality of prospective
therapy parameter sets, and a clinician programmer may present a
list of prospective parameter sets and associated representative
values to a clinician. The clinician may use the list to identify
potentially efficacious parameter sets, and may program a permanent
implantable neurostimulator or pump for the patient with the
identified parameter sets.
[0133] In some embodiments, a therapy delivering implantable or
external medical device does not determine whether the patient is
attempting to sleep, determine values for sleep quality metrics,
determine activity metric values, and/or periodically determine
activity levels. Instead, in some embodiments, a computing device,
such as one of programming devices 20, 26, or the other types of
computing devices identified above, performs one or more of these
functions. For example, a programming device, and more particularly
a processor of the programming device, e.g., processor 120, may
receive physiological parameter values, activity levels, and/or
samples of an activity signal from a medical device, and determine
activity metric values and sleep quality metric values based on the
information received from the medical device using any of the
techniques described herein with reference to a medical device.
[0134] In some embodiments, the medical device may associate
recorded physiological parameter values, signal samples, and/or
activity levels with a current therapy parameter set, and may
provide information identifying and plurality of therapy parameter
sets and collected information associated with the therapy
parameter sets to the programming device or other computing device.
In such embodiments, the programming device may determine
representative sleep quality metric values and activity metric
values associated with the various therapy parameter sets using any
of techniques described herein with reference to a medical device.
The programming device may receive such information from the
medical device in real time, or may interrogate the medical device
for information recorded by the medical device over a period of
time.
[0135] Additionally, the invention is not limited to embodiments in
which the therapy delivering medical device monitors the
physiological parameters of the patient described herein. In some
embodiments, a separate monitoring device monitors values of one or
more physiological parameters of the patient instead of, or in
addition to, a therapy delivering medical device. The monitor may
include a processor 46 and memory 48, and may be coupled to or
include sensors 40, as illustrated above with reference to IMD 14
and FIGS. 2 and 3. The monitor may determine whether the patient is
attempting to sleep, determine values for sleep quality metrics,
determine activity metric values, and/or periodically determine
activity levels based on the values of the monitored physiological
parameter values, or may transmit activity levels or the
physiological parameter values to a computing device for
determination of whether the patient is attempting to sleep, values
for sleep quality metrics, and/or activity metric values.
[0136] In embodiments in which the medical device determines sleep
quality and activity metric values, the medical device may identify
the current therapy parameter set when a value of one or more sleep
quality or activity metrics is collected, and may associate that
value with the therapy parameter set. In embodiments in which a
programming device or other computing device determines activity
levels, or activity or sleep quality metric values, the medical
device may associate recorded physiological parameter values or
activity levels with the current therapy parameter set in the
memory. Further, in embodiments in which a separate monitoring
device records physiological parameter values, determines activity
levels, or determines activity or sleep quality metric values, the
monitoring device may mark recorded physiological parameter values,
activity levels, or activity or sleep quality metric values with a
current time in a memory, and the medical device may store an
indication of a current therapy parameter set and time in a memory.
A programming device of other computing device may receive
indications of the physiological parameter values, activity levels,
or activity or sleep quality metric values and associated times
from the monitoring device, and indications of the therapy
parameter sets and associated times from the medical device, and
may associate the physiological parameter values, activity levels,
or activity or sleep quality metric values with the therapy
parameter set that was delivered by the medical device when the
values or levels were recorded.
[0137] FIG. 11 is a conceptual diagram illustrating a monitor 150
that monitors values of one or more physiological parameters of the
patient instead of, or in addition to, a therapy delivering medical
device. In the illustrated example, monitor 150 is configured to be
attached to or otherwise carried by a belt 152, and may thereby be
worn by patient 12. FIG. 11 also illustrates various sensors 40
that may be coupled to monitor 150 by leads, wires, cables, or
wireless connections, such as EEG electrodes 154A-C placed on the
scalp of patient 12, a plurality of EOG electrodes 156A and 156B
placed proximate to the eyes of patient 12, and one or more EMG
electrodes 158 placed on the chin or jaw the patient. The number
and positions of electrodes 154, 156 and 158 illustrated in FIG. 11
are merely exemplary. For example, although only three EEG
electrodes 154 are illustrated in FIG. 1, an array of between 16
and 25 EEG electrodes 114 may be placed on the scalp of patient 12,
as is known in the art. EEG electrodes 154 may be individually
placed on patient 12, or integrated within a cap or hair net worn
by the patient.
[0138] In the illustrated example, patient 12 wears an ECG belt
160. ECG belt 160 incorporates a plurality of electrodes for
sensing the electrical activity of the heart of patient 12. The
heart rate and, in some embodiments, ECG morphology of patient 12
may monitored by monitor 150 based on the signal provided by ECG
belt 160. Examples of suitable belts 160 for sensing the heart rate
of patient 12 are the "M" and "F" heart rate monitor models
commercially available from Polar Electro. In some embodiments,
instead of belt 160, patient 12 may wear of plurality of ECG
electrodes attached, e.g., via adhesive patches, at various
locations on the chest of the patient, as is known in the art. An
ECG signal derived from the signals sensed by such an array of
electrodes may enable both heart rate and ECG morphology
monitoring, as is known in the art.
[0139] As shown in FIG. 11, patient 12 may also wear a respiration
belt 162 that outputs a signal that varies as a function of
respiration of the patient. Respiration belt 162 may be a
plethysmograpy belt, and the signal output by respiration belt 162
may vary as a function of the changes is the thoracic or abdominal
circumference of patient 12 that accompany breathing by the
patient. An example of a suitable belt 162 is the TSD201
Respiratory Effort Transducer commercially available from Biopac
Systems, Inc. Alternatively, respiration belt 162 may incorporate
or be replaced by a plurality of electrodes that direct an
electrical signal through the thorax of the patient, and circuitry
to sense the impedance of the thorax, which varies as a function of
respiration of the patient, based on the signal. In some
embodiments, ECG and respiration belts 160 and 162 may be a common
belt worn by patient 12, and the relative locations of belts 160
and 162 depicted in FIG. 11 are merely exemplary.
[0140] In the example illustrated by FIG. 11, patient 12 also wears
a transducer 164 that outputs a signal as a function of the oxygen
saturation of the blood of patient 12. Transducer 164 may be an
infrared transducer. Transducer 164 may be located on one of the
fingers or earlobes of patient 12. Monitor 150 may additionally or
alternatively include or be coupled to any of the variety of
sensors 40 described above with reference to FIGS. 2 and 3, which
output signals which vary as a function of any one or more of
activity level, posture, heart rate, ECG morphology, respiration
rate, respiratory volume, blood pressure, blood oxygen saturation,
partial pressure of oxygen within blood, partial pressure of oxygen
within cerebrospinal fluid, muscular activity and tone, core
temperature, subcutaneous temperature, arterial blood flow, brain
electrical activity, eye motion, and galvanic skin response, as
described above.
[0141] The invention may also be embodied as a computer-readable
medium that includes instructions to cause a processor to perform
any of the methods described herein. These and other embodiments
are within the scope of the following claims.
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