U.S. patent application number 11/081811 was filed with the patent office on 2005-09-29 for collecting sleep quality information via a medical device.
Invention is credited to Heruth, Kenneth T., Miesel, Keith A..
Application Number | 20050215847 11/081811 |
Document ID | / |
Family ID | 34962791 |
Filed Date | 2005-09-29 |
United States Patent
Application |
20050215847 |
Kind Code |
A1 |
Heruth, Kenneth T. ; et
al. |
September 29, 2005 |
Collecting sleep quality information via a medical device
Abstract
At least one of a medical device, such as an implantable medical
device, a monitor, and a computing device determines values for one
or more metrics that indicate the quality of a patient's sleep.
Sleep efficiency, sleep latency, and time spent in deeper sleep
states are example sleep quality metrics for which values may be
determined. In some embodiments, determined sleep quality metric
values are associated with a current therapy parameter set. In some
embodiments, a programming device presents sleep quality
information to a user based on determined sleep quality metric
values values. A clinician, for example, may use the sleep quality
information presented by the programming device to evaluate the
effectiveness of therapy delivered to the patient by the medical
device, to adjust the therapy delivered by the medical device, or
to prescribe a therapy not delivered by the medical device in order
to improve the quality of the patient's sleep.
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: |
34962791 |
Appl. No.: |
11/081811 |
Filed: |
March 16, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11081811 |
Mar 16, 2005 |
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10826925 |
Apr 15, 2004 |
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60553783 |
Mar 16, 2004 |
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Current U.S.
Class: |
600/26 ; 600/509;
600/544; 600/549 |
Current CPC
Class: |
A61N 1/3702 20130101;
A61N 1/36071 20130101; A61M 5/1723 20130101; A61B 5/6826 20130101;
A61B 5/1116 20130101; A61N 1/36557 20130101; A61N 1/36078 20130101;
A61B 5/4815 20130101; A61N 1/36542 20130101; A61M 5/14276 20130101;
A61B 3/113 20130101; A61N 1/3614 20170801; A61N 1/36521 20130101;
A61N 1/37247 20130101; A61B 5/686 20130101; A61B 5/1118 20130101;
A61N 1/36514 20130101; A61B 5/318 20210101; A61N 1/36139 20130101;
A61B 5/369 20210101; A61B 5/01 20130101 |
Class at
Publication: |
600/026 ;
600/544; 600/549; 600/509 |
International
Class: |
A61M 021/00; A61B
005/00; A61B 005/04 |
Claims
1. A method comprising: monitoring at least one physiological
parameter of a patient; determining a value of a metric that is
indicative of sleep quality based on the at least one physiological
parameter; identifying a current therapy parameter used by a
medical device to deliver a therapy to a patient; and associating
the sleep quality metric value with the current therapy parameter
set.
2. The method of claim 1, wherein monitoring at least one
physiological parameter comprises monitoring at least one of
electrocardiogram morphology, subcutaneous temperature, muscular
tone, electrical activity of a brain of the patient or eye
motion.
3. A medical system comprising: a medical device that delivers a
therapy to a patient; a monitor that monitors at least one
physiological parameter of the patient based on a signal received
from at least one sensor; and a processor that determines a value
of a metric that is indicative of sleep quality based on the at
least one physiological parameter, identifies a current therapy
parameter set used by the medical device to deliver the therapy to
the patient, and associates the sleep quality metric value with the
current therapy parameter set.
4. The medical system of claim 3, wherein the monitor monitors at
least one of electrocardiogram morphology, subcutaneous
temperature, muscular tone, electrical activity within a brain of
the patient or eye motion.
5. The medical system of claim 3, further comprising a computing
device to present sleep quality information to a user based on the
sleep quality metric values determined by the processor.
6. The medical system of claim 5, wherein the user comprises a
clinician, and the computing device comprises a clinician
programmer that presents at least one of a trend diagram, a
histogram or a pie chart to the clinician based on the sleep
quality metric values.
7. The medical system of claim 5, wherein the user comprises a
patient, and the computing device comprises a patient programmer
that presents a message related to sleep quality to the patient
based on the sleep quality metric values.
8. The medical system of claim 3, wherein the processor determines
a plurality of values of the sleep quality metric over time, and
associates each of the determined values of the sleep quality
metric with a current therapy parameter set, and wherein, for each
of a plurality of therapy parameter sets, the processor determines
a representative value of the sleep quality metric based on the
values of the sleep quality metric associated with the therapy
parameter set, the system further comprising a computing device
that presents a list of the therapy parameter sets and the
associated representative values to a user, and orders the list of
therapy parameter sets according to the associated representative
values.
9. The medical system of claim 3, wherein the processor determines
a plurality of values over time for each of a plurality of metrics
that are indicative of sleep quality, and associates each of the
determined values with a current therapy parameter set, and
wherein, for each of the plurality of therapy parameter sets, the
processor determines a representative value for each of the sleep
quality metrics based on the values of that sleep quality metric
associated with the therapy parameter set, the system further
comprising a computing device that presents a list of the therapy
parameter sets and the associated representative values to a user,
and orders the list of therapy parameter sets according to the
representative values of a user-selected one of the sleep quality
metrics.
10. The medical system of claim 3, wherein the processor comprises
a processor of at least one of the medical device and the
monitor.
11. The medical system of claim 3, further comprising a computing
device, wherein the processor comprises a processor of the
computing device.
12. The medical system of claim 3, wherein the medical device
comprises the monitor.
13. A method comprising: monitoring a plurality of signals, each of
the signals generated by a sensor as a function of at least one
physiological parameter of a patient; identifying when the patient
is attempting to sleep; identifying when the patient is asleep
based on at least one of the signals; and determining a value of a
metric that is indicative of sleep quality based on the
identifications of when the patient is attempting to sleep and
asleep, wherein identifying when the patient is asleep comprises
identifying when the patient is asleep based on at least one of
electrocardiogram morphology, subcutaneous temperature, muscular
tone, electrical activity within a brain of the patient or eye
motion.
14. A medical system comprising: a plurality of sensors, each of
the sensors generating a signal as a function of at least one
physiological parameter of a patient; and a processor that monitors
the signals generated by the sensors, identifies when the patient
is attempting to sleep, identifies when the patient is asleep based
on at least one of the signals, and determines a value of a metric
that is indicative of sleep quality based on the identifications of
when the patient is attempting to sleep and asleep, wherein the
processor identifies when the patient is asleep based on at least
one of electrocardiogram morphology, subcutaneous temperature,
muscular tone, electrical activity within a brain of the patient or
eye motion.
15. A medical system comprising: a plurality of sensors, each of
the sensors generating a signal as a function of at least one
physiological parameter of a patient; a computing device that
monitors the signals generated by the sensors, identifies when the
patient is attempting to sleep, identifies when the patient is
asleep based on at least one of the signals, and determines a value
of a metric that is indicative of sleep quality based on the
identifications of when the patient is attempting to sleep and
asleep.
16. The medical system of claim 15, wherein the computing device
comprises a programming device.
17. The medical system of claim 15, wherein the computing device
receives the signals generated by the sensors via a network.
18. The medical system of claim 17, wherein the computing device
comprises a server.
Description
[0001] This application is a continuation-in-part of U.S.
application Ser. No. 10/826,925, filed Apr. 15, 2004, which claims
the benefit of U.S. Provisional Application No. 60/553,783, 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 that a patient has may affect the
quality of the patient's sleep. For example, chronic pain may cause
a patient to have difficulty falling asleep, and may disturb the
patient's sleep, e.g., cause the patient to wake. Further, chronic
pain may cause the patient to have difficulty achieving deeper
sleep states, such as one or more of the nonrapid eye movement
(NREM) sleep states. Other ailments that may negatively affect
patient sleep quality include movement disorders, psychological
disorders, sleep apnea, congestive heart failure, gastrointestinal
disorders and incontinence. 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.
SUMMARY
[0005] In general, the invention is directed to techniques for
collecting information that relates to the quality of patient sleep
via a medical device, such as an implantable medical device (IMD).
In particular, values for one or more metrics that indicate the
quality of the patient's sleep are determined based on
physiological parameters monitored by a medical device. In some
embodiments, sleep quality information is presented to a user based
on the sleep quality metric values. A clinician, for example, may
use the presented sleep quality information to evaluate the
effectiveness of therapy delivered to the patient by the medical
device, to adjust the therapy delivered by the medical device, or
to prescribe a therapy not delivered by the medical device in order
to improve the quality of the patient's sleep.
[0006] The medical device that delivers the therapy or a separate
monitoring device monitors one or more physiological parameters of
the patient. Example physiological parameters that the medical
device may monitor 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, melatonin level
within one or more bodily fluids, brain electrical activity, eye
motion, and galvanic skin response. In order to monitor one or more
of these parameters, the medical device or monitoring 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.
[0007] The medical device or monitoring device may determine a
value of one or more sleep quality metrics based on the one or more
monitored physiological parameters, and/or the variability of one
or more of the monitored physiological parameters. In other
embodiments, one or both of the medical device or monitoring device
records values of the one or more physiological parameters, and
provides the physiological parameter values to a programming
device, such as a clinician programming device or a patient
programming device, or another computing device. In such
embodiments, the programming or other computing device determines
values of one or more sleep quality metrics based on the
physiological parameter values received from the medical device
and/or the variability of one or more of the physiological
parameters. The medical device or monitoring device may provide the
recorded physiological parameter values to the programming or other
computing device in real time, or may provide physiological
parameter values recorded over a period of time to the programming
or other computing device when interrogated.
[0008] Sleep efficiency and sleep latency are example sleep quality
metrics for which a medical device or programming 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. 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.
[0009] The time when the patient begins attempting to fall asleep
may be determined in a variety of ways. For example, the patient
may provide an indication that he or she is trying to fall asleep,
e.g., via a patient programming device. In other embodiments, the
medical device or monitoring may monitor the activity level of the
patient, and the time when the patient is attempting to fall asleep
may be identified 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 medical device or monitoring device may monitor patient
posture, and the medical device or a programming device may
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 medical device or monitoring device may also
monitor patient activity, and either the medical device, monitoring
device, programming device, or other computing device may confirm
that the patient is attempting to sleep based on the patient's
activity level.
[0010] As another example, the medical device or monitoring 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 medical device or monitoring
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 medical device or monitoring
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.
[0011] The time at which the patient has fallen asleep may be
determined based on the activity level of the patient and/or one or
more of the other physiological parameters that may be monitored by
the medical device as indicated above. For example, a discernable
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, a
sleep probability metric value may be determined based on a value
of a physiological parameter monitored by the medical device. In
such embodiments, the sleep probability metric value may be
compared to a threshold to identify when the patient has fallen
asleep. In some embodiments, a plurality of sleep probability
metric values are determined based on a value of each of a
plurality of physiological parameters, the sleep probability values
are averaged or otherwise combined to provide an overall sleep
probability metric value, and the overall sleep probability metric
value is compared to a threshold to identify the time that the
patient falls asleep.
[0012] Other sleep quality metrics that may be determined 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,
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) may be determined based on physiological parameters
monitored by the medical device, and the amount of time per day
spent in these various sleep states may be 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, average or median values of one or more sleep quality
metrics over greater periods of time, e.g., a week or a month, may
be determined as the value of the sleep quality metric. Further, in
embodiments in which values for a plurality of the sleep quality
metrics are determined, a value for an overall sleep quality metric
may be determined based on the values for the plurality of
individual sleep quality metrics.
[0013] In some embodiments, the medical device delivers a therapy.
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.
[0014] In embodiments in which the medical device determines sleep
quality metric values, the medical device may identify the current
therapy parameter set when a value of one or more sleep quality
metrics is collected, and may associate that value with the therapy
parameter set. For example, 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 programs 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. In other embodiments in which a programming device
or other computing device determines sleep quality metric values,
the medical device may associate recorded physiological parameter
values with the current therapy parameter set in the memory.
Further, in embodiments in which a separate monitoring device
records physiological parameter values or determines sleep quality
metric values, the monitoring device may mark recorded
physiological parameter values 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 or sleep quality
metrics 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 or sleep quality metrics with the therapy parameter set that
was delivered by the medical device when the physiological
parameter values or sleep quality metrics were collected.
[0015] A programming device or other computing device according to
the invention may be capable of wireless communication with the
medical device, and may receive sleep quality metric values or
recorded physiological parameter values from the medical device or
a separate monitoring device. In either case, when the computing
device either receives or determines sleep quality metric values,
the computing device may provide sleep quality information to a
user based on the sleep quality metric values. For example, the
computing device may be a patient programmer, and may provide a
message to the patient related to sleep quality. The patient
programmer may, for example, suggest that the patient visit a
clinician for prescription of sleep medication or for an adjustment
to the therapy delivered by the medical device. As other examples,
the patient programmer may suggest that the patient increase the
intensity of therapy delivered by the medical device during
nighttime hours relative to previous nights, or select a different
therapy parameter set for use during sleep than the patient had
selected during previous nights. Further, the patient programmer
may provide a message that indicates the quality of sleep to the
patient to, for example, provide the patient with an objective
indication of whether his or her sleep quality is good, adequate,
or poor.
[0016] In other embodiments, the computing device is a clinician
programmer that presents information relating to the quality of the
patient's sleep to a clinician. The clinician programmer may
present, for example, a trend diagram of values of one or more
sleep quality metrics over time. As other examples, the clinician
programmer may present a histogram or pie chart illustrating
percentages of time that a sleep quality metric was within various
value ranges.
[0017] As indicated above, the computing device may receive
representative values for one or more sleep quality metrics or the
physiological parameter values from the therapy delivering medical
device or separate monitoring device. The computing device may
receive information identifying the therapy parameter set with
which the representative values are associated, or may itself
associate received physiological parameter or sleep quality metric
values with therapy parameter sets based on time information
received from one or more devices. In embodiments in which the
computing device receives physiological parameter values, the
computing device may determine sleep quality metric values
associated with the plurality of parameter sets based on the
physiological parameter values, and representative sleep quality
metric values for each of the therapy parameter sets based on the
sleep quality metric values associated with the therapy parameter
sets. In some embodiments, the computing device may determine the
variability of one or more of the physiological parameters based on
the physiological parameter values received from the medical device
or monitoring device, and may determine sleep quality metric values
based on the physiological parameter variabilities.
[0018] The computing device may display a list of the therapy
parameter sets to the clinician ordered according to their
associated representative sleep quality metric values. Such a list
may be used by the clinician to identify effective or ineffective
therapy parameter sets. Where a plurality of sleep quality metric
values are determined, the programming device may order the list
according to values of a user-selected one of the sleep quality
metrics.
[0019] In other embodiments, a system according to the invention
does not include a programming or other computing device. For
example, an external medical device according to the invention may
include a display, determine sleep quality metric values, and
display sleep quality information to a user via the display based
on the sleep quality metric values.
[0020] In one embodiment, the invention is directed to a method in
which at least one physiological parameter of a patient is
monitored. A value of a metric that is indicative of sleep quality
is determined based on the at least one physiological parameter. A
current therapy parameter set used by a medical device to delivery
a therapy to the patient is identified, and the sleep quality
metric value is associated with the current therapy parameter
set.
[0021] In another embodiment, the invention is directed to a
medical system comprising a medical device, and monitor, and a
processor. The medical device delivers a therapy to a patient, and
the monitor monitors at least one physiological parameter of a
patient based on a signal received from at least one sensor. The
processor determines a value of a metric that is indicative of
sleep quality based on the at least one physiological parameter,
identifies a current therapy parameter set, and associates the
sleep quality metric value with the current therapy parameter set.
The medical device may comprise the monitor.
[0022] In another embodiment, the invention is directed to a
medical system comprising means for monitoring at least one
physiological parameter of a patient, means for determining a value
of a metric that is indicative of sleep quality based on the at
least one physiological parameter, means for identifying a current
therapy parameter set used by a medical device to delivery therapy
to the patient, and means for associating the sleep quality metric
value with the current therapy parameter set.
[0023] In another embodiment, the invention is directed to a
medical system comprising an implantable medical device and an
external programming device including a display. The implantable
medical device delivers a therapy to a patient, monitors at least
one physiological parameter of the patient, and determines a
plurality of values of a metric that is indicative of sleep quality
based on the at least one physiological parameter. The external
programming device receives sleep quality metric values from the
implantable medical device, and presents sleep quality information
to a user via the display based on the sleep quality metric
values.
[0024] In another embodiment, the invention is directed to a
programming device comprising a telemetry circuit, a user interface
including a display, and a processor. The processor receives sleep
quality metric values from a medical device via the telemetry
circuit. The medical device delivers therapy according to a
plurality of therapy parameter sets, and associates each of the
sleep quality metric values with a current therapy parameter set.
The processor receives information identifying the plurality of
therapy parameter sets and the sleep quality metric values
associated with the therapy parameter sets from the medical device,
presents a list of the therapy parameter sets and the associated
sleep quality metric values to a user, and orders the list
according to the associated sleep quality metric values.
[0025] In another embodiment, the invention is directed to a
computer-readable medium comprising program instructions. The
program instructions cause a programmable processor to receive
sleep quality metric values from a medical device that delivers
therapy according to a plurality of parameter sets, and associates
each of the sleep quality metric values with a current therapy
parameter set. The instructions further cause a processor to
receive information identifying the plurality of therapy parameter
sets and the sleep quality metric values associated with the
therapy parameter sets from the medical device, present a list of
the therapy parameter sets and the associated sleep quality metric
values to a user, and order the list of therapy parameter sets
according to the associated sleep quality metric values.
[0026] In another embodiment, the invention is directed to a method
in which a plurality of signals are monitored, each of the signals
generated by a sensor as a function of at least one physiological
parameter of a patient. When the patient is attempting to sleep is
identified. When the patient is asleep is identified based on at
least one of the signals. A value of a metric that is indicative of
sleep quality is determined based on the identifications of when
the patient is attempting to sleep and asleep.
[0027] In another embodiment, the invention is directed to a
medical system comprising a plurality of sensors and a processor.
Each of the plurality of sensors generates a signal as a function
of at least one physiological parameter of a patient. The processor
monitors the signals generated by the sensors, identifies when the
patient is attempting to sleep, identifies when the patient is
asleep based on at least one of the signals, and determines a value
of a metric that is indicative of sleep quality based on the
identifications of when the patient is attempting to sleep and
asleep.
[0028] The invention may be capable of providing one or more
advantages. For example, by providing information related to 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. Using
the sleep quality information provided by the system, the clinician
and/or patient can, for example, make changes to the therapy
provided by a medical device in order to better address symptoms
which are disturbing the patient's sleep. Further, a clinician may
choose to prescribe a therapy that will improve the patient's
sleep, such as a sleep inducing medication, in situations where
poor sleep quality is increasing symptoms experienced by the
patient.
[0029] 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
[0030] FIG. 1 is a conceptual diagram illustrating an example
system that includes an implantable medical device that collects
sleep quality information according to the invention.
[0031] FIG. 2 is a block diagram further illustrating the example
system and implantable medical device of FIG. 1.
[0032] FIG. 3 is a block diagram illustrating an example memory of
the implantable medical device of FIG. 1.
[0033] FIG. 4 is a flow diagram illustrating an example method for
collecting sleep quality information that may be employed by an
implantable medical device.
[0034] FIG. 5 is a flow diagram illustrating an example method for
associating sleep quality information with therapy parameter sets
that may be employed by an implantable medical device.
[0035] FIG. 6 is a block diagram illustrating an example clinician
programmer.
[0036] FIG. 7 is a flow diagram illustrating an example method for
presenting sleep quality information to a clinician that may be
employed by a clinician programmer.
[0037] FIG. 8 illustrates an example list of therapy parameter sets
and associated sleep quality information that may be presented by a
clinician programmer.
[0038] FIG. 9 is a flow diagram illustrating an example method for
displaying a list of therapy parameter sets and associated sleep
quality information that may be employed by a clinician
programmer.
[0039] FIG. 10 is a block diagram illustrating an example patient
programmer.
[0040] FIG. 11 is a flow diagram illustrating an example method for
presenting a sleep quality message to a patient that may be
employed by a patient programmer.
[0041] FIG. 12 is a conceptual diagram illustrating a monitor that
monitors values of one or more physiological parameters of the
patient instead of, or in addition to, a therapy delivering medical
device.
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 according to the invention. Sleep quality
information collected by IMD 14 is provided to a user, such as a
clinician or the patient. Using the sleep quality 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.
Further, the invention is not limited to implementation via an IMD.
In other words, any implantable or external medical device may
collect sleep quality 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 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 in each 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) 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 relating to
the quality of sleep experienced by patient 12. Specifically, as
will be described in greater detail below, IMD 14 monitors one or
more physiological parameters of patient 12, and determines values
for one or more metrics that indicate the quality of sleep based on
values of the physiological parameters. Example physiological
parameters that IMD 14 may monitor include 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
(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.
In some external medical device embodiments of the invention,
galvanic skin response may additionally or alternatively be
monitored. Further, in some embodiments, IMD 14 additionally or
alternatively monitors the variability of one or more of these
parameters. In order to monitor one or more of these parameters,
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.
[0054] For example, IMD 14 may determine sleep efficiency and/or
sleep latency values. Sleep efficiency and sleep latency are
example sleep quality metrics. IMD 14 may measure sleep efficiency
as the percentage of time while patient 12 is attempting to sleep
that patient 12 is actually asleep. IMD 14 may measure sleep
latency as the amount of time between a first time when patient 12
begins attempting to fall asleep and a second time when patient 12
falls asleep.
[0055] IMD 14 may identify the time at which patient begins
attempting to fall asleep in a variety of ways. For example, IMD 14
may receive an indication from the patient that the patient is
trying to fall asleep via patient programmer 26. In other
embodiments, IMD 14 may monitor the activity level of patient 12,
and identify the time when patient 12 is attempting to fall asleep
by determining whether patient 12 has remained inactive for a
threshold period of time, and identifying the time at which patient
12 became inactive. In still other embodiments, IMD 14 may monitor
the posture of patient 12, and may identify the time when the
patient 12 becomes recumbent, e.g., lies down, as the time when
patient 12 is attempting to fall asleep. In these embodiments, IMD
14 may also monitor the activity level of patient 12, and confirm
that patient 12 is attempting to sleep based on the activity
level.
[0056] As another example, IMD 14 may determine the time at which
patient 12 is attempting to fall asleep based on the level of
melatonin within one or more bodily fluids of patient 12, such as
the patient's blood, cerebrospinal fluid (CSF), or interstitial
fluid. IMD 14 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 patient 12, which, in turn, may
cause patient 12 to attempt to fall asleep.
[0057] IMD 14 may, for example, detect an increase in the level of
melatonin in a bodily fluid, and estimate the time that patient 12
will attempt to fall asleep based on the detection. For example,
IMD 14 may compare the melatonin level or rate of change in the
melatonin level to a threshold level, and identify the time that
threshold value is exceeded. IMD 14 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.
[0058] IMD 14 may identify the time at which patient 12 has fallen
asleep based on the activity level of the patient and/or one or
more of the other physiological parameters that may be monitored by
IMD 14 as indicated above. For example, IMD 14 may identify a
discernable change, e.g., a decrease, in one or more physiological
parameters, or the variability of one or more physiological
parameters, which may indicate that patient 12 has fallen asleep.
In some embodiments, IMD 14 determines a sleep probability metric
value based on a value of a physiological parameter monitored by
the medical device. In such embodiments, the sleep probability
metric value may be compared to a threshold to identify when the
patient has fallen asleep. In some embodiments, a sleep probability
metric value is determined based on a value of each of a plurality
of physiological parameters, the sleep probability values are
averaged or otherwise combined to provide an overall sleep
probability metric value, and the overall sleep probability metric
value is compared to a threshold to identify the time that the
patient falls asleep.
[0059] Other sleep quality metrics include total time sleeping per
day, and the amount or percentage of time sleeping during nighttime
or daytime hours per day. In some embodiments, IMD 14 may be able
to detect arousal events and apneas occurring during sleep based on
one or more monitored physiological parameters, and the number of
apnea and/or arousal events per night may be determined as a sleep
quality metric. Further, in some embodiments IMD 14 may be able to
determine which sleep state patient 12 is in based on one or more
monitored physiological parameters, e.g., rapid eye movement (REM),
S1, S2, S3, or S4, and the amount of time per day spent in these
various sleep states may be a sleep quality metric.
[0060] 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.
[0061] 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.
[0062] In some embodiments, IMD 14 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 IMD 14 collects values for
a plurality of the sleep quality metrics identified above, IMD 14
may determine a value for an overall sleep quality metric based on
the collected values for the plurality of sleep quality metrics.
IMD 14 may determine the value of an overall sleep quality metric
by applying a function or look-up table to a plurality of sleep
quality metric values, which may also include the application of
weighting factors to one or more of the individual sleep quality
metric values.
[0063] In some embodiments, IMD 14 may identify the current set of
therapy parameters when a value of one or more sleep quality
metrics is collected, and may associate that value with the current
therapy parameter sets. For example, for each of a plurality
therapy parameter sets used over time by IMD 14 to deliver therapy
to patient 12, 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 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.
[0064] One or both of programmers 20, 26 may receive sleep quality
metric values from IMD 14, and may provide sleep quality
information to a user based on the sleep quality metric values. For
example, patient programmer 26 may provide a message to patient 12,
e.g., via display 28, related to sleep quality based on received
sleep quality metric values. Patient programmer 26 may, for
example, suggest that patient 12 visit a clinician for prescription
of sleep medication or for an adjustment to the therapy delivered
by IMD 14. As other examples, patient programmer 26 may suggest
that patient 12 increase the intensity of therapy delivered by IMD
14 during nighttime hours relative to previous nights, or select a
different therapy parameter set for use by IMD 14 than the patient
had selected during previous nights. Further, patient programmer 26
may report the quality of the patient's sleep to patient 12 to, for
example, provide patient 12 with an objective indication of whether
his or her sleep quality is good, adequate, or poor.
[0065] Clinician programmer 20 may receive sleep quality metric
values from IMD 14, and present a variety of types of sleep
information to a clinician, e.g., via display 22, based on the
sleep quality metric values. For example, clinician programmer 20
may present a graphical representation of the sleep quality metric
values, such as a trend diagram of values of one or more sleep
quality metrics over time, or a histogram or pie chart illustrating
percentages of time that a sleep quality metric was within various
value ranges.
[0066] In embodiments in which IMD 14 associates sleep quality
metric values with therapy parameter sets, clinician programmer 20
may receive representative values for one or more sleep quality
metrics from IMD 14 and information identifying the therapy
parameter sets with which the representative values are associated.
Using this information, clinician programmer 20 may display a list
of the therapy parameter sets to the clinician ordered according to
their associated representative sleep quality metric values. The
clinician may use such a list to identify effective or ineffective
therapy parameter sets. Where a plurality of sleep quality metric
values are collected, clinician programmer 20 may order the list
according to values of a user-selected one of the sleep quality
metrics. In this manner, the clinician may quickly identify the
therapy parameter sets producing the best results in terms of sleep
quality.
[0067] 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 the signals to
determine values for one or more metrics that are indicative of
sleep quality.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] As discussed above, exemplary physiological parameters of
patient 12 that may be monitored by IMD 14 to determine values of
one or more sleep quality metrics include 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, the level of melatonin within a
bodily fluid of patient 12, electrical activity of the brain of the
patient, 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.
[0074] In some embodiments, in order to determine one or more sleep
quality metric values, processor 46 determines when patient 12 is
attempting to fall asleep. 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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, chest, 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] Processor 46 may also determine when patient 12 is asleep,
e.g., identify the times that patient 12 falls asleep and wakes up,
in order to determine one or more sleep quality metric values. 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 wakes up. 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.
[0086] 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 discernable 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.
[0087] 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.
[0088] 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," which was assigned Attorney Docket No. 1023-360US02 and
filed on Mar. 16, 2005, and is incorporated herein by reference in
its entirety.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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. The
thoracic impedance signal varies as a function of respiration by
patient 12. In other embodiments, sensors 40 may include a strain
gage, 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.
[0093] 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 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, Minn. 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.
[0094] 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.
Embodiments in which an IMD comprises an implantable pump, for
example, may include a catheter with a distal portion located in
the cerebrospinal fluid.
[0095] 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.
[0096] Additionally, in some embodiments, sensors 40 may include
one or more electrodes positioned within or proximate to the brain
of patient 12, which detect electrical activity of the brain. For
example, in embodiments in which IMD 14 delivers stimulation or
therapeutic agents 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.
[0101] 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. As discussed above, inadequate time spent
in deeper sleep states, e.g., S3 and S4, is an indicator of poor
sleep quality. Consequently, in some embodiments, processor 46 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.
[0102] 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 via
patient programmer 26.
[0103] Memory 48 may also include parameter information 62 recorded
by processor 46, e.g., physiological parameter values, or mean or
median physiological parameter values. Memory 48 stores threshold
values 64 used by processor 46 in the collection of sleep quality
metric values, as discussed above. 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,
or to determine an overall sleep quality metric value.
[0104] Further, processor 46 stores determined values 66 for one or
more sleep quality metrics within memory 48. Processor 46 may
collect sleep quality metric values 66 each time patient 12 sleeps,
or only during selected times that patient 12 is asleep. 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
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.
[0105] In some embodiments, processor 46 identifies which of
therapy parameter sets 60 is currently selected for use in
delivering therapy to patient 12 when a value of one or more sleep
quality metrics is collected, and may associate that value 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 which of the therapy parameter sets that
representative value is associated with. 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.
[0106] 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 from processor 46 via
telemetry circuit 50.
[0107] FIG. 4 is a flow diagram illustrating an example method for
collecting sleep quality 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, melatonin level, and/or a patient indication, as
described above (72). If IMD 14 determines that patient 12 is
attempting to fall asleep, IMD 14 identifies the time that patient
12 began attempting to fall asleep using any of the techniques
described above (74), and monitors one or more of the various
physiological parameters of patient 12 discussed above to determine
whether patient 12 is asleep (76, 78).
[0108] 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 (76) to
determine whether patient 12 is asleep (78), IMD 14 may continue to
monitor the posture and/or activity level of patient 12 (70) to
confirm that patient 12 is still attempting to fall asleep
(72).
[0109] 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 (80). While patient
12 is sleeping, IMD 14 will continue to monitor physiological
parameters of patient 12 (82). As discussed above, IMD 14 may
identify the occurrence of arousals and/or apneas based on the
monitored physiological parameters (84). 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 (84).
[0110] Additionally, while patient 12 is sleeping, IMD 14 monitors
physiological parameters of patient 12 (82) to determine whether
patient 12 has woken up (86). When IMD 14 determines that patient
12 is awake, IMD 14 identifies the time that patient 12 awoke (88),
and determines sleep quality metric values based on the information
collected while patient 12 was asleep (90).
[0111] 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 14 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.
[0112] 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.,
one or both of the S3 and S4 sleep states. IMD 14 may store the
determined values as sleep quality metric values 66 within memory
48.
[0113] IMD 14 may perform the example method illustrated in FIG. 4
continuously, e.g., may monitor to identify when patient 12 is
attempting to sleep and asleep any time of day, each day. In other
embodiments, IMD 14 may only perform the method during evening
hours and/or once every N days to conserve battery and memory
resources. Further, in some embodiments, IMD 14 may only perform
the method in response to receiving a command from patient 12 or a
clinician via one of programmers 20, 26. For example, patient 12
may direct IMD 14 to collect sleep quality information at times
when the patient believes that his or her sleep quality is low or
therapy is ineffective.
[0114] FIG. 5 is a flow diagram illustrating an example method for
associating sleep quality information with therapy parameter sets
60 that may be employed by IMD 14. IMD 14 determines a value of a
sleep quality metric according to any of the techniques described
above (100). IMD 14 also identifies the current therapy parameter
set, e.g., the therapy parameter set 60 used by IMD 14 to control
delivery of therapy when patient 12 was asleep (102), and
associates the newly determined value with the current therapy
parameter set 60.
[0115] 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. IMD 14 updates the representative values for the
current therapy parameter set based on the newly determined value
of the sleep quality metric. 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.
[0116] FIG. 6 is a block diagram further illustrating clinician
programmer 20. A clinician may interact with a processor 110 via a
user interface 112 in order to program therapy for patient 12.
Further, processor 110 may receive sleep quality metric values 66
from IMD 14 via a telemetry circuit 114, and may generate sleep
quality information for presentation to the clinician via user
interface 112. 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.
[0117] 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.
[0118] FIG. 7 is a flow diagram illustrating an example method for
presenting sleep quality information to a clinician that may be
employed by clinician programmer 20. Clinician programmer 20
receives sleep quality metric values 66 from IMD 14, e.g., via
telemetry circuit 114 (120). The sleep quality metric values 66 may
be daily values, or mean or median values determined over greater
periods of time, e.g., weeks or months.
[0119] Clinician programmer 20 may simply present the values to the
clinician via display 22 in any form, such as a table of average
values, or clinician programmer 20 may generate a graphical
representation of the sleep quality metric values (122). For
example, clinician programmer 20 may generate a trend diagram
illustrating sleep quality metric values 66 over time, or a
histogram, pie chart, or other graphic illustration of percentages
of sleep quality metric values 66 collected by IMD 14 that were
within ranges. Where clinician programmer 20 generates a graphical
representation of the sleep quality metric values 66, clinician
programmer 20 presents the graphical representation to the
clinician via display 22 (124).
[0120] FIG. 8 illustrates an example list 130 of therapy parameter
sets and associated sleep quality metric values 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, and a representative
value for one or more sleep quality metrics associated with the
identified therapy parameter set, such as sleep efficiency, sleep
latency, or both. The example list 130 includes representative
values for sleep efficiency, sleep latency, and "deep sleep," e.g.,
the average amount of time per night spent in either of the S3 and
S4 sleep states.
[0121] FIG. 9 is a flow diagram illustrating an example method for
displaying a list 130 of therapy parameter sets and associated
sleep quality 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, and one or
more representative sleep quality metric values associated with
each of the therapy parameter sets (140). Clinician programmer 20
generates a list 130 of the therapy parameter sets 60 and any
associated representative sleep quality metric values (142), and
orders the list according to a selected sleep quality metric (144).
For example, in the example list 130 illustrated in FIG. 8, the
clinician may select whether list 130 should be ordered according
to sleep efficiency or sleep latency via user interface 112 of
clinician programmer 20.
[0122] FIG. 10 is a block diagram further illustrating patient
programmer 26. Patient 12 may interact with a processor 150 via a
user interface 152 in order to control delivery of therapy, i.e.,
select or adjust one or more of therapy parameter sets 60 stored by
IMD 14. Processor 150 may also receive sleep quality metric values
66 from IMD 14 via a telemetry circuit 154, and may provide
messages related to sleep quality to patient 12 via user interface
152 based on the received values. User interface 152 may include
display 28 and keypad 30, and may also include a touch screen or
peripheral pointing devices as described above.
[0123] In some embodiments, processor 150 may determine whether to
provide a message related to sleep quality to patient 12 based on
the received sleep quality metric values. For example, processor
150 may periodically receive sleep quality metric values 66 from
IMD 14 when placed in telecommunicative communication with IMD 14
by patient 12, e.g., for therapy selection or adjustment. Processor
150 may compare these values to one or more thresholds 156 stored
in a memory 158 to determine whether the quality of the patient's
sleep is poor enough to warrant a message.
[0124] Processor 150 may present messages to patient 12 as text via
display, and/or as audio via speakers included as part of user
interface 152. The message may, for example, direct patient 12 to
see a physician, increase therapy intensity before sleeping, or
select a different therapy parameter set before sleeping than the
patient had typically selected previously. In some embodiments, the
message may indicate the quality of sleep to patient 12 to, for
example, provide patient 12 with an objective indication of whether
his or her sleep quality is good, adequate, or poor. Further, in
some embodiments processor 150 may, like clinician programmer 20,
receive representative sleep quality metric values. In such
embodiments, processor 150 may identify a particular one or more of
therapy parameter sets 60 to recommend to patient 12 based on
representative sleep quality metric values associated with those
programs.
[0125] Processor 150 may include a microprocessor, a controller, a
DSP, an ASIC, an FPGA, discrete logic circuitry, or the like.
Memory 158 may also include program instructions that, when
executed by processor 150, cause patient programmer 26 to perform
the functions ascribed to patient programmer 26 herein. Memory 158
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. 11 is a flow diagram illustrating an example method for
presenting a sleep quality message to patient 12 that may be
employed by patient programmer 26. According to the illustrated
example method, patient programmer 26 receives a sleep quality
metric value from IMD 14 (160), and compares the value to a
threshold value 156 (162). Patient programmer 26 determines whether
the comparison indicates poor sleep quality (164). If the
comparison indicates that the quality of sleep experienced by
patient 12 is poor, patient programmer 26 presents a message
related to sleep quality to patient 12 (166).
[0127] 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, 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.
[0128] 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. In some embodiments, the
medical device is an external medical device, and may itself
include a display to present information to a user.
[0129] 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 collected by 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. In particular, a trial neurostimulator or
pump may determine representative values of one or more sleep
quality metrics for each of a plurality of prospective therapy
parameter sets, and a computing device, such as 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.
[0130] Further, the invention is not limited to embodiments in
which an implantable or external medical device that delivers
therapy to a patient determines sleep quality metric values.
Instead a medical device according to the invention may record
values for one or more physiological parameters, and provide the
physiological parameter values to a computing device, such as one
or both of programmers 20, 26. In such embodiments, the computing
device, and more particularly a processor of the computing device,
e.g., processors 110, 150, employs any of the techniques described
herein with reference to IMD 14 in order to determine sleep quality
metric values based on the physiological parameter values received
from the medical device. The computing device may receive
physiological parameter values from the medical device in real
time, or may monitor physiological parameters of the patient by
receiving and analyzing physiological parameter values recorded by
the medical device over a period of time. In some embodiments, in
addition to physiological parameter values, the medical device
provides the computing device information identifying times at
which the patient indicated that he or she was attempting to fall
asleep, which the computing device may use to determine one or more
sleep quality metric values as described herein.
[0131] In some embodiments, the medical device may associate
recorded physiological parameter values with current therapy
parameter sets. The medical device may provide information
indicating the associations of recorded physiological parameter
values and therapy parameter sets to the computing device, e.g.,
programmer 20 or 26. The computing device may determine sleep
quality metric values and representative sleep quality metric
values for each of the plurality of therapy parameter sets based on
the physiological parameter values associated with the therapy
parameter sets, as described herein with reference to IMD 14.
[0132] 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 sensors
40, as illustrated above with reference to IMD 14 and FIGS. 2 and
3. The monitor may identify sleep quality metric values based on
the values of the monitored physiological parameter values, or may
transmit the physiological parameter values to a computing device
for determination of the sleep quality metric values. In some
embodiments, an external computing device, such as a programming
device, may incorporate the monitor.
[0133] FIG. 12 is a conceptual diagram illustrating a monitor 170
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 170 is configured to be
attached to or otherwise carried by a belt 172, and may thereby be
worn by patient 12. FIG. 12 also illustrates various sensors 40
that may be coupled to monitor 170 by leads, wires, cables, or
wireless connections, such as EEG electrodes 174A-C placed on the
scalp of patient 12, a plurality of EOG electrodes 176A and 176B
placed proximate to the eyes of patient 12, and one or more EMG
electrodes 178 placed on the chin or jaw the patient. The number
and positions of electrodes 174, 176 and 178 illustrated in FIG. 12
are merely exemplary. For example, although only three EEG
electrodes 174 are illustrated in FIG. 1, an array of between 16
and 25 EEG electrodes 174 may be placed on the scalp of patient 12,
as is known in the art. EEG electrodes 174 may be individually
placed on patient 12, or integrated within a cap or hair net worn
by the patient.
[0134] In the illustrated example, patient 12 wears an ECG belt
180. ECG belt 180 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 170 based on the signal provided by ECG
belt 180. Examples of suitable belts 180 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 180, 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.
[0135] As shown in FIG. 12, patient 12 may also wear a respiration
belt 182 that outputs a signal that varies as a function of
respiration of the patient. Respiration belt 182 may be a
plethysmograpy belt, and the signal output by respiration belt 182
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 182 is the TSD201
Respiratory Effort Transducer commercially available from Biopac
Systems, Inc. Alternatively, respiration belt 182 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 180 and 182 may be a common
belt worn by patient 12, and the relative locations of belts 180
and 182 depicted in FIG. 12 are merely exemplary.
[0136] In the example illustrated by FIG. 12, patient 12 also wears
a transducer 184 that outputs a signal as a function of the oxygen
saturation of the blood of patient 12. Transducer 184 may be an
infrared transducer. Transducer 184 may be located on one of the
fingers or earlobes of patient 12. Sensors 40 coupled to monitor
170 may additionally or alternatively include any of the variety of
sensors described above that monitor 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.
[0137] 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.
* * * * *