U.S. patent application number 12/248609 was filed with the patent office on 2009-02-05 for sensitivity analysis for selecting therapy parameter sets.
This patent application is currently assigned to Medtronic, Inc.. Invention is credited to Kenneth T. Heruth, Keith A. Miesel.
Application Number | 20090036951 12/248609 |
Document ID | / |
Family ID | 34963077 |
Filed Date | 2009-02-05 |
United States Patent
Application |
20090036951 |
Kind Code |
A1 |
Heruth; Kenneth T. ; et
al. |
February 5, 2009 |
SENSITIVITY ANALYSIS FOR SELECTING THERAPY PARAMETER SETS
Abstract
Techniques for controlling delivery of a therapy to a patient by
a medical device, such as an implantable medical device (IMD),
involve a sensitivity analysis of a performance metric. The
performance metric may relate to efficacy or side effects of the
therapy. For example, the performance metric may comprise a sleep
quality metric, an activity level metric, a movement disorder
metric for patients with Parkinson's disease, or the like. The
sensitivity analysis identifies values of therapy parameters that
defines a substantially maximum or minimum value of the performance
metric. The identified therapy parameters are a baseline therapy
parameter set, and a medical device may control delivery of the
therapy based on the baseline therapy parameter set.
Inventors: |
Heruth; Kenneth T.; (Edina,
MN) ; Miesel; Keith A.; (St. Paul, MN) |
Correspondence
Address: |
SHUMAKER & SIEFFERT , P.A
1625 RADIO DRIVE , SUITE 300
WOODBURY
MN
55125
US
|
Assignee: |
Medtronic, Inc.
|
Family ID: |
34963077 |
Appl. No.: |
12/248609 |
Filed: |
October 9, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11081873 |
Mar 16, 2005 |
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12248609 |
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60553769 |
Mar 16, 2004 |
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Current U.S.
Class: |
607/46 ; 604/66;
607/62 |
Current CPC
Class: |
A61N 1/36557 20130101;
A61B 8/12 20130101; A61M 2230/06 20130101; A61M 2230/63 20130101;
A61B 5/4824 20130101; A61B 5/4815 20130101; A61B 5/4839 20130101;
A61N 1/36135 20130101; A61M 5/1723 20130101; A61B 5/0031 20130101;
A61B 5/0205 20130101; A61N 1/36146 20130101; G16H 40/63 20180101;
A61B 3/113 20130101; A61B 8/06 20130101; A61N 1/36071 20130101;
A61B 5/0215 20130101; A61B 5/389 20210101; A61N 1/36521 20130101;
A61B 5/283 20210101; A61N 1/3614 20170801; A61B 5/11 20130101; A61B
5/1459 20130101; A61N 1/37241 20130101; A61N 1/36542 20130101; G16H
20/40 20180101; A61M 5/14276 20130101 |
Class at
Publication: |
607/46 ; 604/66;
607/62 |
International
Class: |
A61N 1/36 20060101
A61N001/36; A61M 37/00 20060101 A61M037/00 |
Claims
1. A method comprising: delivering a therapy to a patient via a
medical device according to each of a plurality of therapy
parameter sets, each of the parameter sets including a value for
each of a plurality of therapy parameters; monitoring a value of a
performance metric of the patient in response to therapy delivered
according to each of the plurality of therapy parameter sets;
conducting a sensitivity analysis of the performance metric for
each of the plurality of therapy parameters based on the monitored
values of the performance metric; and identifying a baseline value
for each of the therapy parameters based on the sensitivity
analysis to form a baseline therapy parameter set.
2. The method of claim 1, further comprising receiving a range for
at least one therapy parameter in the therapy parameter sets and
generating the plurality of therapy parameter sets with different
values of the therapy parameter distributed over the range.
3. The method of claim 1, wherein identifying a value comprises
identifying a value corresponding to one of a maximum value and a
minimum value of the performance metric.
4. The method of claim 1, further comprising delivering the therapy
to the patient based on a baseline therapy parameter set.
5. The method of claim 4, wherein delivering the therapy to the
patient based on a baseline therapy parameter set comprises
delivering the therapy to the patient via one of the medical device
or another medical device.
6. The method of claim 4, further comprising perturbing at least
one therapy parameter value of the baseline therapy parameter set,
monitoring a perturbed value of the performance metric in response
to the perturbed therapy parameter value, comparing the perturbed
value of the performance metric to the value of the performance
metric for the baseline therapy parameter set and adjusting the
baseline therapy parameter set based on the comparison.
7. The method of claim 1, wherein the performance metric comprises
at least one of an activity level metric, or a posture metric,
8. The method of claim 1, wherein the performance metric comprises
at least one of a movement disorder metric, or a side-effects
metric.
9. The method of claim 1, wherein the performance metric comprises
a patient feedback metric.
10. A medical device comprising: a therapy module to deliver a
therapy to a patient according to each of a plurality of therapy
parameter sets, each of the therapy parameter sets including a
value for each of a plurality of therapy parameters; and a
processor to monitor a value of a performance metric of the patient
in response to therapy delivered according to each of the plurality
of therapy parameter sets, conduct a sensitivity analysis of the
performance metric for each of the plurality of therapy parameters
based on the monitored values of the performance metric, and
identify a baseline value for each of the therapy parameters based
on the sensitivity analysis to form a baseline therapy parameter
set.
11. The medical device of claim 10, further comprising telemetry
circuitry to receive a range for at least one therapy parameter in
the therapy parameter sets, wherein the processor generates the
plurality of therapy parameter sets with different values of the
therapy parameter distributed over the range.
12. The medical device of claim 10, further comprising a memory to
store a range for at least one therapy parameter in the therapy
parameter sets, wherein the processor generates the plurality of
therapy parameter sets with different values of the therapy
parameter distributed over the range and stores the plurality of
therapy parameter sets in the memory.
13. The medical device of claim 10, wherein the processor controls
delivery of the therapy by the therapy module according to a
baseline therapy parameter set.
14. The medical device of claim 13, wherein the processor perturbs
at least one therapy parameter value of the baseline therapy
parameter set, monitors a perturbed value of the performance metric
in response to the perturbed therapy parameter value, compares the
perturbed value of the performance metric to the value of the
performance metric for the baseline therapy parameter set and
adjusts the baseline therapy parameter set based on the
comparison.
15. The medical device of claim 10, wherein the performance metric
comprises at least one of an activity level metric, or a posture
metric,
16. The medical device of claim 10, wherein the performance metric
comprises at least one of a movement disorder metric, or a
side-effects metric.
17. The medical device of claim 10, wherein the processor monitors
a patient feedback metric.
18. The medical device of claim 10, wherein the medical device
comprises an implantable medical device.
19. The medical device of claim 10, wherein the medical device
comprises at least one of a neurostimulator or a pump.
20. The medical device of claim 10, wherein the medical device
comprises at least one of a trial neurostimulator or a trial
pump.
21. The medical device of claim 10, wherein the medical device
delivers the therapy to the patient to treat chronic pain.
22. A computer-readable medium comprising instructions that cause a
programmable processor to: monitor a value of a performance metric
of a patient for each of a plurality of therapy parameter sets,
wherein a medical device delivers a therapy to the patient
according to each of the therapy parameter sets, and each of the
therapy parameter sets includes a value for each of a plurality of
therapy parameters; conduct a sensitivity analysis of the
performance metric for each of the plurality of therapy parameters
based on the monitored values of the performance metric; and
identify a baseline value for each of the plurality of therapy
parameters based on the sensitivity analysis to form a baseline
therapy parameter set.
23. The medium of claim 22, further comprising instructions that
cause the processor to receive a range for at least one therapy
parameter in the therapy parameter sets and generate the plurality
of therapy parameter sets with different values of the therapy
parameter distributed over the range.
24. The medium of claim 22, further comprising instructions that
cause the processor to perturb at least one therapy parameter value
of a baseline therapy parameter set that includes the identified
therapy parameter values, monitor a perturbed value of the
performance metric in response to the perturbed therapy parameter
value, compare the perturbed value of the performance metric to the
value of the performance metric for the baseline therapy parameter
set and adjust the baseline therapy parameter set based on the
comparison.
25. The medium of claim 22, wherein the performance metric
comprises at least one of an activity level metric, or a posture
metric,
26. The medium of claim 22, wherein the performance metric
comprises at least one of a movement disorder metric, or a
side-effects metric.
27. The medium of claim 22, wherein the performance metric
comprises a patient feedback metric.
28. A system comprising: a therapy device that delivers a therapy
to a patient according to each of a plurality of therapy parameter
sets, each of the therapy parameter sets including a value for each
of a plurality of therapy parameters; a monitor that monitors
values of at least one physiological parameter of a patient during
delivery of therapy according to each of the plurality of therapy
parameter sets; and a computing device that receives the
physiological parameter values from the monitor, identifies a value
of a performance metric of the patient for each of the plurality of
parameter sets based on the physiological parameter values
monitored during delivery of therapy according to each of the
plurality of therapy parameter sets, conducts a sensitivity
analysis of the performance metric for each of the plurality of
therapy parameters based on the monitored values of the performance
metric, and identifies a baseline value for each of the therapy
parameters based on the sensitivity analysis to form a baseline
therapy parameter set.
29. The system of claim 28, wherein one of the therapy device and
the computing device comprises the monitor.
30. The system of claim 28, wherein the computing device comprises
a programming device.
31. The system of claim 28, wherein the computing device receives a
range for at least one therapy parameter in the therapy parameter
sets and generates the plurality of therapy parameter sets with
different values of the therapy parameter distributed over the
range.
32. The system of claim 28, wherein the computing device conducts
the sensitivity analysis to determine a value for at least one
therapy parameter corresponding to one of a maximum value and a
minimum value of the performance metric.
33. The system of claim 28, wherein one of the therapy device or
another therapy device receives a baseline therapy parameter set
that includes the identified baseline therapy parameter values from
the computing device, and delivers the therapy to the patient
according to the baseline therapy parameter set.
34. The system of claim 33, wherein the computing device directs
the therapy device or the other therapy device to perturb at least
one therapy parameter value of the baseline therapy parameter set
and monitors a perturbed value of the performance metric in
response to the perturbed therapy parameter value.
35. The system of claim 34, wherein the computing device compares
the perturbed value of the performance metric to the value of the
performance metric for the baseline therapy parameter set and
updates the baseline therapy parameter set based on the comparison,
and one of the therapy device or the other therapy device receives
the updated baseline therapy parameter set from the computing
device to adjust the therapy based on the updated baseline therapy
parameter set.
36. The system of claim 28, wherein the performance metric
comprises at least one of an activity level metric, or a posture
metric,
37. The system of claim 28, wherein the performance metric
comprises at least one of a movement disorder metric, or a
side-effects metric.
38. The system of claim 28, wherein the performance metric
comprises a patient feedback metric.
39. The system of claim 28, wherein the therapy device comprises an
implantable medical device.
40. The system of claim 28, wherein the therapy device comprises at
least one of a neurostimulator or a pump.
41. The system of claim 28, wherein the therapy device comprises at
least one of a trial neurostimulator or a trial pump.
Description
[0001] This application is a continuation of U.S. patent
application Ser. No. 11/081,873, filed Mar. 16, 2005, which claims
the benefit of U.S. Provisional Application Ser. No. 60/553,769,
filed Mar. 16, 2004, the entire content of both of which is
incorporated herein by reference.
TECHNICAL FIELD
[0002] The invention relates to medical devices and, more
particularly, to medical devices that deliver a therapy.
BACKGROUND
[0003] In some cases, an ailment may affect a patient's sleep
quality or physical activity level, or a therapy delivered to the
patient to treat the ailment may produce undesirable side effects.
For example, chronic pain may cause a patient to have difficulty
falling asleep, and may disturb the patient's sleep, e.g., causing
the patient to wake. Further, chronic pain may cause the patient to
have difficulty achieving deeper sleep states, such as one of the
nonrapid eye movement (NREM) sleep states associated with deeper
sleep. Other ailments that may negatively affect patient sleep
quality include movement disorders, psychological disorders, sleep
apnea, congestive heart failure, gastrointestinal disorders and
incontinence. As another example, chronic pain may cause a patient
to avoid particular physical activities, or activity in general,
where such activities increase the pain experienced by the patient.
Movement disorders and congestive heart failure may also affect
patient activity level.
[0004] Furthermore, 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.
[0005] In some cases, these ailments are treated via a medical
device, such as an implantable medical device (IMD). For example,
patients may receive an implantable neurostimulator or drug
delivery device to treat chronic pain or a movement disorder.
Congestive heart failure may be treated by, for example, a cardiac
pacemaker.
SUMMARY
[0006] In general, the invention is directed to systems, devices
and techniques for controlling delivery of a therapy to a patient
by a medical device, such as an implantable medical device (IMD),
based on a sensitivity analysis of a performance metric. The
performance metric may relate to efficacy or side effects
associated with a particular therapy. For example, the performance
metric may comprise a sleep quality metric, an activity level
metric, a posture metric, a movement disorder metric for patients
with Parkinson's disease, a side-effects metric, or the like. The
sensitivity analysis facilitates generation of a therapy parameter
set that defines a substantially maximum or minimum value of the
performance metric. A medical device according to an embodiment of
the invention may conduct the sensitivity analysis for the
performance metric, and identify values for each of a plurality of
physiological parameters based on the sensitivity analysis. A
system according to an embodiment of the invention may include a
monitor, a programmer, and a therapy device to conduct the
sensitivity analysis for the performance metric, and determine a
baseline therapy parameter set based on the sensitivity analysis.
In either case, the medical device or another medical device may
control delivery of the therapy based on a baseline therapy
parameter set that includes the identified values. The baseline
therapy parameter set may be a therapy parameter set found to be
most efficacious or to result in the least side effects, as
indicated by the performance metric value associated with that
therapy parameter set.
[0007] For the sensitivity analysis, a medical device may deliver
therapy according to a plurality of different therapy parameter
sets. Each of the therapy parameter sets comprises a value for each
of a plurality of therapy parameters. The plurality of therapy
parameter sets for the sensitivity analysis encompass a range of
therapy parameter values. The therapy parameter sets may be
generated either randomly or non-randomly. The therapy parameter
sets may be defined, for example, by the medical device or an
external programming device. The medical device, programming
device, or another device may monitor performance metric values for
each therapy parameter set in order to conduct the sensitivity
analysis.
[0008] Furthermore, after a baseline therapy parameter set has been
identified, the medical device that delivers therapy according to
the baseline therapy parameter set may periodically perturb at
least one therapy parameter value of the baseline therapy parameter
set to determine whether the performance metric value has changed
over time. The therapy parameter may be increased or decreased in
small increments relative to the range values. If perturbing the
therapy parameter improves the performance metric, the therapy
parameter value is further increased or decreased to again define a
substantially maximum or minimum performance metric value. The
baseline therapy parameter set is then updated to correspond to the
therapy parameter set with the perturbed therapy parameter value or
values. If changing the therapy parameter worsens the performance
metric, the baseline therapy parameter set is maintained. The
medical device that delivers therapy according to the baseline
therapy parameter set, a programming device, or another device may
determine the performance metric values for each perturbation, and
update the baseline therapy parameter set if indicated by the
comparison to the performance metric value for the baseline therapy
parameter set.
[0009] The medical device or a separate monitor, as examples, may
monitor one or more physiological parameters of the patient in
order to determine values for the one or more performance metrics.
Example physiological parameters that the medical device 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, muscular
activity and tone, core temperature, subcutaneous temperature,
arterial blood flow, brain electrical activity, eye motion, and
galvanic skin response. These parameters may be indicative of sleep
quality and activity level, and therefore may be useful in
determining the performance metric values for different therapy
parameter sets. In some embodiments, the medical device
additionally or alternatively monitors the variability of one or
more of these parameters. In order to monitor one or more of these
parameters, the medical device may include, be coupled to, or be in
wireless communication with one or more sensors, each of which
outputs a signal as a function of one or more of these
physiological parameters.
[0010] In one embodiment, the invention is directed to a method
comprising delivering a therapy to a patient via a medical device
according to each of a plurality of therapy parameter sets, each of
the therapy parameter sets including a value for each of a
plurality of therapy parameters, and monitoring a value of a
performance metric of a patient in response to therapy delivered
according to each of a plurality of therapy parameter sets. The
method further comprises conducting a sensitivity analysis of the
performance metric for each of the plurality of therapy parameter
sets, and identifying a baseline value for each of the therapy
parameters based on the sensitivity analysis to form a baseline
therapy parameter set.
[0011] In another embodiment, the invention is directed to a
medical device that includes a therapy module and a processor. The
therapy module delivers a therapy to a patient according to each of
a plurality of therapy parameter sets, each of the therapy
parameter sets including a value for each of a plurality of therapy
parameters. The processor monitors a value of a performance metric
of the patient in response to therapy delivered according to each
of a plurality of therapy parameter sets. The processor further
conducts a sensitivity analysis of the performance metric for each
of the plurality of therapy parameter sets, and identifies a
baseline value for each of the therapy parameters based on the
sensitivity analysis to form a baseline therapy parameter set.
[0012] In another embodiment, the invention is directed to a
computer-readable medium containing instructions. The instructions
cause a programmable processor to monitor a value of a performance
metric of a patient for each of a plurality of therapy parameter
sets, wherein a medical device delivers a therapy to the patient
according to each of the therapy parameters sets, and each of the
parameter sets includes a value for each of a plurality of therapy
parameters. The instructions further cause the processor to conduct
a sensitivity analysis of the performance metric for each of the
plurality of therapy parameter sets, and identify a baseline value
for each of the plurality of therapy parameters based on the
sensitivity analysis to form a baseline therapy parameter set.
[0013] In another embodiment, the invention is directed to a system
comprising a therapy device, a monitor, and a computing device. The
therapy device delivers therapy to a patient according to each of a
plurality of therapy parameter sets, each of the therapy parameter
sets including a value for each of a plurality of therapy
parameters. The monitor monitors values of at least one
physiological parameter of a patient in response to therapy
delivered according to each of the plurality of therapy parameter
sets. The computing device receives the physiological parameter
values from the monitor, identifies values of a performance metric
of the patient for each of the plurality of parameter sets based on
the physiological parameter values monitored during delivery of
therapy according to each of the plurality of therapy parameter
sets, conducts a sensitivity analysis of the performance metric for
each of the plurality of therapy parameter sets, and identifies a
baseline value for each of the therapy parameters based on the
sensitivity analysis to form a baseline therapy parameter set.
[0014] The invention is capable of providing one or more
advantages. For example, through the sensitivity analysis of the
performance metric, a baseline therapy parameter set that provides
substantially maximum or minimum value of the performance metric
may be identified. A medical device may provide therapy according
to the baseline therapy parameter set.
[0015] Further, the medical device may be able to adjust therapy to
produce an improved performance metric value. In particular, the
adjustments may address symptoms that cause a poor performance
metric value or symptoms that are worsened by a poor performance
metric value. Adjusting therapy based on the performance metric
value information may significantly improve the patient's
performance quality and condition. The ability of a medical device
to periodically check performance metric values and adjust therapy
parameters based on the performance metric values may reduce the
need for the patient to make time consuming and expensive clinic
visits when the patient's sleep is disturbed, physical activity
level has decreased, or symptoms have worsened.
[0016] 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
[0017] FIG. 1 is a conceptual diagram illustrating an example
system that includes an implantable medical device that controls
delivery of therapy based on a sensitivity analysis of a
performance metric.
[0018] FIG. 2 is a block diagram further illustrating the example
system and implantable medical device of FIG. 1.
[0019] FIG. 3 is a block diagram illustrating an example memory of
the implantable medical device of FIG. 1.
[0020] FIG. 4 is a flow diagram illustrating an example method for
collecting sleep quality information that may be employed by an
implantable medical device.
[0021] FIG. 5 is a flow diagram illustrating an example method for
identifying and modifying a baseline therapy parameter set based on
a sensitivity analysis of a sleep quality metric, which is an
example of a performance metric.
[0022] FIG. 6 is a conceptual diagram illustrating a monitor that
monitors values of one or more physiological parameters of a
patient.
DETAILED DESCRIPTION
[0023] FIG. 1 is a conceptual diagram illustrating an example
system 10 that includes an implantable medical device (IMD) 14 that
controls delivery of a therapy to a patient 12 based on a
sensitivity analysis of a performance metric. The performance
metric may relate to efficacy or side effects. For example, the
performance metric may comprise a sleep quality metric, a physical
activity level metric, a posture metric, a movement disorder metric
for patients with Parkinson's disease, or the like. The sensitivity
analysis determines values of a therapy parameter set that define a
substantially maximum or minimum value of the performance metric.
In particular, as will be described in greater detail below, IMD 14
or another device conducts the sensitivity analysis of the
performance metric, and determines a baseline therapy parameter set
based on the sensitivity analysis. IMD 14 controls delivery of the
therapy based on the baseline therapy parameter set. Furthermore,
IMD 14 or another device may periodically perturb at least one
therapy parameter value of the baseline therapy parameter set to
determine whether the performance metric value has changed over
time.
[0024] Feedback entered by patient 12, such as comments and/or a
pain level value, may also be used as a performance metric to
determine the baseline therapy parameter set. In some cases, a
clinician or physician may determine a weighting scheme to provide
more or less significance to the patient's feedback, i.e., the
physician may choose to give the patient feedback zero weight and
instead rely completely on other performance metric values, or the
physician may judge that the patient has enough perspective to be
able to competently gage pain levels and input substantially
objective feedback into the sensitivity analysis.
[0025] Although the invention may use any performance metric, for
purposes of illustration, the invention will be described herein as
using a sleep quality metric to control the delivery of therapy to
a patient. IMD 14 may be able to adjust the therapy to address
symptoms causing disturbed sleep, or symptoms that are worsened by
disturbed sleep. In exemplary embodiments, IMD 14 delivers a
therapy to treat chronic pain, which may both negatively impact the
quality of sleep experienced by patient 12, and be worsened by
inadequate sleep quality.
[0026] In the illustrated example system, IMD 14 takes the form of
an implantable neurostimulator that delivers neurostimulation
therapy in the form of electrical pulses to patient 12. 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.
[0027] However, the invention is not limited to the configuration
of leads 16 shown in FIG. 1, or to the delivery of SCS therapy. For
example, one or more leads 16 may extend from IMD 14 to the brain
(not shown) of patient 12, and IMD 14 may deliver deep brain
stimulation (DBS) therapy to patient 12 to, for example, treat
tremor or epilepsy. As further examples, one or more leads 16 may
be implanted proximate to the pelvic nerves (not shown) or stomach
(not shown), and IMD 14 may deliver neurostimulation therapy to
treat incontinence, sexual dysfunction, or gastroparesis.
[0028] Moreover, the invention is not limited to implementation via
an implantable neurostimulator, or even implementation via an IMD.
In other words, any implantable or external medical device that
delivers a therapy may control delivery of the therapy based on
performance metric information, such as sleep quality information,
according to the invention.
[0029] Further, the invention is not limited to embodiments in
which the therapy-delivering medical device performs the
sensitivity analysis. For example, in some embodiments, a computing
device, such as a programming device, controls testing of therapy
parameter sets by a therapy-delivering medical device, receives
performance metric values from the medical device, performs the
sensitivity analysis, and provides a baseline therapy parameter set
to the therapy-delivering medical device. In some embodiments,
multiple computing devices may cooperate to perform these
functions. For example, a programming device may control testing of
therapy parameter sets by the therapy-delivering medical device and
receive performance metric values from the medical device, while
another computing device performs the sensitivity analysis on the
performance metric values, and identifies the baseline therapy
parameter set. The other computing device may provide the baseline
therapy parameter set to the programming device, which may in turn
provide the baseline therapy parameter set to the medical device.
The other computing device may have a greater computing capacity
than the programming device, which may allow it to more easily
perform the sensitivity analysis, and may, for example, be a server
coupled to the programming device by a network, such as a local
area network (LAN), wide area network (WAN), or the Internet.
[0030] As another example, in some embodiments, the programming
device or other computing device may receive values for one or more
physiological parameters from the medical device, and may determine
values for the performance metric based on the physiological
parameter values. Further, in some embodiments of the invention, an
implantable or external monitor separate from the
therapy-delivering medical device may monitor physiological
parameters of the patient instead of, or in addition to the
therapy-delivering medical device. The monitor may determine values
of the performance metric based on values of the physiological
parameters, or transmit the physiological parameter values to a
programming device or other computing device that determines the
values of the performance metric. In some embodiments, the
programming device and the monitor may be embodied within a single
device.
[0031] Additionally, in some embodiments, a therapy device other
than IMD 14 may deliver therapy during the process of determining
the baseline therapy parameter sets. The other therapy device may
be an external or implantable trialing device, such as a trial
neurostimulator or trial pump. The other therapy delivery device
may monitor physiological parameter values of patient 12, determine
performance metric values, and perform the sensitivity analysis, as
described herein with reference to IMD 14. In other embodiments,
some or all of these functions may be performed by a monitor,
programming device, or other computing device, as described above.
In such embodiments, IMD 14 may deliver therapy according to a
baseline therapy parameter set determined by a sensitivity analysis
during a trialing period, and may perturb the therapy parameters
for continued refinement of the baseline therapy parameter set, as
will be described in greater detail below
[0032] In the illustrated embodiment, 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 may include voltage or current pulse amplitudes, pulse
widths, pulse rates, duty cycles, durations, 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.
[0033] In other non-neurostimulator embodiments of the invention,
the IMD 14 may still deliver therapy according to a different type
of therapy parameter set. For example, implantable pump IMD
embodiments may deliver a therapeutic agent to a patient according
to a therapy parameter set that includes, for example, a dosage, an
infusion rate, and/or a duty cycle.
[0034] System 10 also includes a clinician programmer 20, which is
an example of a programming device that may determine values of a
performance metric and/or perform a sensitivity analysis, as
described above. 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.
[0035] 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 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, mouse, or the like.
Keypad 24 may take the form of a complete keyboard, an alphanumeric
keypad or a reduced set of keys associated with particular
functions.
[0036] 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. As an example, patient 12 may
increase or decrease stimulation pulse amplitude using patient
programmer 26. Patient programmer 26 is also an example of a
programming device that may determine values of a performance
metric and/or perform a sensitivity analysis, as described above.
Patient programmer 26 may also 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.
[0037] 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 a tablet-based computing device, a desktop computing
device, or a workstation.
[0038] IMD 14, clinician programmer 20 and patient programmer 26
may, as shown in FIG. 1, communicate via wireless communication.
Clinician programmer 20 and patient programmer 26 may, for example,
communicate via wireless communication with IMD 14 using radio
frequency (RF) or infrared telemetry techniques known in the art.
Clinician programmer 20 and patient programmer 26 may communicate
with each other using any of a variety of local wireless
communication techniques, such as RF communication according to the
802.11 or Bluetooth specification sets, infrared communication
according to the IRDA specification set, or other standard or
proprietary telemetry protocols.
[0039] 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.
[0040] As mentioned above, IMD 14 controls delivery of a therapy,
e.g., neurostimulation, to patient 12 based on a sensitivity
analysis of the sleep quality experienced by the patient. In some
embodiments, as will be described in greater detail below, IMD 14
conducts the sensitivity analysis to determine values of a therapy
parameter set that defines a substantially maximum value of a sleep
quality metric that indicates the quality of sleep experienced by
patient 12. IMD 14 determines a baseline therapy parameter set
based on the sensitivity analysis and controls delivery of the
therapy to patient 12, e.g., adjusts the therapy, based on the
baseline therapy parameter set. Furthermore, IMD 14 may
periodically perturb at least one therapy parameter value of the
baseline therapy parameter set to determine whether the response of
the sleep quality metric value to perturbation has changed over
time. The perturbation may occur at a preset time, in response to a
change in a physiological parameter of a patient, or in response to
a signal from a patient or a clinician. The therapy parameter
values may be increased or decreased in small increments relative
the therapy parameter range.
[0041] In some embodiments, IMD 14 compares the sleep quality
metric value defined by the baseline therapy parameter set to a
sleep quality metric value defined by the perturbed therapy
parameter values. IMD 14 then adjusts the therapy delivered to
patient 12 based on the comparison. For example, IMD 14 may
maintain the baseline therapy parameter set when the comparison
shows no improvement in the value of the sleep quality metric
during perturbation. When the comparison shows improvement in the
sleep quality metric value during perturbation, IMD 14 updates the
baseline therapy parameter set based on the one or more perturbed
therapy parameter values.
[0042] In other embodiments, an implantable or external programmer,
such as programmers 20 and 26, may perturb at least one therapy
parameter value of the baseline therapy parameter set and an
implantable or external monitoring device may monitor the sleep
quality metric value. The programmer may also conduct the
comparison and update the baseline parameter set based on the
comparison. An implantable or external therapy device, such as IMD
14, may then alter the therapy provided to the patient based on the
updated baseline parameter set.
[0043] IMD 14 may monitor one or more physiological parameters of
the patient in order to determine values for one or more sleep
quality metrics. 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, muscular
activity and tone, core temperature, subcutaneous temperature,
arterial blood flow, brain electrical activity, and eye motion.
Some external medical device embodiments of the invention may
additionally or alternatively monitor galvanic skin response.
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, be coupled to, or be in wireless communication with one or
more sensors (not shown in FIG. 1), each of which outputs a signal
as a function of one or more of these physiological parameters.
[0044] 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 sleep and a second time when patient 12 falls
asleep, e.g., as an indication of how long it takes patient 12 to
fall asleep.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] 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 output signals as a function of
one or more physiological parameters of patient 12.
[0049] 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 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.
[0050] 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
one or more neurostimulation therapy programs selected from
available programs stored in a memory 48. 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 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, and a processor of the
IMD may control delivery of a therapeutic agent by the pump
according to an infusion program selected from among a plurality of
infusion programs stored in a memory.
[0051] IMD 14 may also include a telemetry circuit 50 that enables
processor 46 to communicate with programmers 20, 26. Via telemetry
circuit 50, processor 46 may receive therapy programs specified by
a clinician from clinician programmer 20 for storage in memory 48.
Processor 46 may also receive program selections and therapy
adjustments made by patient 12 using patient programmer 26 via
telemetry circuit 50. In some embodiments, processor 46 may provide
diagnostic information recorded by processor 46 and stored in
memory 48 to one of programmers 20, 26 via telemetry circuit
50.
[0052] 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.
[0053] Each of sensors 40 outputs 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 output 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 output 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.
[0054] 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 located outside of IMD 14 may be in wireless
communication with processor 46.
[0055] 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, brain electrical activity, and
eye motion. Further, as discussed above, external medical device
embodiments of the invention may additionally or alternatively
monitor galvanic skin response. Sensors 40 may be of any type known
in the art capable of outputting a signal as a function of one or
more of these parameters.
[0056] 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.
[0057] 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 by leads 16 or wirelessly, or,
if IMD 14 is implanted in these locations, integrated with a
housing of IMD 14.
[0058] 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 via leads 16 or wirelessly, 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] Other sensors 40 that may generate a signal that indicates
the posture of patient 12 include electrodes that generate an
electromyogram (EMG) signal, or bonded piezoelectric crystals that
generate a signal as a function of contraction of muscles. Such
sensors 40 may be implanted in the legs, buttocks, abdomen, or back
of patient 12, as described above. The signals generated by such
sensors when implanted in these locations may vary based on the
posture of patient 12, e.g., may vary based on whether the patient
is standing, sitting, or laying down.
[0063] Further, the posture of patient 12 may affect the thoracic
impedance of the patient. Consequently, sensors 40 may include an
electrode pair, such as 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.
[0064] 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.
[0065] In some embodiments, processor 46 considers both the posture
and the activity level of patient 12 when determining whether
patient 12 is attempting to fall asleep. For example, processor 46
may determine whether patient 12 is attempting to fall asleep based
on a sufficiently long period of sub-threshold activity, as
described above, and may identify the time that patient began
attempting to fall asleep as the time when patient 12 became
recumbent. Any of a variety of combinations or variations of these
techniques may be used to determine when patient 12 is attempting
to fall asleep, and a specific one or more techniques may be
selected based on the sleeping and activity habits of a particular
patient.
[0066] 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, values of 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. In particular, 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.
[0067] 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.
[0068] 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 value, and/or
the variability of each of a plurality of physiological parameters
to determine a sleep probability metric value 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 level.
[0069] 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. 11/081,786, by Ken Heruth and Keith Miesel, entitled "DETECTING
SLEEP," bearing Attorney Docket No. 1023-360US02 and filed on Mar.
16, 2005, which is incorporated herein by reference in its
entirety.
[0070] 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, buttocks, chest, 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.
[0071] As another example, sensors 40 may include electrodes
located on leads or integrated as part of the housing of IMD 14
that output 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 sensor within the bloodstream or cerebrospinal
fluid of patient 12, or a temperature sensor located within the
bloodstream of patient 12. The signals output 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.
[0072] 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.
[0073] In some embodiments, sensors 40 may include an electrode
pair, including one electrode integrated with the housing of IMD 14
and one of electrodes 16, that output a signal as a function of the
thoracic impedance of patient 12 as described above, which varies
as a function of respiration by patient 12. In other embodiments,
sensors 40 may include a strain gauge, bonded piezoelectric
element, or pressure sensor within the blood or CSF that outputs a
signal that varies based on patient respiration. An electrogram
output by electrodes as discussed above may also be modulated by
patient respiration, and may be used as an indirect representation
of respiration rate.
[0074] Sensors 40 may include electrodes that output 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 output a signal as a function of a
core or subcutaneous temperature of patient 12. Such electrodes and
temperature sensors may be incorporated within the housing of IMD
14, or coupled to IMD 14 wirelessly or via leads. Sensors 40 may
also include a pressure sensor within, or in contact with, a blood
vessel. The pressure sensor may output a signal as a function of
the 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.
[0075] 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 output signals as a
function 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 output 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.
[0076] In some embodiments, sensors 40 may include one or more
intraluminal, extraluminal, or external flow sensors positioned to
output 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 output a signal as a function of galvanic skin
response.
[0077] Additionally, in some embodiments, sensors 40 may include
one or more electrodes positioned within or proximate to the brain
of patient, which detect electrical activity of the brain. For
example, in embodiments in which IMD 14 delivers stimulation or
other therapy to the brain, processor 46 may be coupled to
electrodes implanted on or within the brain via a lead 16. In other
embodiments, processor 46 may be wirelessly coupled to electrodes
that detect brain electrical activity.
[0078] 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 scalp, or a headband,
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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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. Processor 46 may determine, as sleep quality metric values, the
amounts of time per night spent in the various sleep states.
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. Inadequate time spent in deeper sleep
states, e.g., S3 and S4, is an indicator of poor sleep quality.
[0083] FIG. 3 further illustrates memory 48 of IMD 14. As
illustrated in FIG. 3, memory 48 stores a plurality of therapy
parameter sets 60. Therapy parameter sets 60 may include parameter
sets randomly or non-randomly generated by processor 46 over
therapy parameter ranges 68 set by a clinician using clinician
programmer 20. Therapy parameter sets 60 may also include parameter
sets specified by a clinician using clinician programmer 20 and
preprogrammed therapy parameter sets.
[0084] Memory 48 may also include parameter data 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.
[0085] Further, processor 46 stores determined sleep quality metric
values 66 for each of the plurality of therapy parameter sets 60
within memory 48. Processor 46 conducts a sensitivity analysis of
the sleep quality metric values for each therapy parameter. The
sensitivity analysis determines a value for each therapy parameter
that defines a substantially maximum sleep quality metric value. In
other words, the sensitivity analysis identifies parameter values
that yield the best sleep quality metric values. Processor 46 then
determines a baseline therapy parameter set based on the
sensitivity analysis and stores the baseline therapy parameter set
with therapy parameter set 66 or separately within memory 48. The
baseline therapy parameter set may be identical to a single one of
therapy parameter sets 60, or may be a new therapy parameter set
that includes one or more therapy parameter values from a plurality
of therapy parameter sets 60. The baseline therapy parameter set
includes the values for respective therapy parameters that produced
the best sleep quality metric values.
[0086] 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. 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
of weighting factors for one or more of the individual sleep
quality metric values.
[0087] In some embodiments, as discussed above, processor 46 may
adjust the therapy delivered by therapy module 44 based on a change
in the sleep quality metric value. In particular, processor 46 may
perturb one or more therapy parameters of the baseline therapy
parameter set, such as pulse amplitude, pulse width, pulse rate,
duty cycle, and duration to determine if the current sleep quality
metric value improves or worsens during perturbation. In some
embodiments, processor 46 may iteratively and incrementally
increase or decrease values of the therapy parameters until a
substantially maximum value of the sleep quality metric value is
again determined.
[0088] FIG. 4 is a flow diagram illustrating an example method for
collecting sleep quality information that may be employed by IMD 14
alone, or in combination with a computing device and/or a monitor.
In some embodiments, as discussed above, a computing device, such
as one of programmers 20 and 26, may determine sleep quality metric
values based on monitored physiological parameter values, rather
than IMD 14. Further, in some embodiments, a monitor may monitor
physiological parameter values instead of, or in addition to, IMD
14.
[0089] In the illustrated example, however, IMD 14 monitors the
posture and/or activity level of patient 12, or monitors for an
indication from patient 12, e.g., via patient programmer 26 (70),
and determines whether patient 12 is attempting to fall asleep
based on the posture, activity level, and/or a patient indication,
as described above (72). 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).
[0090] 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).
[0091] 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).
[0092] 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).
[0093] For example, one sleep quality metric value that IMD 14 may
calculate is sleep efficiency, which IMD 14 may calculate as a
percentage of time during which patient 12 is attempting to sleep
that patient 12 is actually asleep. IMD 14 may determine a first
amount of time between the time IMD 14 identified that patient 12
fell asleep and the time IMD 14 identified that patient 12 awoke.
IMD may also determine a second amount of time between the time IMD
14 identified that patient 12 began attempting to fall asleep and
the time IMD 14 identified that patient 12 awoke. To calculate the
sleep efficiency, IMD 14 may divide the first time by the second
time.
[0094] 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. IMD 14
may store the determined values as sleep quality metric values 66
within memory 48.
[0095] IMD 14 may perform the example method illustrated in FIG. 4
continuously. For example, IMD 14 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.
[0096] FIG. 5 is a flow diagram illustrating an example method for
identifying and modifying a baseline therapy parameter set based on
a sensitivity analysis of a sleep quality metric, which is an
example of a performance metric. In the illustrated example, the
method is employed by IMD 14. However, in other embodiments, a
system including one or more of IMD 14, a physiological parameter
monitor, a trial therapy device, and a programmer and/or other
computing device may perform the example method, as described
above.
[0097] IMD 14 receives a therapy parameter range 68 for therapy
parameters (100) from a clinician using clinician programmer 20 via
telemetry circuit 50. The range 68 may include minimum and maximum
values for each of one or more individual therapy parameters, such
as pulse amplitude, pulse width, pulse rate, duty cycle, duration,
dosage, infusion rate, electrode placement, and electrode
selection. Range 68 may be stored in memory 48, as described in
reference to FIG. 3. Processor 46 then randomly or non-randomly
generates a plurality of therapy parameter sets 60 with individual
parameter values selected from the range 68 (102). The generated
therapy parameter sets 60 may substantially cover range 68, but do
not necessarily include each and every therapy parameter value
within range 68, or every possible combination of therapy
parameters within range 68. Therapy parameter sets 60 may also be
stored in memory 48.
[0098] IMD 14 monitors a sleep quality metric of patient 12 for
each of the randomly or non-randomly generated therapy parameter
sets 60 spanning range 68 (104). The values of the sleep quality
metric 66 corresponding to each of the therapy parameter sets 60
may be stored in memory 48 of IMD 14. IMD 14 then conducts a
sensitivity analysis of the sleep quality metric for each of the
therapy parameters (106). The sensitivity analysis determines a
value for each of the therapy parameters that produced a
substantially maximum value of the sleep quality metric. A baseline
therapy parameter set is then determined based on the therapy
parameter values from the sensitivity analysis (108). The baseline
therapy parameter set includes a combination of the therapy
parameter values individually observed to produce a substantially
maximum sleep quality metric. In some embodiments, the patient may
enter comments, a pain value from a scale, or other feedback used
along with the sensitivity analysis to determine the baseline
parameter set. The baseline therapy parameter set may also be
stored with therapy parameters sets 60 in memory 48. In some
embodiments, the baseline therapy parameter set may be stored
separately from the generated therapy parameter sets.
[0099] IMD 14 controls delivery of the therapy based on the
baseline therapy parameter set. Periodically during the therapy,
IMD 14 checks to ensure that the baseline therapy parameter
continues to define a substantially maximum sleep quality metric
value for patient 12. IMD 14 first perturbs at least one of the
therapy parameter values of the baseline therapy parameter set
(110). The perturbation comprises incrementally increasing and/or
decreasing the therapy parameter value. A perturbation period may
be preset to occur at a specific time, in response to a
physiological parameter monitored by the IMD, or in response to a
signal from the patient or clinician. The perturbation may be
applied for a single selected parameter or two or more parameters,
or all parameters in the baseline therapy parameter set. Hence,
numerous parameters may be perturbed in sequence. For example, upon
perturbing a first parameter and identifying a value that produces
a maximum metric value, a second parameter may be perturbed with
the first parameter value fixed at the identified value. This
process may continue for each of the parameters in the therapy
parameter set.
[0100] Upon perturbing a parameter value, IMD 14 then compares a
value of the sleep quality metric defined by the perturbed therapy
parameter set to the value of the sleep quality metric defined by
the baseline therapy parameter set (112). If the sleep quality
metric value does not improve with the perturbation, IMD 14
maintains the unperturbed baseline therapy parameter set values
(114). If the sleep quality metric value does improve with the
perturbation, IMD 14 perturbs the therapy parameter value again
(116) in the same direction that defined the previous improvement
in the sleep quality metric value. IMD 14 compares a value of the
sleep quality metric defined by the currently perturbed therapy
parameter set and the sleep quality metric value defined by the
previously perturbed therapy parameter set (118). If the sleep
metric value does not improve, IMD 14 updates the baseline therapy
parameter set based on the therapy parameter values from the
previous perturbation (120). If the sleep metric value improves
again, IMD 14 continues to perturb the therapy parameter value
(116).
[0101] Periodically checking the value of the sleep quality metric
for the baseline therapy parameter set allows IMD 14 to
consistently deliver a therapy to patient 12 that defines a
substantially maximum sleep quality metric value of patient 12.
This allows the patient's symptoms to be continually managed even
as the patient's physiological parameters change.
[0102] In some embodiments, an external computing device, such as
clinician programmer 20, may generate the plurality of therapy
parameter sets over the range. A clinician may then provide the
therapy parameter sets to IMD 14 via clinician programmer 20. The
computing device may provide individual therapy parameter sets to
be tested, and may thus control the testing by IMD 14, or may
provide a listing of therapy parameter sets to be tested.
[0103] Furthermore, an external computing device, such as
programmer 20, a separate desktop computer, or server, may receive
the sleep quality metric values collected by the IMD for the
plurality of therapy parameter sets. The external computing device
may then conduct the sensitivity analysis to determine the baseline
therapy parameter set. The external computing device may also
control the subsequent perturbations. In some embodiments, the
external computing device may receive physiological parameter
values from IMD 14, and, rather that IMD 14, the external computing
device may determine values of the sleep quality or other
performance metric based on the physiological parameter values
received from IMD 14.
[0104] In some embodiments, the sensitivity analysis and
determination of a baseline therapy parameter set may be performed
as part of a trialing process. In such embodiments, an external or
implanted trial therapy device, such as a trial neurostimulator,
may perform the functions ascribed to IMD 14 above that are
associated with performing the sensitivity analysis and
determination of a baseline therapy parameter set. The trial
therapy device may include a therapy module 44, processor 46, and
memory 48, and may be coupled to sensors 40 and leads 16, as
described above with reference to IMD 14 and FIGS. 2 and 3.
[0105] IMD 14 may then be implanted in patient 12, and programmed
to deliver therapy according to the baseline therapy parameter set.
In such embodiments, IMD 14 may perform the perturbation and
updating functions of the example method illustrated by FIG. 5. In
some embodiments, an external computing device may control delivery
of a plurality of therapy parameter sets by the trial device,
determine performance metric values based on physiological
parameter values received from the trial device, and/or perform the
sensitivity analysis.
[0106] FIG. 6 illustrates, a separate monitor 130 that monitors
values of one or more physiological parameters of patient 12
instead of, or in addition to the trial device or IMD 14. Monitor
130 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. Monitor 130 may identify performance 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 performance metric values. In some
embodiments, an external computing device, such as a programming
device, may incorporate monitor 130. In the illustrated embodiment,
monitor 130 is portable, and is configured to be attached to or
otherwise carried by a belt 132, and may thereby be worn by patient
12.
[0107] FIG. 6 also illustrates various sensors 40 that may be
coupled to monitor 130 by leads, wires, cables, or wireless
connections, such as EEG electrodes 134A-C placed on the scalp of
patient 12, a plurality of EOG electrodes 136A and 136B placed
proximate to the eyes of patient 12, and one or more EMG electrodes
138 placed on the chin or jaw the patient. The number and positions
of electrodes 134, 136 and 138 illustrated in FIG. 6 are exemplary.
For example, although only three EEG electrodes 13 are illustrated
in FIG. 1, an array of between 16 and 25 EEG electrodes 143 may be
placed on the scalp of patient 12, as is known in the art. EEG
electrodes 134 may be individually placed on patient 12, or
integrated within a cap or hair net worn by the patient.
[0108] In the illustrated example, patient 12 wears an ECG belt
140. ECG belt 140 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 130 based on the signal provided by ECG
belt 140. Examples of suitable belts 140 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 140, patient 12 may wear a 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.
[0109] As shown in FIG. 6, patient 12 may also wear a respiration
belt 142 that outputs a signal that varies as a function of
respiration of the patient. Respiration belt 142 may be a
plethysmograpy belt, and the signal output by respiration belt 142
may vary as a function of the changes in the thoracic or abdominal
circumference of patient 12 that accompany breathing by the
patient. An example of a suitable belt 142 is the TSD201
Respiratory Effort Transducer commercially available from Biopac
Systems, Inc. Alternatively, respiration belt 142 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 140 and 142 may be a common
belt worn by patient 12, and the relative locations of belts 140
and 142 depicted in FIG. 6 are exemplary.
[0110] In the example illustrated by FIG. 1, patient 12 also wears
a transducer 144 that outputs a signal as a function of the oxygen
saturation of the blood of patient 12. Transducer 144 may be an
infrared transducer. Transducer 144 may be located on one of the
fingers or earlobes of patient 12. Sensors 40 coupled to monitor
130 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.
[0111] FIG. 6 also illustrates an external trial therapy device 146
in conjunction with patient 12. In the illustrated example, patient
12 wears trial therapy device 146 with monitor 130 on belt 132. The
trial therapy device 146 may be coupled to one or more
transcutaneoulsy implanted leads or catheters for delivery of
therapy, such as neurostimulation or a drug, to patient 12. As
described above, trial therapy device 146 may deliver therapy to
patient 12 during the sensitivity analysis and baseline therapy
parameter set determination portion of the method illustrated in
FIG. 5 and, in some embodiments, may also monitor physiological
parameters of patient 12, determine performance metric values,
and/or perform the sensitivity analysis to determine the baseline
therapy parameter set for use by IMD 14.
[0112] Various embodiments of the invention have been described.
However one skilled in the art will appreciate, however, 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 or implantable pump, the
invention is not so limited. Moreover, the invention is not limited
to implantable medical devices. The invention may be embodied in
any implantable or external medical device that delivers therapy to
treat any ailment of symptom of a patient.
[0113] As another example, the invention has been primarily
described in the context of monitoring a sleep quality metric;
however the invention is not so limited. The invention may monitor
any performance metric, such as an activity metric, posture metric,
a movement disorder metric, or other metrics that indicate the
efficacy or degree of side effects associated a therapy delivered
to a patient.
[0114] In some embodiments, for example, IMD 14 or any of the other
devices described herein may periodically determine an activity
level of patient 12 during delivery of therapy to the patient
according to a plurality of parameter sets by monitoring at least
one signal that is generated by a sensor 40 and varies as a
function of patient activity, as described above. A value of at
least one activity metric for each of a plurality of therapy
parameter sets may be determined based on the activity levels
associated with that parameter set. An activity metric value may
be, for example, a mean or median activity level, such as an
average number of activity counts per unit time. In other
embodiments, an activity metric value may be chosen from a
predetermined scale of activity metric values based on comparison
of a mean or median activity level to one or more threshold values.
The scale may be numeric, such as activity metric values from 1-10,
or qualitative, such as low, medium or high activity.
[0115] In some embodiments, each activity level associated with a
therapy parameter set is compared with the one or more thresholds,
and percentages of time above and/or below the thresholds are
determined as one or more activity metric values for that therapy
parameter set. In other embodiments, each activity level associated
with a therapy parameter set is compared with a threshold, and an
average length of time that consecutively determined activity
levels remain above the threshold is determined as an activity
metric value for that therapy parameter set. One or both of the
medical device or a programming device may determine the activity
metric values as described herein.
[0116] As another example, the device may monitor one or more
signals that are generated by respective sensors 40 and vary as a
function of patient posture, as described above. Posture events are
identified based on the posture of the patient, e.g., the patient's
posture and/or posture transitions are periodically identified, and
each identified posture event is associated with the current
therapy parameter set.
[0117] A value of at least one posture metric is determined for
each of the therapy parameter sets based on the posture events
associated with that parameter set. A posture metric value may be,
for example, an amount or percentage of time spent in a posture
while a therapy parameter set is active, e.g., average amount of
time over a period of time, such as an hour, that a patient was
within a particular posture. In some embodiments, a posture metric
value may be an average number of posture transitions over a period
of time, e.g., an hour, that a particular therapy parameter sets
was active.
[0118] In embodiments in which a plurality of posture metrics are
determined for each therapy parameter set, an overall posture
metric may be determined based on the plurality of posture metrics.
The plurality of posture metrics may be used as indices to select
an overall posture metric from a look-up table comprising a scale
of potential overall posture metrics. The scale may be numeric,
such as overall posture metric values from 1-10.
[0119] Similarly, a device may sense physiological parameter values
of a patient indicative of movement disorders, such as tremor, via
one or more sensors 40, such as one or more accelerometers.
Movement disorder metrics values that may be determined include
mean or median values output by the sensors, amounts of time the
sensor signal is above or below a threshold, or frequency of
episodes above or below a threshold.
[0120] Further details regarding activity and posture metric values
may be found in U.S. patent application Ser. No. 11/081,785, by Ken
Heruth and Keith Miesel, entitled "COLLECTING ACTIVITY INFORMATION
TO EVALUATE THERAPY," bearing Attorney Docket No. 1023-361US02 and
filed on Mar. 16, 2005, and U.S. patent application Ser. No.
11/081,872, by Ken Heruth and Keith Miesel, entitled "COLLECTING
POSTURE INFORMATION TO EVALUATE THERAPY," bearing Attorney Docket
No. 1023-359US02 and filed on Mar. 16, 2005. The content of these
applications is incorporated herein by reference in its
entirety.
[0121] Additionally, as discussed above, feedback entered by
patient 12, may be used as a performance metric instead of, or in
addition to, the other performance metrics described herein. One of
programming devices 20, 26 may receive the feedback from patient
12. In embodiments in which another device, such as a medical
device or other computing device, performs the sensitivity
analysis, the programming device may provide the feedback or
performance metric values derived from the feedback to the other
device. As examples, the feedback may include comments, or numeric
values for pain, efficacy, or side effect levels.
[0122] For example, the programming device 20, 26 may prompt
patient 12 for feedback after a new or modified program is
delivered by a therapy-delivering medical device during the
sensitivity analysis or perturbation portions of the method
illustrated by FIG. 5. Additionally or alternatively, if patient 12
experiences discomfort, the patient could cause the sensitivity
analysis or perturbation to "step backward" to the most recent
setting before the setting was changed by the algorithm via the
programming device. A perturbation of a therapy parameter may
produce results, either related or unrelated to the performance
metric, that the patient does not like. For example, a perturbation
to a higher drug dosage may result in somnolence, or a perturbation
to a higher SCS amplitude may painfully stimulate ribs or abdominal
muscles. The patient may cause the sensitivity analysis or
perturbation to "step backward" to the most recent setting to
rapidly stop the undesirably results
[0123] When the patient causes the algorithm to step backward, the
device performing the sensitivity analysis or perturbation may
record this as a low performance metric value for the avoided
program, or may prevent further program testing, perturbation, or
other program selection of the avoided program, or within in a zone
of therapy parameters determined based on the avoided program. In
embodiments in which feedback is used in addition to one or more
other performance metrics, a clinician or physician may determine a
weighting scheme to provide more or less significance to the
patient's feedback, i.e., the physician may choose to give the
patient feedback zero weight and instead rely completely on other
performance metric values, or the physician may judge that the
patient has enough perspective to be able to competently gage pain
levels and input substantially objective feedback into the
sensitivity analysis.
[0124] These and other embodiments are within the scope of the
following claims.
* * * * *