U.S. patent application number 13/359636 was filed with the patent office on 2012-06-07 for deriving patient activity information from sensed body electrical information.
This patent application is currently assigned to Greatbatch Ltd.. Invention is credited to Andres Belalcazar, Ji Chen.
Application Number | 20120143031 13/359636 |
Document ID | / |
Family ID | 40754160 |
Filed Date | 2012-06-07 |
United States Patent
Application |
20120143031 |
Kind Code |
A1 |
Belalcazar; Andres ; et
al. |
June 7, 2012 |
Deriving Patient Activity Information from Sensed Body Electrical
Information
Abstract
Electrodes of a subcutaneous monitoring system receive body
electrical signals that indicate both cardiac and non-cardiac
muscle activity. In general, non-cardiac muscle activity is often
correlated with physical activity, and physical activity is
typically a strong indicator of patient health. Exemplary systems
and methods that detect non-cardiac muscle activity information in
sensed body electrical waveforms may provide a diagnostic tool for
monitoring physical activity level over time in patients that have
subcutaneous monitoring systems. In an illustrative embodiment,
systems and methods for presenting patient activity information in
a graphical format over intervals of time include processing ECG
waveform information to identify and to accumulate non-cardiac
muscular activity information during each of the intervals of time.
In various implementations, number, intensity, and/or duration of
the events that are identified during a time interval may be
accumulated and stored for subsequent recall.
Inventors: |
Belalcazar; Andres; (Saint
Paul, MN) ; Chen; Ji; (Woodbury, MN) |
Assignee: |
Greatbatch Ltd.
Clarence
NY
|
Family ID: |
40754160 |
Appl. No.: |
13/359636 |
Filed: |
January 27, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11956884 |
Dec 14, 2007 |
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13359636 |
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Current U.S.
Class: |
600/377 ;
600/523; 607/19 |
Current CPC
Class: |
A61B 5/0031 20130101;
A61B 5/4519 20130101; A61B 5/287 20210101 |
Class at
Publication: |
600/377 ;
600/523; 607/19 |
International
Class: |
A61N 1/365 20060101
A61N001/365; A61B 5/042 20060101 A61B005/042; A61B 5/0432 20060101
A61B005/0432 |
Claims
1. A method of storing information indicative of patient activity,
the method comprising: a) receiving an electrical waveform using
subcutaneous electrodes, the received electrical waveform
comprising electrical signals generated by electrical activity of a
heart in a patient during a selected time period; b) determining a
value to represent a level of physical activity of the patient by
identifying portions of the received electrical waveform that
include signals indicative of non-cardiac muscular activity in the
patient; and c) storing the determined value in a data store for
subsequent retrieval as an indicator of a level of physical
activity of the patient during the selected time period.
2. The method of claim 1 further comprising sending the stored
determined value for display on a display device in a histogram
format to represent a measure of patient activity during the
selected time period.
3. The method of claim 1 further comprising telemetering
information indicative of non-cardiac muscle activity from a
transmit module implanted in the patient to a receiver module
external to the patient.
4. The method of claim 3 wherein the telemetered information
comprises the stored determined value.
5. The method of claim 1 further comprising sending for display on
a display device information about the values determined for each
of a plurality of selected time periods.
6. A system for presenting physical activity information for an
individual by detecting non-cardiac muscle noise in
electrocardiogram (ECG) waveform information collected by an
implanted device, the system comprising: a) an implantable waveform
acquisition module operable to acquire ECG waveforms from
subcutaneous electrodes implanted in a body; b) a processing module
to process the acquired ECG waveforms to identify non-cardiac
muscle activity events; c) an accumulation module to determine an
activity level for each of a plurality of time intervals, each
activity level being determined from an aggregation of the
identified non-cardiac muscle activity events; and d) a format
module to render a graphical representation of the determined
activity level for each of the plurality of time intervals.
7. The system of claim 6 further comprising a housing that contains
the implantable waveform acquisition module and the processing
module, the housing having an electrically conductive exterior
portion that forms at least one of the subcutaneous electrodes.
8. The system of claim 7 wherein at least a portion of the
electrically conductive exterior portion of the housing is adapted
to make substantially direct electrical contact with non-cardiac
muscle tissue.
9. The system of claim 8 wherein the non-cardiac muscle tissue
comprises fascia.
10. The system of claim 7 wherein at least a portion of the
electrically conductive exterior portion of the housing is
substantially free of any electrically insulating coating when
implanted in a body of a patient.
11. The system of claim 6 wherein the processing module receives
the acquired ECG waveform from a receiver module located external
to the body.
12. The system of claim 6 wherein the processing module is
substantially remote from the body.
13. The system of claim 6 wherein the graphical representation
comprises a histogram.
14. A method of controlling a rate of application of electrical
stimulation to a heart of a patient based on patient activity as
determined from non-cardiac muscle electrical signals in
electrocardiogram (ECG) waveform information collected by an
implanted device, the method comprising: a) receiving an electrical
waveform using subcutaneous electrodes, the received electrical
waveform comprising electrical signals generated by electrical
activity of a heart in a patient during a selected time period; b)
determining a level of physical activity of the patient by
identifying portions of the received electrical waveform that
include signals indicative of non-cardiac muscular activity in the
patient during the selected time period; and c) adjusting a rate of
electrical stimulation applied to the heart in response to changes
in the determined level of physical activity.
15. The method of claim 14 wherein the determining step comprises
post-processing the received electrical waveform to determine a
level of activity of the patient during a portion of the selected
time periods by identifying non-cardiac muscle activity events in
the received electrical waveform and aggregating the identified
activity events.
16. The method of claim 15 wherein the determining step further
comprises counting the identified activity events to determine the
level of activity of the patient.
17. The method of claim 15 wherein the determined level of activity
of the patient is a function of an amplitude of each of the
identified activity events.
18. The method of claim 15 wherein the determined level of activity
of the patient is a function of a duration of one or more of the
identified activity events.
19. The method of claim 14 wherein the adjusting step comprises
adjusting the rate of electrical stimulation applied to the heart
in response to a moving average of the determined physical activity
level.
20. A system for applying electrical stimulus to an organ in a
patient, the stimulation being responsive to patient physical
activity information determined by detecting non-cardiac muscle
noise in electrocardiogram (ECG) waveform information collected by
a device implanted in the patient, the system comprising: a) an
implantable waveform acquisition module operable to acquire ECG
waveforms from subcutaneous electrodes implanted in a body; b) a
first processing module to process the acquired ECG waveforms to
identify non-cardiac muscle activity events; a second processing
module to determine an activity level from an aggregation of the
identified non-cardiac muscle activity events; and c) a stimulation
module to generate electrical stimulation to apply to an organ in
the body based on the determined activity level.
21. The system of claim 20 wherein the implantable waveform
acquisition module comprises a housing having an electrically
conductive exterior portion that forms at least one of the
subcutaneous electrodes.
22. The system of claim 21 wherein at least a portion of the
electrically conductive exterior portion of the housing is adapted
to make substantially direct electrical contact with non-cardiac
muscle tissue.
23. The system of claim 22 wherein the non-cardiac muscle tissue
comprises fascia.
24. The system of claim 20 wherein at least a portion of the
electrically conductive exterior portion of the housing is
substantially free of any electrically insulating coating when
implanted in the body.
Description
TECHNICAL FIELD
[0001] Various embodiments relate to monitoring a patient's
physical activity based on body electrical information sensed by
electrodes within a body of the patient.
BACKGROUND
[0002] Electrical signals cause a heart to beat. In a healthy
patient, regular heart beats pump blood through the cardiovascular
system. The human cardiovascular system is responsible for
receiving oxygen-deprived blood into the heart from the venous
system of the body, delivering the oxygen-deprived blood to the
lungs to be replenished with oxygen, receiving the oxygenated blood
from the lungs back into the heart, and delivering the oxygenated
blood to the body via the arterial vasculature. This process is
regulated within the heart by electrical pulses that control
operation of the heart's receiving and pumping chambers.
[0003] In a healthy heart, the sinoatrial node of the heart
generates electrical pulses in a consistent and regulated fashion
to regulate receiving and pumping blood in the heart's chambers.
The electrical impulses propagate as activation wavefronts across
the atria, the upper chambers of the heart, and cause cells of the
atria to depolarize and contract, which forces blood from the atria
to the ventricles, the lower chambers of the heart. The ventricles
receive the blood from the atria, and the wavefront, after passing
through the atrioventricular node and moving to the Purkinje
system, moves to cells of the ventricles causing the ventricles to
contract and pump the blood to the lungs and to the rest of the
body.
[0004] Various aspects of cardiac activity (e.g., heart rate,
arrhythmias) can be detected by measuring, recording, and analyzing
cardiac electrical signals, such as an electrocardiogram (ECG)
signal. One way of measuring ECG signals involves attaching
electrodes, typically ten, externally to a patient's skin and
sensing the electrical signals that form the ECG waveform.
[0005] Implantable monitoring systems can be implanted under the
skin with electrodes that sense subcutaneous electrical signals,
including ECG signals, which are analyzed as being indicative of
cardiac activity. In such systems, the electrodes also receive
extraneous non-cardiac electrical signal information, which is
typically filtered out to produce a more readable ECG Non-cardiac
electrical signals can be generated by muscle tissues during
physical activity.
SUMMARY
[0006] Electrodes of a subcutaneous monitoring system receive body
electrical signals that indicate both cardiac and non-cardiac
muscle activity. In general, non-cardiac muscle activity is often
correlated with physical activity, and physical activity is
typically a strong indicator of patient health. Exemplary systems
and methods that detect non-cardiac muscle activity information in
ECG waveforms may give health care providers a diagnostic tool for
monitoring physical activity level over time in patients that have
subcutaneous monitoring systems.
[0007] Various embodiments monitor a patient's activity level over
time by processing received ECG signals to detect episodes of
electrical signals that indicate non-cardiac activity.
[0008] In some examples, systems and methods for presenting patient
activity information in a graphical format over intervals of time
include processing ECG waveform information to identify and to
accumulate non-cardiac muscular activity information during each of
the intervals of time. In an illustrative embodiment, ECG waveforms
received by subcutaneous electrodes may be processed by analog
and/or digital signal processing techniques to identify signals
indicative of non-cardiac muscle activity. In various
implementations, number, intensity, and/or duration of the events
that are identified during a time interval may be accumulated and
stored for subsequent recall. In an illustrative diagnostic
application, the accumulated information may graphically represent
patient activity levels during each of a number of selected time
intervals to a reviewing health care provider.
[0009] Some embodiments may have one or more advantages. For
example, some embodiments may take advantage of existing ECG
waveform detection capability of existing implanted devices, for
example, with or without the addition of a software modification to
the implanted medical device. In particular embodiments,
telemetered ECG waveform information may be processed digitally to
detect and/or characterize non-cardiac muscle electrical noise
(e.g., EMG) events. Various embodiments may further process for
display the patient activity information according to configurable
parameters, such as number of events within each of a number of
user-specified time intervals, event duration, event intensity,
and/or a combination of these or other parameters, such as
integration over time of non-cardiac muscle activity. Some
embodiments may further include improved sensitivity to non-cardiac
muscle activity, for example, by substantially removing a coating,
which may be applied to conductive exterior portions of an
implantable medical device, to provide more direct electrical
interface between implanted electrodes (e.g., a housing or "can" of
the implantable device) and non-cardiac muscle tissues (e.g.,
fascia of the muscles).
[0010] Various embodiments may provide significantly improved
quality and flexibility in the presentation of patient activity
information to medical personnel. Various implementations may
provide graphical display of patient information, for example, on
demand or in substantially real time. Various parameters, such as
interval length, number, and/or spacing, display formatting may be
independently controllable, such as by automatic detection of the
size and/or contents of the data set to be displayed in combination
with optional user override capability. Various implementations may
afford improved flexibility in component capabilities within the
overall system architecture, for example by post-processing ECG
waveform data, in whole or in part, at any of the nodes in the
system network. Thus, data storage and processing operations
performed within the system may be dynamically reconfigured to
optimize performance. By way of example, post-processing may occur,
at least in part, in a medical device implanted within the patient,
at a communication node local to the patient, at a central data
center, and/or at a remote client device operated by medical
personnel reviewing the patient activity information in desired
time ranges of interest.
[0011] The details of one or more embodiments are set forth in the
accompanying drawings and the description below. Other features,
objects, and advantages will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF DRAWINGS
[0012] FIG. 1A shows an exemplary system to acquire ECG waveform
data, to process the ECG waveform to identify non-cardiac muscle
activity in each of a number of time intervals, and to use the
identified non-cardiac muscle activity to display a graphical
representation of patient activity in each of the time
intervals.
[0013] FIG. 1B illustrates a typical example of an ECG waveform
acquired by a subcutaneous cardiac rhythm monitoring device.
[0014] FIGS. 2A-2E show some illustrative examples of two- or
three-dimensional histogram-type graphical representations to
present the accumulated physical activity information during each
of a number of specified time intervals.
[0015] FIG. 3A shows an exemplary medical device configured for
subcutaneous cardiac rhythm monitoring that includes receiving ECG
waveforms.
[0016] FIG. 3B shows an exemplary signal processing chain to detect
and measure non-cardiac muscle activity episodes in an ECG
waveform.
[0017] FIG. 4A shows a typical ECG waveform plotted on a chart in
which the x-axis represents the time in seconds and the y-axis
represents the voltage in millivolts.
[0018] FIG. 4B shows an example of a processed ECG waveform, which
represents a version of the ECG waveform of FIG. 4A after
processing to identify non-cardiac muscle activity events.
[0019] FIG. 5 shows an exemplary diagnostic method for processing
ECG waveforms to detect non-cardiac muscle activity, and to notify
a patient's health care provider if the detected activity indicates
that the patient's activity level is out of a healthy range.
[0020] FIG. 6 shows an exemplary diagnostic method for operating a
device implanted with subcutaneous electrodes to collect ECG
waveforms, and to transmit the ECG waveforms out of the patient for
post-processing.
[0021] FIG. 7 shows an exemplary therapeutic system configured to
dynamically control cardiac stimulation based on physical activity
information derived from an ECG waveform.
[0022] FIG. 8 shows an exemplary therapeutic method for updating a
pacing rate in response to estimated physical activity levels
derived from ECG waveforms.
[0023] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION OF ILLUSTRATIVE EXAMPLES
[0024] When diagnosing or counseling a patient, medical care
providers often would like to have accurate information about the
patient's level of physical activity. Physical activity level, such
as whether the patient is generally sedentary or highly active, can
sometimes give valuable insight about the patient's overall health.
For example, information about physical activity levels at night
may shed light on the quality of the patient's sleep, and
information about physical activity levels during waking hours may
shed light on the quality of exercise or stress level for that
patient.
[0025] Physical activity information may be valuable not only in
accurately diagnosing health issues, but also in prescribing an
appropriate treatment. In an illustrative example, a relatively
small subdermal body electrical signal sensor system is implanted
to temporarily monitor body electrical signals. If the monitored
body signals include ECG signals, the ECG signals may be evaluated
(e.g., to detect arrhythmias) to make a diagnosis of cardiac
arrhythmias, or to determine whether the patient is a good
candidate for an implantable medical device, such as a pacemaker,
cardioverter-defibrillator, or other cardiac rhythm management
device. The collected ECG information may also pick up important
transient events that are unlikely to be detected without
continuous monitoring over a period of time. After evaluating the
ECG data collected over a period of time, an appropriate treatment
plan may be designed by the medical care team. For example, a
cardiologist may determine what type of implantable medical device
to use, and how best to apply it (e.g., where to apply the
electrodes to stimulate the heart, criteria for initiating and
parameters for delivering electrical stimulation) to meet the needs
of the patient. With the addition of some hardware and/or software
to the ECG sensor system, the acquired ECG information can further
be evaluated to identify physical activity level over time. When
considered together, ECG and physical activity information provide
a substantially more complete picture of the health of the patient
than either ECG or physical activity information taken alone. In
particular, features of the ECG may be explained or better
understood after correlation with contemporaneous patient activity
measurements. For example, ECG features may be determined to have
occurred during sleep or after vigorous physical exercise, and thus
the ECG features may be evaluated in the context of the physical
activity information.
[0026] FIG. 1A shows an exemplary system 100 to acquire ECG
waveform data, to process the ECG waveform to identify non-cardiac
muscle activity in each of a number of time intervals, and to
present the identified non-cardiac muscle activity information in a
graphical representation 105 of patient activity in each of the
time intervals.
[0027] In one embodiment, the graphical representation 105 includes
a histogram representing the number of non-cardiac muscle activity
events that were identified in a portion of an ECG waveform. In an
illustrative example, a histogram may graphically present, for each
of a number of time intervals, a tally of the number of non-cardiac
muscle activity events detected in a portion of an ECG waveform
acquired during the corresponding time interval. In some
embodiments, the graphical representation further illustrates
intensity and/or duration of non-cardiac muscle activity during
each time interval. Health care providers may advantageously review
such graphical representations, and "at a glance" diagnose trends
or status of the level and/or quality of patient activity, which
can be a significant indicator of patient health. Furthermore,
patient activity levels may be advantageously monitored in
substantially real time or near real time, and the system 100 may
automatically generate various levels of warning or notification
messages to alert remote medical personnel and/or the patient if
patient activity levels fall outside of certain predetermined
ranges.
[0028] The exemplary system 100 includes a medical device 110
implanted within a body of a patient 115. The medical device 110
has at least two (e.g., three or four or more) subcutaneous
electrodes configured for sensing ECG waveforms associated with
electrical activity of the patient's heart 120. In some examples,
at least one of the subcutaneous electrodes is positioned
substantially in or around the heart 120 to increase sensitivity to
electrical signals associated with cardiac muscle activity. In some
examples, one electrode of the medical device 110 is disposed on a
housing (e.g., "can") of the device 110.
[0029] Also within the patient's 115 body are non-cardiac muscle
tissues 125. By way of example, and not limitation, non-cardiac
muscle tissues 125 may include muscle and/or fascia tissues in and
around the chest (e.g., pectorals), abdomen, back, or neck regions.
During patient activity (e.g., exercise, lifting, arm movements,
and the like), the electrodes of the medical device 110 may receive
electrical signals associated with activity in the non-cardiac
muscle tissue 125 as well as electrical signals associated with
activity of the heart 120. As such, signals for the non-cardiac
muscle activity combine with (e.g., add to) the ECG waveform
signals associated with pumping of the heart 120. The absence of
non-cardiac muscle signals generally indicates a corresponding
absence of physical activity.
[0030] In one illustrative example, the medical device 110 receives
an electrical waveform with a combination of cardiac signals
associated with the heart 120 and non-cardiac muscle activity
signals associated with the non-cardiac muscle tissues 125. At one
or more points in the system 100, processing is performed to detect
non-cardiac muscle activity events that may be indicated by the
non-cardiac portions of the combined waveform.
[0031] The system 100 also includes an exemplary local
communication module 130 that is local to the patient 115 and
configured to communicate with the medical device 110 over a
transdermal wireless link. In the depicted example, the module 130
performs a repeater function to support a further communication
link to a remote data processing facility 135, for example. In some
embodiments, the module 130 also performs operations to detect
non-cardiac muscle activity events. Such detection operations may
be performed automatically, or in response to a request or
predetermined schedule, for example, from a system operator,
medical personnel, or the patient 115. The processing may include
analog and/or digital signal processing, depending on whether the
information is received from the device 110 in analog (e.g.,
continuous waveform signal) or digital (e.g., sampled data)
format.
[0032] The exemplary remote data processing facility 135 of the
system 100 provides data processing, storage, analysis, monitoring,
and distribution to remote medical personnel, such as a physician's
remote access node 140 and a medical clinic 145.
[0033] As an illustrative example, a data server 150 at the remote
data processing facility may receive over the Internet secure
(e.g., encrypted) data packets that contain ECG waveform
information from the local communication module 130 located in
another country. To the extent the received information requires
further processing to detect and/or characterize the occurrences,
intensities (e.g., amplitudes), and/or durations over one or more
selected thresholds of non-cardiac muscle activity, a processor 155
performs suitable analog and/or digital signal processing on the
received information. Examples of such processing are further
described with reference to FIG. 4B. The processor 155 compares the
resulting processed data, which represents activity level
information about the patient 115, to a rules database 160. The
exemplary rules database 160 contains, for example, one or more
predetermined conditions that, if met by the activity level
information, trigger a notification message to a graphic user
interface 165 (GUI) being monitored by a medical analyst, the
physician at the remote access node 140, and/or a care provider at
the clinic 145. In some examples, a notification message or action
may include automatically attempting to contact the patient 115 by
phone, or even dispatching emergency personnel (e.g., by
ambulance). A medical analyst may monitor trends in patient
activity levels, and manually watch for abnormal conditions that
indicate the need for attention from medical personnel. The medical
analyst may, for example, update the rules database 160 to set
conditions and notification/action responses under the direction of
the medical team responsible for the patient 115.
[0034] The remote data processing facility 135 further includes a
data server 170 that provides for communication between the
facility 135, the remote access node 140, and the clinic 145. The
notification messages generated in response to abnormal activity
level information are sent from the data server 170 to an exemplary
wide area network 175 (WAN). The WAN 175 enhances the flexibility
to communicate diagnostic patient activity level information with,
for example, specialized health care providers who are at remote
locations. In an illustrative example, current and historical
patient activity level information may be sent via the WAN 175 for
display in the form of the graphical representation 105 that is
rendered on a display device being monitored by a physician at the
remote access node 140. The display device may include a color
screen as part of, for example, a laptop computer with a wired
network connection to the WAN 175, or a handheld personal
communication device with wireless connection to the WAN 175 to
enable data exchanges with the remote data processing facility
135.
[0035] The clinic 145 includes a server 180 for communication of
raw ECG data, partially processed muscle activity information,
and/or fully processed activity information. Information received
by the server 180 is processed by the processor 185. In some
examples, the processor 185 may perform digital and/or analog
signal processing to identify and/or characterize the number,
intensity, and/or duration of non-cardiac muscle activity events
during a number of time intervals, which time intervals may be
specified, for example, by a health care provider. The clinic 145
further includes a local communication module 187 that is
configured to communicate with the medical device 110 over a
transdermal wireless link when the patient 115 is at the clinic
145. The processor 185 can directly send commands to the medical
device 110, and receive ECG waveform data directly via the local
communication module 187. In some embodiments, the local
communication module 187 may have certain features (e.g.,
programming, diagnostic) capabilities enabled that are not enabled
in a similar module (e.g., the local communication module 130
located in a home of the patient 115) that is outside of the health
care provider's direct control.
[0036] In an illustrative example, a care provider may operate the
GUI 190 to select or specify parameters for processing and/or
displaying non-cardiac muscle activity information. For example,
the user may specify that the GUI 190 display non-cardiac patient
activity information in two-dimensional or three-dimensional
formats, examples of which are described with reference to FIGS.
2A-2E.
[0037] FIG. 1B illustrates a typical example of an ECG waveform 195
acquired by a subcutaneous cardiac rhythm monitoring device. The
ECG waveform 195 includes a combination of electrical signals
associated with activity in the non-cardiac muscle tissue 125
superimposed with electrical signals associated with activity of
the heart 120. The electrical signals associated with activity of
the heart 120 appear as a substantially repeating series of QRS
complex signals. Signals associated with non-cardiac muscle
activity appear as an episode 197 of relatively higher frequency
signals superimposed on the repeating cardiac waveforms.
[0038] In various examples, the system 100 operates to acquire the
ECG waveform 195 using subcutaneous electrodes, processes the ECG
waveform 195 to identify episodes of non-cardiac muscle activity,
such as the episode 197, accumulate information about the
identified episodes that occur during specified time intervals, and
formats the accumulated information for display in the graphical
representation 105. In various examples, the accumulated
information may include an aggregate of the number of episodes that
occur during the specified time interval, the intensity (e.g., peak
or average amplitude), an accumulated or average duration of the
episode (e.g., time that the episode satisfies a minimum threshold
intensity), and/or an integration of the intensity of each event
over time. Various examples may be displayed in a two- or
three-dimensional histogram or trend type graphical
representation.
[0039] FIGS. 2A-2E show some illustrative examples of two- or
three-dimensional histogram-type graphical representations to
present the accumulated physical activity information during each
of a number of specified time intervals. Such graphical
presentations, such as the graphical presentation 105, may
facilitate rapid "at a glance" review of physical activity levels
or trends, which are sometimes key diagnostic indicators of patient
health, by a physician or other health care personnel, for
example.
[0040] FIGS. 2A-2B show exemplary two dimensional graphical
representations 200, 205, for an active patient and a sedentary
patient, respectively. The vertical axes of both graphs 200, 205
represent an average rate at which non-cardiac muscle activity
signals in a patient's ECG waveform exceeded minimum threshold
detection criteria. In this example, the active patient had
significantly higher rates of non-cardiac muscle activity than the
sedentary patient had during the middle hours of the day. Both
patients had similar rates during some of the early morning hours
corresponding to sleep times.
[0041] FIGS. 2C-2E show exemplary graphical representations that
provide information about both the quantity (e.g., number of events
detected in each time interval) and the quality (e.g., intensity
and/or duration) of the patient's activity as derived from
non-cardiac signals detected in an ECG waveform.
[0042] FIG. 2C shows an exemplary two-dimensional graphical
representation 215 that depicts rates at which a patient's
non-cardiac muscle activity signals in the patient's ECG waveform
exceeded any of three different intensity levels (e.g., low,
medium, and high). In various examples, any number of intensity
levels and threshold criteria may be specified by the reviewing
medical personnel to produce a chart with some further qualitative
information about the activity.
[0043] In the depicted example, 12 total events were detected both
in time interval 6 and again in time interval 10. In time interval
10, the events were primarily low intensity, and no high intensity
events were detected. In time interval 6, the detected events were
of several different intensity levels, including 2 high intensity
events. Accordingly, a reviewing health care provider can
advantageously review the breakdown of events by intensity to
rapidly evaluate the patient's physical activity level during
various time intervals based on both the quantity and the quality
of their non-cardiac muscle activity.
[0044] FIG. 2D shows an exemplary two-dimensional graphical
representation 220 that depicts rates at which non-cardiac muscle
activity signals in the patient's ECG waveform exceeded any of
three different durations (e.g., short, medium, and long). In
various examples, any number of duration levels and threshold
criteria may be specified by the reviewing medical personnel to
produce a chart with some further qualitative information about the
activity.
[0045] In the depicted example, 10 short duration events were
detected both in time interval 7 and again in time interval 10. In
time interval 10, the events were primarily short duration, and no
long duration events were detected. In time interval 7, the
detected events were of several different intensity levels,
including 9 long duration events. Accordingly, a reviewing health
care provider can advantageously review the breakdown of events by
duration to rapidly evaluate the patient's physical activity level
based on both the quantity and the quality of their non-cardiac
muscle activity over a number of time intervals.
[0046] FIG. 2E shows an exemplary three-dimensional graphical
representation 225 that is similar to FIGS. 2A-2B in that it
depicts in one dimension (labeled "Events") the number of times
that non-cardiac muscle activity signals in the patient's ECG
waveform were detected. In an orthogonal dimension (labeled
"Integral"), the graphical representation 225 further indicates a
time integral (e.g., numerical integration, analog integration, or
accumulation over time) of the intensity of patient's non-cardiac
muscle activity signal that was accumulated throughout each time
interval. In various examples, any number of integrator gain (e.g.,
scale factor) values may be specified by the reviewing medical
personnel to produce a chart with some further qualitative
information about the activity of the patient.
[0047] In the depicted example, a similar number of events were
detected both in a time interval 230 and again in a time interval
235. However, the integral value in the time interval 230 is
substantially higher than the integral value in the time interval
235. This indicates that for the same number of events, the
patient's activities were significantly more intense and/or of
longer duration during the time interval 230. Accordingly, a
reviewing health care provider can advantageously review the
breakdown of events as an integration of intensity levels of
non-cardiac muscle activity during each of a number of time
intervals to rapidly evaluate the patient's physical activity level
based on both the quantity and the quality of their non-cardiac
muscle activity.
[0048] The portion of the ECG waveform acquired during each of a
number of user-specified time intervals may be processed to
identify non-cardiac muscle activity information. The identified
muscle activity information is then accumulated to determine an
activity level to display in the specified time interval of the
graphical representation 105.
[0049] In various examples, the time intervals used for display of
the patient activity information may be specified to span certain
sequential periods of time (e.g., every 5 minutes, every 15
minutes, every hour, every 3 hours, every 12 hours, every 24 hours,
every week, every month, or any other suitable time interval). In
other examples, non-sequential (e.g., separated by gaps of time)
and/or non-uniform (e.g., different spans of time) time intervals
may be specified for processing or display. For example, exemplary
non-uniform time intervals may include a time interval specified to
span sleeping hours, followed by a time interval over the morning
wake time for the patient, followed by an afternoon time interval
and an evening time interval.
[0050] FIG. 3A shows an exemplary medical device 300 configured for
monitoring subcutaneous cardiac rhythms by receiving ECG waveforms.
In an illustrative example, the medical device 300 may be used as
the medical device 110 described with reference to FIG. 1A. The
device 300 includes a housing 305, a lead 310, and an electrode 315
adapted to be positioned in or around a heart, or over a skeletal
muscle such as the chest muscle. In some other examples, the
medical device may have one or more additional leads with one or
more additional electrodes for detecting ECG waveforms.
[0051] In some embodiments, at least a portion of the housing 305
may be conductive to serve as an electrode for sensing ECG
waveforms. In some embodiments, at least a portion of the housing
305 may be positioned in close proximity or in at least partial
direct contact with non-cardiac tissues that generate and/or
conduct electrical signals associated with non-cardiac muscle
activity. For example, one exemplary method involves positioning
the housing 305 in substantially direct contact with muscle fascia.
Sensitivity to electrical signals associated with non-cardiac
muscle may advantageously be improved by positioning the housing
305 or another subcutaneous electrode in close proximity to muscle
fascia in a pectoral region, for example. Further improvements to
sensitivity to non-cardiac muscle activity signals superimposed on
an ECG waveform may be realized by substantially reducing or
eliminating an insulative coating from portions of the housing 305
that serve as an ECG electrode. In some embodiments, an insulative
coating, such as parylene, is removed, substantially reduced in
thickness, or not applied to at least a portion of the exterior of
the housing 305.
[0052] Signals received by the electrode 305, 315 are communicated
to a signal conditioning and analog-to-digital conversion module
320 in the housing 305. The module 320 includes analog signal
conditioning circuitry to, for example, limit, filter, amplify,
attenuate, rectify, and/or sample the received ECG waveform as a
continuous-time analog signal. In some examples, the analog signal
conditioning in the module 320 may divide the ECG waveform at some
point for processing in two substantially separate signal
processing chains: one for ECG monitoring of electrical signals
associated with cardiac activity, and one for detecting electrical
activity associated with non-cardiac activity. For example, to the
extent that ECG signals have lower frequency components than
non-cardiac muscle signals exhibit, a signal processing chain to
detect non-cardiac muscle activity may provide substantially more
attenuation (or less amplification) of low frequencies than the ECG
signal processing chain provides. For instance, ECG signals may
include frequency contents with substantial energy between about 10
Hz and about 20 Hz, with some components at higher frequencies, but
in general under about 150 Hz. One reference, Medical
instrumentation, John G Webster Editor, Chapter 6, page 259,
published by John Wiley and Sons, 1998, 3.sup.rd edition ISBN
0-471-15368-0, describes example EMG signals as including frequency
components between about 25 Hz and about 2 kiloHertz.
[0053] In some implementations, analog signal conditioning of the
non-cardiac muscles signals may be supplemented and/or
substantially replaced by digital signal processing that is
performed on samples (e.g., a digital representation) of the ECG
waveform. For example, digital signal processing techniques may
supplement and/or substantially replace analog signal conditioning
circuitry to discriminate and separately process (e.g., filter,
amplify, detect, and/or characterize) non-cardiac muscle noise
information from ECG information.
[0054] After sampling and conversion to a digital representation in
an analog-to-digital conversion process, the ECG waveform may be
stored in a memory location and/or processed (e.g., in a digital
FIR filter). In the depicted example, the samples are communicated
over a digital bus 325 for processing by a processor 330, storage
in a memory 335 or a non-volatile memory (NVM) 340, and/or a
transmission by a telemetry module 345 via an antenna 350. Handling
of the sampled data may be supervised by the processor 330, which
may be supplemented by one or more processing elements configured
to supervise, control, and monitor operations by executing
instructions retrieved from storage in a memory, such as the NVM
340.
[0055] The depicted embodiment further includes a time keeping unit
(TKU) 355, which may associate time stamp information (e.g., date
and time to within fractions of a second) with sampled data, and a
battery module 360 to supply power to operate the device 305. In
some examples, the TKU 335 includes a real time clock, and/or a
substantially stable time reference (e.g., oscillator) to which
time intervals may be synchronized. Some embodiments use the time
stamp information to determine which segments of the ECG waveform
to process when determining non-cardiac muscle activity during a
specified time interval. In other examples, time information from
the TKU 355 may be associated with processed information (e.g.,
intensity, duration, integral contribution) about each non-cardiac
muscle activity event so that the event and time information may be
communicated as a packet of information via the telemetry module
345.
[0056] For example, the processor 330 may supervise various
operations, such as waveform data collection and transdermal
communications. The processor 330 may include one or more of the
following: a math coprocessor, an ASIC (application specific
integrated circuit), DSP (digital signal processor), discrete or
integrated analog and/or digital circuits, and a dedicated digital
logic architecture to perform mathematics functions, for example.
The math coprocessor may perform various operations that include
floating point arithmetic, signal processing, digital filtering
(e.g., IIR, FIR) and/or numerical operations (e.g., curve fitting,
numerical derivative computation, numerical integration, fast
Fourier transformation (FFT), and interpolation).
[0057] In some embodiments, the processor 330 may also perform
operations in response to input data or commands received via a
wireless (e.g., transdermal) communication link. For example,
programming instructions and or commands may be executed as
received via the antenna 350.
[0058] In this example, the NVM 340 is coupled to the processor 330
by the digital address/data bus 325. The processor 330 may execute
instructions and retrieve information stored in the NVM 340 via the
bus 325. The NVM 340 may include a number of code modules (not
shown) containing instructions that, when executed by the processor
330, cause the performance of, for example, ECG waveform
measurement operations, or house-keeping operations in support of
the device 300 (e.g., user interface, programming, boot-up,
configurations, and the like).
[0059] In some embodiments, an analog version of the ECG signal may
be coupled to a control input of the telemetry module, which is
configured to modulate a continuous (e.g., analog) transmission
carrier signal (e.g., AM (amplitude modulated), FM (frequency
modulated), PM (phase modulated), frequency shift keying (FSK),
pulse-width modulated (PWM), etc. . . . ) to communicate raw or
partially processed ECG waveform information in an analog domain to
a receiver outside the patient, such as the local communication
module 130, which may retransmit or further process the ECG
waveform in the analog and/or digital domains.
[0060] In some examples, some processing operations to detect
non-cardiac muscle activity events may be implemented with hardware
components and/or digital signal processing, that apply, for
example, frequency selective filtering (e.g., high-pass, low-pass,
band-pass, band-reject) to the received waveform. Further exemplary
processing of the received waveform in an analog domain includes a
rectification stage, followed by a filtering (e.g., capacitive)
stage, and a threshold detection module to detect when the filtered
signal satisfies one or more fixed or user-specified (e.g.,
variable) threshold criteria.
[0061] FIG. 3B shows an exemplary signal processing chain 365 to
detect and measure non-cardiac muscle activity episodes in an ECG
waveform. In various implementations, this may be implemented in
part or entirely in either the analog domain or the digital domain.
With reference to FIG. 1A, any of the aspects of the signal
processing chain 365 may be implemented in any node of the system
100, including but not limited to the medical device 110, the local
communication module 130, the remote data processing facility 135,
the remote access node 140, or the clinic 145.
[0062] In this example, the signal processing chain 365 includes a
high pass filter module 375, a rectification module 380, a low pass
filter module 385, a threshold module 390, and a counter module
395.
[0063] The high pass filter module 375 may attenuate low
frequencies, such as the low frequencies evident in the typical ECG
waveform 195 of FIG. 1B. Accordingly, the module 375 substantially
removes at least some low frequency components of the electrical
signals that are associated with the heart, but without
substantially attenuating signal components in the frequency range
of interest, including signal components associated with
non-cardiac muscle activity. In an analog example, a high pass
filter may include a series capacitance, as is well known in the
art.
[0064] The rectification module 380 and the low pass filter module
385 may be considered to operate like an envelope detector that
substantially tracks an envelope of the peaks of oscillations, with
some decay over time. This also converts the high frequency signal
components associated with non-cardiac muscle activity into a
unidirectional voltage signal that is suitable for comparison to a
threshold.
[0065] The threshold module 390 compares the output of the module
385 to one or more thresholds. Each threshold that is used for
detecting non-cardiac muscle activity events is independently
adjustable via an amplitude threshold input and a time threshold
input. To detect a non-cardiac muscle activity event, for example,
the input to the module 390 must be over a specified amplitude
threshold for at least a specified time threshold. This may reduce
responding to short duration noise glitches (e.g., electromagnetic
interference, electrostatic discharge, or the like). Some
embodiments may employ a user-specifiable amount of hysteresis,
whereby a second (e.g., lower) amplitude threshold may be specified
such that in order to detect an event, the input signal must exceed
the first (higher) threshold and then remain above the second
(lower) threshold for at least the specified time threshold. In
various embodiments, multiple compound time-threshold functions may
be specified (e.g., with or without hysteresis) as criteria for
detecting various levels of non-cardiac muscle activity events.
[0066] Upon detecting a non-cardiac muscle activity event, a signal
is sent to increment a counter in the counter module 395. The
counter module 395 of this example is reset by a signal on an
interval input, which is activated at the start or end of every
time interval. Upon detecting the start of a time interval, the
counter is reset to zero. Upon detecting the end of a time
interval, the count is stored in a memory for subsequent processing
or display in a graphical representation, such as the graphical
representation 105 of FIG. 1A.
[0067] In other embodiments, the threshold module 390 may further
measure and/or record amplitude as a measure of intensity, and the
intensity value may be displayed in a graphical representation such
as the example described with reference to FIG. 2C. Similarly, the
threshold module 390 may further measure and/or record time over a
threshold (or compound threshold) as a measure of duration, and the
duration value may be displayed in a graphical representation such
as the example described with reference to FIG. 2D.
[0068] In some embodiments, the threshold module 390 may measure
and/or record a time integral of the intensity throughout a time
interval, and the integral value may be displayed in a graphical
representation such as the example described with reference to FIG.
2E. For example, the integration may accumulate intensity in excess
of a threshold. In the analog domain, integration circuits may be
used, and their outputs may optionally be sampled, and reset so
that integration only occurs while a non-cardiac muscle activity
event is active. As such, the integral result for a time interval
may be a sum of the integrated values that were sampled and
recorded during the time interval. Similarly, numerical integration
may be performed in the digital domain whenever a non-cardiac
muscle activity event is active.
[0069] In other embodiments, the signal processing chain may
further include amplification (e.g., differential) or frequency
shifting (e.g., by mixing with a higher frequency for filtering
purposes).
[0070] FIG. 4A shows a typical ECG waveform 400 plotted on a chart
in which the x-axis represents the time in seconds and the y-axis
represents the voltage in millivolts. FIG. 4B shows an example of a
processed ECG waveform 405, which represents a version of the ECG
waveform 400 after processing to identify non-cardiac muscle
activity events. In some examples, the processed ECG waveform 405
may use a channel of an ECG monitor that is normally used for
R-wave detection. The waveform 405 is a high-pass filtered,
rectified, and smoothed version of the original ECG waveform 400.
Non-cardiac muscle activity events are indicated by a rectangular
area 410. In this example, the criteria for detecting non-cardiac
muscle activity events are that the minimum rectified signal
amplitude is above a user-specified threshold voltage 415 (e.g., 50
A/D counts) for a user-specified duration (e.g., 0.5 seconds). The
rectangular area 410 at the bottom of the graph indicates when the
foregoing exemplary criteria are met in this example.
[0071] FIGS. 5-6 illustrate some exemplary processes associated
with various components of the system 100 of FIG. 1A. Some or all
of the steps of these processes may be performed by one or more
processors executing instructions, which instructions may be
encoded in one or more code modules stored in at least one data
store.
[0072] FIG. 5 shows an exemplary diagnostic method 500 for
processing ECG waveforms to detect non-cardiac muscle activity, and
to notify a patient's health care provider if the detected activity
indicates that the patient's activity level is out of a healthy
range. In some examples, the notification may include a graphical
representation of the patient's activity level in each of a number
of time intervals, as shown for example in the graphical
representations 105 of FIG. 1A, or those described with reference
to FIGS. 2A-2E. In the illustrative example described with
reference to FIG. 1A, some or all of the steps of the method 500
may be performed in any or each of the remote data processing
facility 135, the remote access node 140, and/or the clinic
145.
[0073] The method 500 includes receiving ECG sampled waveform data
at step 505. This may occur via a communication link from the local
communication module 130, or from a database in the facility 135
that contains previously stored ECG waveform data samples collected
by an implanted medical device, such as the medical device 110. At
steps 510-520, a processor, such as the processor 155, retrieves
amplitude threshold parameters, time threshold parameters, and time
interval parameters, such as those described with reference to the
threshold module 390 and the counter module 395 of FIG. 3B. The
parameters of steps 510-520 may be adjusted or specified by health
care providers, programmed as a fixed value within an implanted ECG
monitor device, adjusted according to a measured baseline for the
patient, and/or set according to statistical norms for the
patient's peers (e.g., similar age, weight, health status, sex, or
the like). For example, the time interval parameters may specify
uniform-length, adjacent-in-time intervals (e.g., hourly, every 90
minutes, and so on), or custom time intervals, which may be
non-uniform and have intervals separated by gaps in time.
[0074] Having retrieved the parameters for processing the ECG
waveform data, a first time interval is selected at step 525 to
process the ECG waveform samples. At step 530, samples of the ECG
waveform within the selected time interval are pre-processed to
produce a waveform representative of electrical signals associated
with non-cardiac muscle activity, for example, as described with
reference to modules 375-385 in FIG. 3B, with reference to waveform
405 in FIG. 4B, and elsewhere herein. The retrieved time and
amplitude threshold parameters are then applied in step 535 to scan
the pre-processed waveform to identify non-cardiac muscle activity
events. Examples of step 535 are described with reference to the
threshold module 390 in FIG. 3B or to the areas 410 in FIG. 4B.
After identifying the events, one or more values representing
non-cardiac muscle activity are accumulated, at step 540, for the
selected time period. Accumulated values may include, but are not
limited to, a number of events, average event intensity, average
event duration, weighted sum of events as a function of intensity
and duration, and a time integral of the intensity. The accumulated
information for the selected time interval is stored in a data
store at step 545 for subsequent retrieval and rendering, for
example, as a portion of a histogram of patient activity level in
the selected time interval.
[0075] If, at step 550, other time intervals have been requested to
be checked for non-cardiac muscle activity, then another time
interval is selected at step 555, and the step 530 is repeated.
Otherwise, a processor checks at step 560 whether all of the
accumulated values are within predetermined limits according to
specified alarm parameters. Predetermined limits and alarm
parameters may be stored, for example, in the rules database 160 of
FIG. 1. In some examples, predetermined limits reflect a range of
healthy levels of physical activity for the patient, or trends in
the patient's physical activity levels (e.g., the rate of change in
the patient's physical activity may be increasing or decreasing too
rapidly). If the accumulated values are within limits, then the
accumulated value information for each of the time intervals
selected for the method 500 is, at step 565, stored in a data store
for subsequent retrieval and display as a graphical representation.
Otherwise, at step 570, an alarm message is generated to notify the
health care provider team, and optionally contact the patient,
about the potentially unhealthy level of physical activity. In one
embodiment, the alarm message is an electronic mail message that
contains an attached graphical representation of the patient's
activity level over the relevant time intervals of interest.
[0076] In one embodiment, the alarm parameters of step 560 may
include criteria for comparing qualitative information about the
patient's physical activity levels to prescribed normal ranges. For
example, if 10 levels of duration or intensity are monitored, then
an alarm condition may be set for each of the levels. In one
example, an alarm condition occurs if more than 3 events at an
intensity level of 9 out of a maximum of 10 are detected within a
particular time interval. The alarm condition profile may have
different minimum and/or maximum limits, and the limits may be
adjusted according to time of day. For example, a low priority
alarm may be activated if the patient repeatedly has more than an
average of 3 medium intensity events very early in the morning
during normal sleeping hours. This may indicate that the patient is
not getting sufficiently restful sleep, for example. Similarly, if
a patient is averaging less than a minimum number of medium
duration physical activity events during the day, a physician may
want to call the patient and inquire about his declining activity
and related symptoms, or counsel the patient to increase exercise
activities.
[0077] FIG. 6 shows an exemplary diagnostic method 600 for
operating a device implanted with subcutaneous electrodes to
collect ECG waveforms, and to transmit the ECG waveforms out of the
patient for post-processing, for example, according to the method
500 of FIG. 5. In the illustrative example described with reference
to FIG. 1A, the steps of the method 600 may be performed by the
medical device 110.
[0078] The method 600 includes selecting a time interval to sample
an ECG waveform at step 605. In some embodiments, the ECG waveform
may be sampled substantially continuously, periodically, or at
specified time intervals. In one example, a processor cooperates
with a time keeping unit, such as the TKU 335 described with
reference to FIG. 3A, to determine if the current time is within
the selected time interval. At step 610, step 605 is repeated until
the start of the selected time interval. Upon the start of the
selected time interval, sampling is performed to convert an analog
ECG waveform received by subcutaneous electrodes to a digital
representation at step 615. At step 620, the sampled ECG waveform
data point is stored in a data store. At step 625, step 615 is
repeated until the end of the selected time interval is reached.
After reaching the end of the selected time interval, a check is
made at step 630 whether or not to transmit the sampled waveform
out of the body.
[0079] In this example, if the processor is not instructed to
transmit the sampled waveform data out of the body, then the
processor selects a next time interval to sample the ECG waveform
at step 635, and the step 610 is repeated. Otherwise, the processor
sends the stored data to a transmitter module. In the example of
FIG. 3A, sampled ECG waveform data stored in the memory 335 may be
transferred to the telemetry module 345 for transmission to the
local communication module 130 outside of the body.
[0080] In an illustrative example, an instruction whether or not to
transmit the sampled ECG waveform out of the body (at step 630) is
stored as a flag in a register that is programmed using, for
example, the local communication module 130 or a hand-held
programming device that communicates with the implanted medical
device 110. A request to transmit sampled ECG waveform data may
originate from any node in the system 100, including but not
limited to any of the remote data processing facility 135, the
remote access node 140, and/or the clinic 145.
[0081] In another embodiment, the sampled ECG data may be queued
for transmission (step 620) as the sampled data is stored in a
memory device. In some implementations, a packet of information may
include a data payload representing a number of sequential data
samples obtained by repeating step 615. In one example, the payload
contains only a portion of the samples for the entire time
interval. A data stream containing one or more communication
packets with sampled data information may further include other
parameters, which may include, but are not limited to, patient or
device identification information, time stamp information, sampling
rate, gain and/or phase settings for the analog-to-digital
conversion, or other configuration settings of the medical device
110, as well as encryption, error detection (e.g., checksum),
and/or error correction (e.g., error correction code)
information.
[0082] In various examples, the samples stored in a memory of the
implanted medical device 110 may be partially processed (e.g.,
using FIR, decimation, or other digital signal processing
techniques) prior to transmission out of the body.
[0083] Various examples of diagnostic apparatus and methods have
been described with reference to FIG. 1A-FIG. 6. FIG. 7 shows an
exemplary therapeutic system 700 configured to dynamically control
cardiac stimulation based on physical activity information derived
from an ECG waveform.
[0084] The therapeutic system 700 includes a medical device 705
that is implantable within a body of a patient and configured to
provide pacing responsive to an indication of physical activity
level derived from ECG waveform information. The medical device
includes a set of leads 710 with subcutaneous electrodes for
sensing ECG waveforms, and a set of leads 715 for applying
electrical stimulation to a heart 720 in the patient. The device
705 is, for example, a pacemaker that applies an electrical
stimulus through the leads 715 to stimulate the heart 720 to beat
at a prescribed pacing rate. In various examples, the system 700
provides a form of cardiac resynchronization therapy (CRT), and/or
provides subcutaneous electrical stimulation for therapeutic
pacing, cardioversion, and/or defibrillation. In some other
embodiments, the leads 715 may be omitted, for example, if one of
the leads 715 includes an intra-cardiac electrode or epicardial
electrode, in combination with a can electrode on the housing on
705, that provides both for muscle noise detection by sensing of
the heart signals and for therapeutic stimulation.
[0085] The electrical stimulation applied to the heart 120 is
dynamically controlled based on physical activity levels determined
by processing one or more ECG waveforms received by the device 705.
Exemplary processes and apparatus for processing ECG waveforms to
identify and characterize non-cardiac muscle activity events are
described elsewhere herein, such as with reference to FIG. 5, for
example.
[0086] In particular, the device 705 can operate in a mode that is
responsive to non-cardiac muscle activity signals detected on the
ECG waveform signals. For example, as the number, intensity,
duration of non-cardiac events increases, a corresponding change
may be applied to the pacing rate. For example, pacing rate may be
increased upon sensing increased activity level. Some embodiments
may be responsive to a moving average of physical activity. Various
operating parameters (e.g., time delay, acceleration, minimum rate,
maximum rate, and ratio of pacing rate to activity level may be
programmable, either locally or remotely, by a physician, and/or
programmed to a predetermined (e.g., default) level. In some
implementations, a pacing rate offset may be used for exercise
conditions. Some implementation may include a time delay for
changing a pacing rate in response to changes in the intensity,
duration, and/or number of activity events detected. For example,
pacing rate profile may be substantially maintained for a
predetermined time period (e.g., about 5, 10, 15, 20, 25, 30, 35,
40, 45, 50, or about 60 seconds or more) after detecting a change
in activity level. The time delay may be a function of current
activity level, whereby the response delay may be longer at low
activity levels and shorter at higher activity levels. In some
examples, one or more acceleration and/or deceleration rates may be
programmed as a function of recent activity levels as determined
from non-cardiac muscle activity derived from ECG signals, such as
for periods during or after exercise. Examples of techniques (e.g.,
rate adaptive pacing) for controlling pacing rate based on activity
are described in U.S. Pat. No. 5,243,979, U.S. Pat. No. 6,449,508,
U.S. Pat. No. 5,423,870, or "Rate Adaptive Pacing", edited by David
G. Benditt, Boston: Blackwell Scientific Publications, copyright
1993.
[0087] In accordance with various techniques that have been
described herein, the device 705 acquires ECG waveform by
monitoring electrical signal potentials detected by the set of
leads 710. In this example, ECG waveform signals may be monitored
among any of the leads 710, 715. In some examples, at least one of
the subcutaneous leads is an exposed conductive surface portion of
a housing of the device 705. In some embodiments, an insulative
coating (e.g., parylene) is partially or substantially removed or
eliminated from at least a portion of an external conductive
surface of the device 705, which may advantageously increase
sensitivity to non-cardiac muscle electrical signals. The housing
of the device 705, and/or one or more subcutaneous ECG electrodes,
may be positioned substantially proximate non-cardiac muscle
tissues so as to increase coupling to electrical signals associated
with non-cardiac muscle activity.
[0088] FIG. 8 shows an exemplary therapeutic method 800 for
updating a pacing rate in response to estimated physical activity
levels derived from ECG waveforms. In an illustrative example, the
method 800 is performed by the medical device 705 of FIG. 7.
[0089] The method 800 includes a step 805 in which an ECG waveform
is received. ECG waveforms are received, for example, by a
processor (not shown) in the device 705. The received ECG waveform
is processed at step 810 to identify non-cardiac muscle activity.
Examples of identifying non-cardiac muscle activity have been
described above, including measurements of frequency, intensity,
and or duration of non-cardiac muscle activity events. In some
examples, the processing may further include a weighted moving
average that attenuates the contributions (e.g., weights) of
samples based on proximity in time to the present time. In some
examples, the processing may be performed within the device
705.
[0090] At step 815, the identified non-cardiac muscle activity
event information is used to estimate a current level of physical
activity. The estimated level of physical activity is, in some
examples, a function of the rate, intensity and/or duration of
recent non-cardiac muscle activity events.
[0091] At step 820, a determination is made whether the current
pacing rate is appropriate based on the estimated activity level.
In one example, this determination is made with reference to a
look-up table that relates activity level to pacing rate. In some
embodiments, the pacing rate may be computed as a function of
activity level and one or more other parameters.
[0092] If it is determined that the pacing rate is appropriate for
the estimated physical activity level, then the step 805 is
repeated. Otherwise, at step 825, the pacing rate is updated based
on the estimated activity level, and then the step 805 is
repeated.
[0093] In some examples, acceleration/deceleration limits may be
placed on the adjustment to the pacing rate in the step 825.
Various gain factors (e.g., to the proportional, integral,
derivative gains) may be applied in the pacing rate control system
that includes the closed loop formed by feeding back the estimated
physical activity level. In some embodiments, one or more control
gains may be configured so that adjustments to the pacing rate
exhibit a well-behaved (e.g., slightly over-damped, slightly
under-damped, stable) response. For example, the pacing rate
control system may be tuned so that the pacing rate exhibits
substantially low overshoot, but without excessively sluggish
response times that would cause the pacing rate to substantially
lag a desired response to the current physical activity level.
[0094] In some embodiments, the ECG information received by the
device 705 may be transmitted out of the patient's body for
post-processing. In some examples, sampled ECG waveform data is
transmitted to a local communication module located outside of the
patient. The ECG waveform data may be communicated to a local or to
remote processor for post-processing to identify patient activity
level information.
[0095] In an illustrative embodiment, ECG waveform data may be
communicated among nodes of the system 100 of FIG. 1. One or more
steps of the method 500 of FIG. 5 may be performed to post-process
the ECG waveform data in one or more of the local communication
module 130, the remote data processing facility 135, the remote
access node 140, and/or the clinic 145.
[0096] After post-processing the ECG waveform to identify a level
of non-cardiac muscle activity, a decision is made at one of the
nodes in the system 100 about whether to increase, decrease, or
maintain a current pacing rate at which the device 705 electrically
stimulates the heart 720. Referring to the example of FIG. 1A, that
determination may be communicated to the device 110 via the local
communication module 130. In some embodiments, the activity level
information is communicated to the medical device 110, and the
medical device determines whether and how to adjust a pacing rate
in accordance with programmed rules stored in a data store in the
device 110. In various embodiments, the frequency, period,
amplitude, duty cycle, waveform shape, and/or polarity of
electrical stimulation to an organ in a patient may be controlled
in response to a determined level of physical activity of the
patient in accordance with examples described herein.
[0097] Although an exemplary system has been described, further
embodiments and applications are contemplated. For example, the
various diagnostic and therapeutic techniques may be applied to
humans as well as other species, for example. Some embodiments may
further be applied to collect and/or process ECG waveform
statistical data for patient studies in clinical, pharmaceutical,
and/or research applications.
[0098] Although depicted in FIG. 1A in a stand-alone (e.g.,
desktop) configuration, other embodiments of the local
communication module 100 are implemented as a body-worn device,
such as a necklace or belt-worn device carried by the patient 115.
The wireless link may communicate unidirectional or bidirectional
information flows between the implanted medical device 110 and the
module 130. The wireless link may be implemented using a variety of
techniques, such as amplitude, phase, and/frequency modulation
(e.g., AM, FM, spread spectrum, or the like), and may use, for
example, optical (e.g., infrared), audio (e.g., ultrasonic), and/or
electromagnetic field (e.g., radio frequency) signal transmission
modes.
[0099] The communication links among the remote data processing
facility 135, the local communication module 130, the remote access
node 140, and the clinic 145 may be wired, wireless, and/or optical
(e.g., fiber-optic), or a combination thereof. At least a portion
of some links may include aspects of the Internet, a VPN (virtual
private network), LAN, WAN, MAN (metropolitan area network), or the
like.
[0100] In various embodiments, data may be communicated
unidirectionally or bidirectionally among any or all of the nodes
of the system. In some embodiments, communication is unidirectional
from the patient 115 to the remote data processing facility 135. In
one example, data is communicated unidirectionally from the remote
data processing facility 135 to the remote access node and to the
clinic 145. In other embodiments, all of the data communication
links among a number of nodes in the system 100 are bidirectional,
such that each node can communicate commands (e.g., requests for
specified parameters, time intervals, or the like), raw data (e.g.,
ECG waveform samples), and/or processed data (e.g., alarm
notification messages, graphical representation 105) with any other
node.
[0101] Some embodiments normalize the lengths of the time intervals
to a reference physical activity pattern such that the graphical
representation 105 will display a normalized value (e.g., 1.0) for
every interval in which the measured activity levels are the same
as a reference activity level during that time interval. For
example, the reference activity level may represent a normal or
healthy level of activity during the time interval. As an
illustration, a user may select reference time intervals that
include a ten hour period spanning regular sleeping hours, and a 25
minute period during a regularly scheduled exercise time, where the
same activity levels are accumulated during each time interval for
a desired (e.g., healthy) level of patient activity. The reference
level may be customized for each patient's needs, schedule, and
habits. If the measured patient activity level exceeds the
reference activity levels during a time interval, then the
graphical representation displays a physical activity level greater
than the normalized value (e.g., above 1.0) for that time interval.
Similarly, if the measured patient activity levels is below the
reference activity level during a time interval, then the graphical
representation displays a physical activity level below the
normalized value (e.g., below 1.0) for the time interval.
[0102] In another embodiment, the graphical representation 105 is
not normalized, but displays both a reference value and a measured
value for each interval of time. When such a graphical
representation is reviewed by a health care provider, the
differences between the reference and measured physical activity
levels for a number of time intervals can be determined "at a
glance." In some examples, a graphical representation may represent
a graph of average daily activity versus time.
[0103] In some examples, the reference value may be determined
based on statistical data for typical patients with healthy
activity levels. In some cases, reference values for a particular
patient may be determined by monitoring physical activity levels
over time to determine average or typical ranges for that specific
patient. Those recorded activity levels are stored in a database.
In some examples, physical activity levels are categorized as
healthy or unhealthy (or graded by degree of health), by medical
personnel based on a review of the stored data, either alone or in
combination with one or more direct examinations (e.g., interviews,
surveys) of the patient.
[0104] In an illustrative example, a reference value for each time
interval may be developed by monitoring patient physical activity
levels for particular time interval (e.g., 9 AM to 11:30 AM) every
weekday morning. A reference value for an interval may be
determined as an average or weighted average of the monitored
values for that interval. In some applications, health care
providers specify daily or weekly healthy profile (or range of
healthy activity) for physical activity upon review of the
monitored values. The specified ranges or profiles may be stored in
conjunction with alarm conditions stored in a rules database.
[0105] In various embodiments, time intervals may be processed
and/or displayed to distinguish between weekdays, weekends,
holidays, vacations, travel periods, and other periods during which
a patient's activity level may vary substantially from a typical
routine. In some embodiments, the patient 115 may upload (e.g.,
export calendar data) for certain electronic calendar information
(e.g., scheduled appointments, events, trips) to the local
communication module 130 and/or the remote data processing facility
135, for example, where it is electronically stored in association
with other information about the patient 115. Alarm notification
criteria may be adjusted (e.g., reduced or increased sensitivity,
as directed by a physician), or alarm notifications manually
reviewed by an analyst or medical care provider if physical
activity levels shift outside of typical ranges during periods in
which non-routine activities are scheduled. Alarm notification
messages may include information about the schedule calendar
information, to give reviewing medical personnel access to more
complete information about the status of the patient 115. In some
further examples, the patient 115 may provide (e.g., by electronic
message, calendar software data export, html form fill-in, or the
like) schedule information about, for example, time and type of
exercise or other activity information that may be displayed as
annotated information associated with a corresponding time interval
on the graphical representation 105. For example, a patient may
indicate that they plan to walk a dog for 30 minutes every
afternoon at 2:00 PM, and then watch a movie on a particular
Thursdays at 7:00 PM. This information may be annotated to the
corresponding time intervals displayed on the graphical
representation 105, such that medical personnel have access to
improved information about patient actual activities during the
time intervals being reviewed. In some embodiments, the information
may be displayed in a legend (e.g., text below the graphical
representation 105), or in response to the reviewer operating the
GUI to place a cursor over the displayed time interval of interest,
or in response to user input such as a text request for information
about a particular time interval. In some examples, a user may
click (e.g., using a mouse, touch screen, light pen, or the like)
on the histogram bar of interest to cause display of the annotated
information about calendared patient activity.
[0106] In some embodiments, analog and/or digital filtering of the
received waveform is performed within the medical device 110 of
FIG. 1A. In some examples, a received waveform is used to modulate
a carrier signal capable of transmitting an analog version of the
received waveform to a receiver external to the patient 115.
Accordingly, some embodiments perform analog signal processing to
detect non-cardiac muscle activity events using hardware components
and/or digital signal processing modules that are, at least in
part, external to the patient. In some examples, analog signal
processing may be partially performed within the medical device 110
and/or within the subcutaneous electrode leads, and partially
performed by a processor module (not shown) in the local
communication module 130 external to the patient 115. As an
illustrative example, high frequency (e.g., noise) filtering and/or
AC-coupling may be implemented within the medical device 110, and
user-controllable tuning filters may be implemented in a local
processing module external to the patient 115.
[0107] Some embodiments use digital signal processing to detect
non-cardiac muscle activity events from the received ECG waveform.
For example, various digital signal processing operations, which
may include but are not limited to, filtering, rectification,
multiplication, and the like, may be performed upon digital samples
of the received ECG waveform. In various embodiments, analog to
digital conversion of the received ECG waveform may be performed in
the body of the patient 115 (e.g., in the medical device 110) or
external to the body of the patient 115 (e.g., in the local
communication module 130).
[0108] In some examples, digital signal processing to detect
non-cardiac muscle activity events in the received ECG waveform is
performed, at least in part, within a processor of the medical
device 110. Processed or unprocessed (e.g., raw ECG waveform
samples) may be transmitted via the wireless link to the local
communication module 130, where further digital signal processing
may be performed to detect non-cardiac muscle activity events. In
one illustrative example, a processor programmed to implement a
three-tap finite impulse response (FIR) low-pass filter within the
medical device 110 may substantially attenuate signal content above
a highest frequency range of interest for detecting muscle
activity.
[0109] In one example, a processor module to detect non-cardiac
muscle activity from an ECG waveform includes a low pass noise
filter that is implemented in either the analog or the digital
(e.g., FIR, IIR (infinite impulse response)) domain. The filter has
a cut-off frequency, which may be fixed or controllable, of about
50 Hz, 100 Hz, 250 Hz, 1 kHz, 2 kHz, or at least about 5 kHz, for
example.
[0110] Some systems may be implemented as a computer system that
can be used with a number of embodiments. For example, various
implementations may include digital and/or analog circuitry,
computer hardware, firmware, software, or combinations thereof.
Apparatus can be implemented in a computer program product tangibly
embodied in an information carrier, e.g., in a machine-readable
storage device or in a propagated signal, for execution by a
programmable processor; and methods can be performed by a
programmable processor executing a program of instructions to
perform functions by operating on input data and generating an
output. Some embodiments can be implemented advantageously in one
or more computer programs that are executable on a programmable
system including at least one programmable processor coupled to
receive data and instructions from, and to transmit data and
instructions to, a data storage system, at least one input device,
and/or at least one output device. A computer program is a set of
instructions that can be used, directly or indirectly, in a
computer to perform a certain activity or bring about a certain
result. A computer program can be written in any form of
programming language, including compiled or interpreted languages,
and it can be deployed in any form, including as a stand-alone
program or as a module, component, subroutine, or other unit
suitable for use in a computing environment.
[0111] Suitable processors for the execution of a program of
instructions include, by way of example, both general and special
purpose microprocessors, which may include a single processor or
one of multiple processors of any kind of computer. Generally, a
processor will receive instructions and data from a read-only
memory or a random access memory or both. The essential elements of
a computer are a processor for executing instructions and one or
more memories for storing instructions and data. Generally, a
computer will also include, or be operatively coupled to
communicate with, one or more mass storage devices for storing data
files; such devices include magnetic disks, such as internal hard
disks and removable disks; magneto-optical disks; and optical
disks. Storage devices suitable for tangibly embodying computer
program instructions and data include all forms of non-volatile
memory, including, by way of example, semiconductor memory devices,
such as EPROM, EEPROM, and flash memory devices; magnetic disks,
such as internal hard disks and removable disks; magneto-optical
disks; and, CD-ROM and DVD-ROM disks. The processor and the memory
can be supplemented by, or incorporated in, ASICs
(application-specific integrated circuits).
[0112] In some implementations, each system may be programmed with
the same or similar information and/or initialized with
substantially identical information stored in volatile and/or
non-volatile memory. For example, one data interface may be
configured to perform auto configuration, auto download, and/or
auto update functions when coupled to an appropriate host device,
such as a desktop computer or a server.
[0113] In some implementations, one or more user-interface features
may be custom configured to perform specific functions. Some
examples may be implemented in a computer system that includes a
graphical user interface and/or an Internet browser. To provide for
interaction with a user, some implementations may be implemented on
a computer having a display device, such as a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor for displaying
information to the user, a keyboard, and a pointing device, such as
a mouse or a trackball by which the user can provide input to the
computer.
[0114] In various implementations, the system 100 may communicate
using suitable communication methods, equipment, and techniques.
For example, the system 100 may communicate with compatible devices
(e.g., devices capable of transferring data within, to and/or from
the system 100) using point-to-point communication in which a
message is transported directly from the source to the receiver
over a dedicated physical link (e.g., fiber optic link,
point-to-point wiring, daisy-chain). The components of the system
may exchange information by any form or medium of analog or digital
data communication, including packet-based messages on a
communication network. Examples of communication networks include,
e.g., a LAN (local area network), a WAN (wide area network), MAN
(metropolitan area network), wireless and/or optical networks, and
the computers and networks forming the Internet. Other
implementations may transport messages by broadcasting to all or
substantially all devices that are coupled together by a
communication network, for example, by using omni-directional radio
frequency (RF) signals. Still other implementations may transport
messages characterized by high directivity, such as RF signals
transmitted using directional (i.e., narrow beam) antennas or
infrared signals that may optionally be used with focusing optics.
Still other implementations are possible using appropriate
interfaces and protocols such as, by way of example and not
intended to be limiting, USB 2.0, Firewire, ATA/IDE, RS-232,
RS-422, RS-485, 802.11a/b/g, Wi-Fi, Ethernet, IrDA, FDDI (fiber
distributed data interface), token-ring networks, or multiplexing
techniques based on frequency, time, or code division. Some
implementations may optionally incorporate features such as error
checking and correction (ECC) for data integrity, or security
measures, such as encryption (e.g., WEP) and password
protection.
[0115] A number of implementations have been described.
Nevertheless, it will be understood that various modifications may
be made. For example, advantageous results may be achieved if the
steps of the disclosed techniques were performed in a different
sequence, if components in the disclosed systems were combined in a
different manner, or if the components were replaced or
supplemented by other components. The functions and processes
(including algorithms) may be performed in hardware, software, or a
combination thereof, and some implementations may be performed on
modules or hardware not identical to those described. Accordingly,
other implementations are within the scope of the following
claims.
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