U.S. patent application number 12/903325 was filed with the patent office on 2012-04-19 for sequential discrimination approach for detecting treatable cardiac rhythms.
This patent application is currently assigned to Medtronic, Inc.. Invention is credited to Jeffrey M. Gillberg, Robert W. Stadler, Xusheng Zhang.
Application Number | 20120095520 12/903325 |
Document ID | / |
Family ID | 44860542 |
Filed Date | 2012-04-19 |
United States Patent
Application |
20120095520 |
Kind Code |
A1 |
Zhang; Xusheng ; et
al. |
April 19, 2012 |
SEQUENTIAL DISCRIMINATION APPROACH FOR DETECTING TREATABLE CARDIAC
RHYTHMS
Abstract
A system and method for use in a medical device for
discriminating cardiac events establishes population-based
thresholds corresponding to cardiac signal morphology metrics for
discriminating between a first cardiac event and a second cardiac
event. A population-based threshold criterion for discriminating
cardiac events is established. The population-based threshold
criterion is applied to a cardiac signal segment and the segment is
classified if the criterion is satisfied. A patient-specific
threshold is established in response to the sensed cardiac signal
segment not being classified after applying the population-based
threshold criterion. The sensed signal segment is classified in
response to the patient-specific threshold comparison.
Inventors: |
Zhang; Xusheng; (Shoreview,
MN) ; Stadler; Robert W.; (Shoreview, MN) ;
Gillberg; Jeffrey M.; (Coon Rapids, MN) |
Assignee: |
Medtronic, Inc.
|
Family ID: |
44860542 |
Appl. No.: |
12/903325 |
Filed: |
October 13, 2010 |
Current U.S.
Class: |
607/15 |
Current CPC
Class: |
G16H 50/70 20180101;
A61B 5/361 20210101; A61N 1/39622 20170801; A61N 1/3624 20130101;
A61B 5/363 20210101; A61B 5/686 20130101; A61B 5/7239 20130101;
A61B 5/7267 20130101; A61B 5/287 20210101 |
Class at
Publication: |
607/15 |
International
Class: |
A61N 1/36 20060101
A61N001/36 |
Claims
1. A method of classifying cardiac signals according to a cardiac
event type, comprising: establishing a plurality of
population-based thresholds corresponding to a plurality of cardiac
signal morphology metrics for discriminating between a first
cardiac event and a second cardiac event; establishing a first
threshold criterion for discriminating cardiac events, the first
threshold criterion comprising a comparison between a cardiac
signal morphology metric and one established population-based
threshold of the established plurality of population-based
thresholds; sensing a cardiac signal segment in a patient; applying
the first threshold criterion to the sensed cardiac signal segment;
classifying the cardiac signal segment only in response to the
applying the first threshold criterion being satisfied;
establishing a patient-specific threshold in response to the sensed
cardiac signal segment not being classified after applying the
first threshold criterion; computing a metric of the sensed cardiac
signal segment and comparing the metric to the established
patient-specific threshold; and classifying the sensed signal
segment in response to the comparing of the metric to the
established patient-specific threshold.
2. The method of claim 1, further comprising: establishing a
sequence of population-based threshold criteria; advancing to a
next population based threshold criterion in the sequence in
response to the first threshold criterion not being satisfied;
classifying the sensed cardiac signal segment in response to the
earliest criterion in the sequence being satisfied; and cancelling
a remaining population-based threshold criterion in the sequence in
response to classifying the cardiac signal segment.
3. The method of claim 1, wherein establishing the plurality of
population-based thresholds comprises: sensing a cardiac signal for
a plurality of time segments in a population of patients;
determining the plurality of morphology metrics for each of the
plurality of time segments; classifying each of the plurality of
time segments according to a cardiac event; plotting the plurality
of morphology metrics for the classified time segments; and
defining a population-based threshold that substantially separates
a first cluster of classified cardiac signal time segments from a
second cluster of classified cardiac signal time segments.
4. The method of claim 1, wherein the plurality of cardiac signal
morphology metrics comprises at least one of a low slope content, a
normalized mean rectified amplitude, a spectral width, a signal
overall variability, and an RR interval variability.
5. The method of claim 3, wherein establishing a plurality of
population-based thresholds comprises: plotting a multi-dimensional
scatter plot of at least two of the plurality of morphology metrics
for the plurality of classified cardiac signal time segments; and
defining a population-based threshold for one of the plurality of
morphology metrics as a function of another of the plurality of
morphology metrics based on the multi-dimensional plot, the
threshold defined to separate the first cluster of classified
cardiac signal time segments from the second cluster of classified
cardiac signal time segments.
6. The method of claim 5, wherein the second cluster of classified
cardiac signal segments overlaps a third cluster of classified
cardiac signal segments, the established population-based threshold
defining a one-way classification criterion for classifying only
the first cluster of cardiac signal segments in response to the
population-based threshold.
7. The method of claim 2, wherein establishing a sequence of
population-based threshold criteria comprises determining the first
threshold criterion as one of the population-based threshold
criteria that results in a highest frequency of cardiac signal
segment classifications.
8. The method of claim 7, wherein the highest frequency of cardiac
signal segment classifications comprises a highest frequency of a
treatable cardiac signal segment classification.
9. The method of claim 1, wherein establishing a patient-specific
threshold comprises computing a characteristic of an
earlier-occurring cardiac signal segment.
10. The method of claim 1, wherein establishing a patient-specific
threshold comprises computing a characteristic of a currently
occurring cardiac signal segment.
11. The method of claim 1, wherein establishing a patient-specific
threshold comprises: establishing a classification threshold for
detecting a treatable rhythm onset; classifying the sensed cardiac
signal segment as treatable in response to the established
classification threshold for detecting a treatable rhythm onset
being detected; and classifying the sensed cardiac signal segment
as non-treatable in response to the established classification
threshold for detecting a treatable rhythm onset not being
detected.
12. The method of claim 1, wherein establishing a plurality of
population-based thresholds comprises determining a correlation
between a first one of the plurality of morphology metrics
determined for a time segment and a second one of the plurality of
morphology metrics determined for the same time segment.
13. A medical device system for classifying cardiac signals
according to a cardiac event type, comprising: a processor
configured to establish a plurality of population-based thresholds
corresponding to a plurality of cardiac signal morphology metrics
for discriminating between a first cardiac event and a second
cardiac event, and establish a first threshold criterion for
discriminating cardiac events, the first threshold criterion
comprising a comparison between a cardiac signal morphology metric
and one established population-based threshold of the plurality of
population-based thresholds; a plurality of electrodes for sensing
a cardiac signal segment; a programmable memory storing the first
population-based threshold criterion; and a controller configured
to: apply the first threshold criterion to the sensed cardiac
signal segment; classify the sensed cardiac signal segment only in
response to the applying the first threshold criterion being
satisfied; establish a patient-specific threshold in response to
the sensed cardiac signal segment not being classified after
applying the first threshold criterion; compute a metric of the
sensed cardiac signal segment and comparing the metric to the
established patient-specific threshold; and classify the sensed
cardiac signal segment in response to the comparing of the metric
to the established patient-specific threshold.
14. The system of claim 13, wherein the processor is further
configured to establish a sequence of population-based threshold
criteria; and the controller is configured to advance to a next
population based threshold criterion in the sequence in response to
the first threshold criterion not being satisfied, classify the
sensed cardiac signal segment in response to the earliest criterion
in the sequence being satisfied, and cancel a remaining
population-based threshold criterion in the sequence in response to
classifying the cardiac signal segment.
15. The system of claim 13, wherein establishing the plurality of
population-based thresholds comprises: sensing a cardiac signal for
a plurality of time segments in a population of patients;
determining the plurality of morphology metrics for each of the
plurality of time segments; classifying each of the plurality of
time segments according to a cardiac event; plotting the plurality
of morphology metrics for the classified time segments; and
defining a population-based threshold that substantially separates
a first cluster of classified cardiac signal time segments from a
second cluster of classified cardiac signal time segments.
16. The system of claim 13, wherein the plurality of cardiac signal
morphology metrics comprises at least one of a low slope content, a
normalized mean rectified amplitude, a spectral width, a signal
overall variability, and an RR interval variability.
17. The system of claim 15, wherein establishing a plurality of
population-based thresholds comprises plotting a multi-dimensional
scatter plot of at least two of the plurality of morphology metrics
for the plurality of classified cardiac signal time segments; and
defining a population-based threshold for one of plurality of
morphology metrics as a function of another of the plurality of
morphology metrics based on the multi-dimensional plot, the
threshold defined to separate the first cluster of classified
cardiac signal time segments from the second cluster of classified
cardiac signal time segments.
18. The system of claim 17, wherein the second cluster of
classified cardiac signal segments overlaps a third cluster of
classified cardiac signal segments, the established
population-based threshold defining a one-way classification
criterion for classifying only the first cluster of cardiac signal
segments in response to the population-based threshold.
19. The system of claim 14, wherein establishing a sequence of
population-based threshold criteria comprises determining a first
one of the population-based threshold criteria that results in a
highest frequency of cardiac signal segment classifications.
20. The system of claim 19, wherein the highest frequency of
cardiac signal segment classifications comprises a highest
frequency of a treatable cardiac signal segment classification.
21. The system of claim 13, wherein establishing a patient-specific
threshold comprises computing a characteristic of an
earlier-occurring cardiac signal segment.
22. The system of claim 13, wherein establishing a patient-specific
threshold comprises establishing a classification threshold for
detecting a treatable rhythm onset, and wherein the controller is
further configured to classify the sensed cardiac signal segment as
treatable in response to the established classification threshold
for detecting a treatable rhythm onset being detected, and classify
the sensed cardiac signal segment as non-treatable in response to
the established classification threshold for detecting a treatable
rhythm onset not being detected.
23. The system of claim 13, wherein establishing a plurality of
population-based thresholds comprises determining a correlation
between a first one of the plurality of morphology metrics
determined for a time segment and a second one of the plurality of
morphology metrics determined for the same time segment.
24. The system of claim 13, wherein establishing a patient-specific
threshold comprises computing a characteristic of a currently
occurring cardiac signal segment.
25. A computer-readable medium storing a set of instructions which
cause a processor of a medical device system to: establish a
plurality of population-based thresholds corresponding to a
plurality of cardiac signal morphology metrics for discriminating
between a first cardiac event and a second cardiac event; establish
a first threshold criterion for discriminating cardiac events, the
first threshold criterion comprising a comparison between a cardiac
signal morphology metric and one established population-based
threshold of the established plurality of population-based
thresholds; sense a cardiac signal segment; apply the first
threshold criterion to the sensed cardiac signal segment; classify
the sensed cardiac signal segment only in response to the applying
the first threshold criterion being satisfied; establish a
patient-specific threshold in response to the sensed cardiac signal
segment not being classified after applying the first threshold
criterion; compute a metric of the sensed cardiac signal segment
and comparing the metric to the established patient-specific
threshold; and classify the sensed signal segment in response to
the comparing of the metric to the established patient-specific
threshold.
Description
TECHNICAL FIELD
[0001] The disclosure relates generally to implantable medical
devices and, in particular, to a method and apparatus for
discriminating treatable and non-treatable cardiac rhythms.
BACKGROUND
[0002] A typical implantable cardioverter defibrillator (ICD) has
the capability of providing a variety of anti-tachycardia pacing
(ATP) regimens as well as cardioversion/defibrillation shock
therapy. Normally, arrhythmia therapies are applied according to a
pre-programmed sequence of less aggressive to more aggressive
therapies depending on the type of arrhythmia detected. Typically,
termination of an arrhythmia is confirmed by a return to either a
demand-paced rhythm or a sinus rhythm in which successive
spontaneous R-waves are separated by at least a defined interval.
When ATP attempts fail to terminate the tachycardia, high-voltage
cardioversion shocks may be delivered. Since shocks can be painful
to the patient and consume relatively greater battery energy than
pacing pulses, it is desirable to avoid the need to deliver shocks
by successfully terminating the tachycardia using less aggressive
pacing therapies. On the other hand, if a tachycardia is a lethal
arrhythmia that is likely to require a shock therapy for successful
termination, it is desirable to deliver the shock therapy as
quickly as possible without delay.
[0003] The success of a tachycardia therapy depends in part on the
accuracy of the tachycardia detection. In some cases, a tachycardia
originating in the atria, i.e. a supraventricular tachycardia
(SVT), is difficult to distinguish from a tachycardia originating
in the ventricles, i.e. a ventricular tachycardia (VT). For
example, both the atrial chambers and the ventricular chambers may
exhibit a similar tachycardia cycle length when an SVT is conducted
to the ventricles or when a VT is conducted retrograde to the
atria.
[0004] Accordingly, accurate classification of a detected
tachycardia as VT or SVT is needed in order to properly determine
when and what type of therapy is necessary. As more complex
algorithms become available for accurately detecting and
discriminating cardiac rhythms with a high sensitivity and high
specificity, the processing time and burden on the ICD for
performing these algorithms increases. These relatively more
complex algorithms may be needed when the rhythm type is difficult
to discern. At times, however, more complex algorithms may pose
undue processing burden. What is needed, therefore, is a method and
apparatus for discriminating SVT and VT with high sensitivity and
specificity while limiting the signal processing burden.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a schematic representation of an implantable
medical device (IMD).
[0006] FIG. 2 is a functional block diagram of electronic circuitry
that is included in one embodiment of an IMD for practicing the
methods described herein.
[0007] FIG. 3 is a flow chart of a method for classifying a cardiac
signal for use in detecting cardiac events.
[0008] FIGS. 4A through 4C are histogram plots of the frequency of
a morphology metric for ventricular fibrillation (VF) (FIG. 4A), VT
(FIG. 4B) and SVT (FIG. 4C).
[0009] FIG. 5A is a two-dimensional plot of low slope content (LSC)
plotted as a function of spectral width (SW) computed from cardiac
signals obtained from a population of patients.
[0010] FIG. 5B is a two-dimensional plot of normalized mean
rectified amplitude (NMRA) as a function of SW.
[0011] FIG. 6 is a flow chart of one embodiment of sequential
comparisons made using population-based thresholds in an algorithm
for detecting a cardiac event.
[0012] FIG. 7 is a flow chart of a method for establishing a
sequence of population-based threshold comparisons for use in
cardiac rhythm discrimination.
[0013] FIG. 8 is a flow chart of one method for detecting a cardiac
event using a sequential discrimination of cardiac signal
segments.
DETAILED DESCRIPTION
[0014] In the following description, references are made to
illustrative embodiments. It is understood that other embodiments
may be utilized without departing from the scope of the disclosure.
In some instances, for purposes of clarity, identical reference
numbers may be used in the drawings to identify similar
elements.
[0015] FIG. 1 is a schematic representation of an implantable
medical device (IMD) 10. IMD 10 is embodied as an ICD in FIG. 1.
Methods described herein, however, should not be interpreted as
being limited to any particular implantable medical device or any
particular cardiac medical device. Instead, embodiments may include
any cardiac medical device so long as the device utilizes a
plurality of electrodes or other sensors for monitoring the cardiac
rhythm of a patient.
[0016] In FIG. 1, the right atrium (RA), left atrium (LA), right
ventricle (RV), left ventricle (LV), and the coronary sinus (CS),
extending from the opening in the right atrium to form the great
cardiac vein, are shown schematically in heart 12. Two transvenous
leads 16 and 18 connect IMD 10 with the RV and the LV,
respectively. Each lead includes at least one electrical conductor
and pace/sense electrode. The electrodes are capable of sensing
cardiac EGM signals, also referred to herein generally as "cardiac
signals", and delivering electrical pacing pulses to the cardiac
tissue. For example, leads 16 and 18 are connected to pace/sense
electrodes 20, 22, and 24, 28, respectively. In addition, a housing
electrode 26 can be formed as part of the outer surface of the
housing of the device 10. The pace/sense electrodes 20, 22, and 24,
28 and housing electrode 26 can be selectively employed to provide
a number of unipolar and bipolar pace/sense electrode combinations
for pacing and sensing functions. The depicted positions in or
about the right and left heart chambers are merely illustrative.
Moreover, other leads and pace/sense electrodes can be used instead
of, or in combination with, any one or more of the depicted leads
and electrodes.
[0017] Typically, in pacing systems of the type illustrated in FIG.
1, the electrodes designated herein as "pace/sense" electrodes are
used for both pacing and sensing functions. In certain embodiments,
these electrodes can be used exclusively as pace or sense
electrodes in programmed or default combinations for sensing
cardiac signals and delivering pace pulses. The leads and
electrodes described can be employed to record cardiac signals. The
recorded data can be periodically transmitted to a programmer or
other external device enabled for telemetric communication with the
IMD 10.
[0018] An RV coil electrode 34 and a superior vena cava (SVC) coil
electrode 32 are also shown as being coupled to a portion of RV
lead 16. Coil electrodes can additionally or alternatively be
coupled to portions of CS lead 18. The coil electrodes 32 and 34,
or other similar electrode types, can be electrically coupled to
high voltage circuitry for delivering high voltage
cardioversion/defibrillation shock pulses.
[0019] Electrodes shown in FIG. 1 can be disposed in a variety of
locations in, around, and on the heart and are not limited to the
locations shown. ICDs and pacemakers typically use a ventricular
EGM signal for sensing ventricular events (R-waves) for determining
a need for pacing and for detecting a RR intervals meeting
tachycardia detection criteria. An EGM sensing vector may be a
unipolar or bipolar sensing vector using one or two electrodes,
respectively, placed in or on the heart chambers.
[0020] Embodiments described herein are not limited to use with
intracardiac or transvenous leads as shown in FIG. 1.
Subcutaneously implanted electrodes or even external electrode
systems may be used. As used herein, the term "cardiac signal"
refers generally to any cardiac electrical signal sensed using any
electrodes, including an EGM signal or an ECG signal. Reference is
made to U.S. Patent Publication No. 2007/0232945 (Kleckner) for a
description of a subcutaneous ICD in which cardiac event
discrimination methods described herein may be implemented. The
'945 publication is incorporated herein by reference in its
entirety
[0021] Furthermore, other transvenous lead and electrode systems
may be substituted for the system shown in FIG. 1. A detection
algorithm may or may not use electrodes for sensing atrial signals
for detecting and discriminating treatable rhythms. IMD 10 is shown
coupled only to ventricular leads 16 and 18 but implementation of a
selected detection algorithm is not limited to systems employing
only ventricular leads. Additional electrodes may be positioned for
sensing atrial event (P-waves) and determining PP intervals, PR
intervals and/or RP intervals. In other embodiments, dual chamber
or multi-chamber systems may be used which include atrial leads
used to position electrodes in, on or around the atrial chambers.
Systems that employ atrial leads without the use of ventricular
leads may also be used depending on the algorithm implemented for
detecting arrhythmia episodes and according to patient need.
[0022] FIG. 2 is a functional block diagram 100 of electronic
circuitry that is included in one embodiment of an IMD for
practicing the methods described herein. IMD 100 includes cardiac
signal input 102, processing and control 104, therapy module 106,
output 108 and telemetry module 140. As used herein, the term
"module" refers to an application specific integrated circuit
(ASIC), an electronic circuit, a processor (shared, dedicated, or
group) and memory that execute one or more software or firmware
programs, a combinational logic circuit, or other suitable
components that provide the described functionality.
[0023] Input 102 includes at least one cardiac signal sensed to
provide input to processing and control 104 for detecting cardiac
events. Input 102 may include one or more EGM and/or ECG sensing
electrode vectors for obtaining cardiac signals. Other
physiological sensors, such as pressure, flow, oxygen, or motion
sensors, may be included in alternative embodiments for providing
additional signals used to detect arrhythmias or monitoring other
patient conditions. A sequential discrimination algorithm is
described herein which relies exclusively on EGM and/or ECG
signals, collectively referred to herein as "cardiac signals",
however it is contemplated that other signals containing cardiac
information may be substituted for an EGM or ECG signal or used in
combination therewith.
[0024] Processing and control module 104, also referred to herein
as a "controller", includes a buffer 115 which stores an n-second
segment of a cardiac signal received from cardiac signal input 102.
An R-wave detector 118 receives the cardiac signal input 102 for
sensing R-waves and determining RR intervals. A morphology waveform
analysis module 120 receives input from the n-second buffer to
compute various morphology metrics from the buffered cardiac signal
segment. As indicated above, practice of the methods described
herein is not limited to a single ventricular cardiac signal but
may be applied to multiple atrial and/or ventricular cardiac
signals. For the sake of illustration, in the description that
follows a single ventricular cardiac signal is used for cardiac
event detection.
[0025] Segment classification module 126 classifies each cardiac
signal segment as "treatable" or "non-treatable" based on R-wave
intervals received from R-wave detector 118 and/or morphology
analyzer 120. Segment classification module 126 utilizes a
sequential discrimination method including a hierarchal sequence of
classification criteria as will be described in detail below. In
general, the sequence includes a set of comparisons beginning from
less computationally intensive to more computationally intensive
comparisons which allow discrimination of cardiac rhythms with the
highest possible sensitivity and specificity and least processing
power required. Additionally or alternatively, the sequence is
ordered according to a highest probability or frequency of
classifying a cardiac signal segment in response to applying the
criterion for a given cardiac event type.
[0026] A "treatable" rhythm, as used herein, refers to any
tachycardia that is ventricular in origin and can potentially be
treated by delivering a therapy in the ventricles for terminating
the ventricular tachycardia. A "non-treatable" rhythm is any rhythm
with a relatively slow ventricular rate (below a ventricular
tachycardia rate) and any tachycardia that is supraventricular in
origin. Delivering a therapy only in the ventricular chambers
frequently does not resolve a supraventricular tachycardia.
[0027] It is recognized, however, that depending on the particular
application, the designations of "treatable" and "non-treatable"
rhythms may be defined differently. For example, a device that is
only programmed to deliver shock therapies may define treatable
rhythms as those that require a cardioversion/defibrillation shock
and non-treatable rhythms as those that are not treated with a
shock. In cardiac devices capable of delivering atrial therapies,
treatable rhythms may include some atrial arrhythmias.
[0028] In general, possible cardiac signal segment classifications
may include SVT, normal sinus rhythm (NSR), sinus tachycardia, slow
VT, fast VT, VF, or any subset or combination thereof, and these
segment classifications can lead to a cardiac event detection,
which may or may not result in therapy delivery depending on the
therapy delivery capabilities of the particular device and
programmed therapy regimens. In some embodiments, some of these
classifications may be grouped into non-treatable and treatable
classifications. For example, any classification of SVT or sinus
tachycardia would be non-treatable and any classification of fast
VT or VF would be treatable when the sequential discrimination
algorithm is used to identify shockable cardiac events. A
discrimination algorithm will provide discrimination between the
treatable and non-treatable rhythms but may not provide further
discrimination between the different treatable rhythm types and the
non-treatable rhythm types, particularly when the decision to treat
is a decision to shock or not shock.
[0029] An event detector 128 detects a treatable cardiac event when
a required number of cardiac signal segments are classified as
treatable. The therapy control module 130 responds to the detection
of a cardiac event by controlling high voltage (HV) shock pulse
generator 132 to deliver a cardioversion/defibrillation shock using
high voltage electrodes 136 and/or by controlling pacing pulse
generator 134 used to deliver pacing pulses using low voltage (LV)
electrodes 138 as needed, e.g., for anti-tachycardia pacing, during
a programmed menu of therapies including pacing and shock delivery,
or during post-shock recovery. It is recognized that in some
embodiments, any of the HV electrodes 136 and LV electrodes 138 in
output 108 may also be used as sensing electrodes in input 102.
[0030] IMD 100 includes telemetry circuit 140 capable of
bidirectional communication with an external device 144 such as a
programmer or home monitor. Telemetry circuit 140 is used to
transmit cardiac event data to an external programmer Cardiac
signal data obtained by R-wave detector 118, morphology analyzer
120, and/or segment classification 126 may also be collected and
transmitted to an external device for review and analysis. Uplink
telemetry allows device status and diagnostic/event data to be sent
to an external programmer or home monitor 144 for review by the
patient's physician. Downlink telemetry allows the external device
144, via physician control, to enable programming of IMD function
and the optimization of detection and therapy processes for a
specific patient.
[0031] External device 144 may be embodied as an external processor
used to collect cardiac signal data from a patient population and
process the data to establish population-based threshold criterion
for classifying cardiac signal segments. Methods for establishing
population-based threshold criteria will be described in greater
detail below.
[0032] FIG. 3 is a flow chart 200 of a method for classifying a
cardiac signal for use in detecting cardiac events. Flow chart 200
and other flow charts presented herein are intended to illustrate
the functional operation of the device, and should not be construed
as reflective of a specific form of software or hardware necessary
to practice the methods described. It is believed that the
particular form of software, firmware and/or hardware will be
determined primarily by the particular system architecture employed
in the device and by the particular detection and therapy delivery
methodologies employed by the device. Providing software, firmware
and/or hardware to accomplish the described functionality in the
context of any modern medical device, given the disclosure herein,
is within the abilities of one of skill in the art.
[0033] Methods described in conjunction with flow charts presented
herein may be implemented in a computer-readable medium storing
instructions for causing a programmable processor to carry out the
methods described. A "computer-readable medium" includes but is not
limited to any volatile or non-volatile media, such as a RAM, ROM,
CD-ROM, NVRAM, EPROM, EEPROM, flash memory, and the like. The
instructions may be implemented as one or more software modules,
which may be executed by themselves or in combination with other
software.
[0034] At block 202, population-based thresholds for cardiac signal
characteristics are stored in the IMD for discriminating treatable
and non-treatable rhythms. The thresholds are determined from
clinical data acquired from a population of patients. Examples of
determinations of population-based thresholds applied to cardiac
signal characteristics will be described below in conjunction with
FIGS. 4A-C, FIGS. 5A-B and FIG. 7.
[0035] At block 204, a cardiac signal is sensed and an n-second
segment is buffered. In one embodiment, a 3-second signal is
buffered from which morphology metrics are computed at block 206.
The morphology metrics computed at block 206 are selected as the
cardiac signal characteristics that provide the greatest confidence
in separating treatable from non-treatable (or non-treatable from
treatable) cardiac rhythms.
[0036] At block 208, a sequence of comparisons for classifying the
cardiac signal segment is initiated using the computed morphology
metrics and the stored population-based thresholds. While the
blocks shown in FIG. 3 and in other flow charts presented herein
are shown in a particular order, it is recognized that operations
described may be performed in a different order than that shown and
still reach a similar result. For example, the morphology metrics
computed at block 206 may all be computed in advance of performing
a sequence of comparisons at block 210 or may be computed as needed
as the sequence of comparisons advances from one comparison to
another.
[0037] If a threshold criterion is satisfied at decision block 212,
the cardiac signal segment is classified accordingly at block 214.
A classification threshold criterion requires a signal
characteristic to either be greater than or less than the threshold
with a resulting classification occurring only when the requirement
is met. If the requirement is not met, no classification is made.
The classification for a given threshold comparison may be related
to either treatable or non-treatable segments but not both. For
example, if the threshold requirement relates to classifying a
treatable segment, a non-treatable segment classification will
never be made in response to performing that particular threshold
criterion. If the threshold criterion is satisfied, a
classification of treatable is made. If the threshold requirement
is not met, no classification is made at all. Other classification
criterion may define a requirement for classifying a segment as a
non-treatable segment. The result of applying the classification
criterion will be either a non-treatable segment classification or
no classification at all and will never result in a treatable
classification. The population-based threshold comparisons may be
described as "one-way" classification criteria because if a given
criterion is not satisfied, no classification is made at all rather
than giving a different classification. The cardiac signal segment
is classified in response to applying a population-based threshold
criterion only when the criterion is satisfied and otherwise
remains unclassified. A population-based criterion may also be
described as an IF/THEN operation. If the threshold criterion
defines a threshold for classifying a treatable rhythm, the
operation would be:
[0038] IF threshold met, THEN treatable.
[0039] If the threshold criterion defines a threshold for
classifying a non-treatable rhythm, the operation would be:
[0040] IF threshold met, THEN non-treatable.
[0041] In either case, if the threshold is not met, no
classification is made.
[0042] The sequence of comparisons initiated at block 208 begins
with a first comparison that provides a relatively high confidence
in rhythm separation, a relatively low computational burden, and a
high frequency of classified segments after the first comparison.
As used herein, a high degree of confidence may correspond to a
confidence that approximately 80%, 90%, 95% or other acceptable
percentage of all segments classified based on a population-based
threshold comparison are classified correctly.
[0043] If the first classification criterion is satisfied and
results in classifying the cardiac signal segment, the segment
classification is made immediately at block 214 without applying
additional classification criteria. When a classification threshold
criterion is met, the remaining comparisons in the initiated
sequence of population-based threshold criteria are cancelled at
block 216.
[0044] After classifying the segment, cardiac event detection
criteria are applied at block 224. Typically, in order to detect a
treatable cardiac event, more than one cardiac signal segment out
of a given number of the most recent segments must be classified as
treatable. For example, if five out of eight of the most recent
n-second segments are classified as treatable, a treatable episode
is detected at block 224.
[0045] Data relating to the detected cardiac event is stored at
block 226. Depending on the cardiac event being detected and the
therapy delivery capabilities of the device, a therapy may be
delivered in response to the detected event. If the cardiac event
detection threshold is not met at block 224, the process returns to
block 204 to evaluate the next cardiac signal segment.
[0046] If a classification threshold comparison does not result in
a classification threshold being met at block 212, the process
advances to block 218 to determine if all population-based
threshold comparisons in the sequence have been performed. If not,
the process returns to block 210 to perform the next comparison in
the sequence.
[0047] If all of the population-based threshold comparisons have
been performed in the sequence of comparisons (affirmative result
at block 218), the process advances to block 219 to compute a
patient-specific threshold using the current n-second cardiac
signal segment and/or previously stored cardiac signal segments.
Computation of a patient-specific threshold and performing a
patient-specific threshold comparison in real-time may require
greater processing burden than a comparison using a previously
stored population-based threshold. As such, the less
computationally-intensive population-based threshold comparisons
are made first to determine if the segment can be classified with a
relatively high degree of confidence based on empirically-derived
thresholds. If a segment remains unclassified after completing the
sequence of population-based threshold comparisons, the
discrimination algorithm advances to a patient-specific threshold
comparison.
[0048] After computing the patient-specific threshold at block 219,
a patient-specific threshold comparison is performed at block 220.
While not explicitly shown, it is to be understood that this
comparison at block 220 may require one or more new cardiac signal
characteristics or morphology metrics to be computed if not already
computed at block 206 for the current cardiac signal segment.
[0049] Based on this patient-specific comparison, the current
n-second segment is classified at block 222. The patient-specific
threshold comparison will always result in a segment
classification, and may be considered a "two-way" classification.
If the patient-specific threshold criterion is not satisfied to
classify the segment as a first cardiac event, the segment will
still be classified as a second cardiac event. This is in contrast
to the population-based threshold comparison that requires a
threshold criterion to be met in order to make a classification; if
the threshold criterion is not met, no classification is made at
all. The patient-specific comparison may be thought of as an
IF/THEN/ELSE operation wherein, if the threshold is defined to
classify a treatable rhythm, the operation would be:
[0050] IF threshold met, THEN treatable, ELSE non-treatable.
[0051] The segment classification resulting from the
patient-specific classification is used at block 224 to determine
if the cardiac event detection threshold is met (e.g. required
number of segments classified as treatable). If not the process
returns to block 204. If a cardiac event is detected, the process
advances to block 226 to store the cardiac event episode data and
deliver an appropriate therapy as needed.
[0052] FIGS. 4A through 4C are histogram plots of a morphology
metric for cardiac signal segments corresponding to VF (FIG. 4A),
fast VT (FIG. 4B) and SVT (FIG. 4C). In this particular example,
the morphology metric is a low slope content (LSC) computed from
3-second cardiac signal segments obtained from a population of
patients. The LSC of a non-treatable tachycardia is typically high
relative to the LSC of a shockable tachycardia. As such, the LSC is
a useful morphology metric for discriminating between treatable and
non-treatable rhythms.
[0053] The LSC may be computed according to methods generally
described in U.S. Pat. Publication No. 2008/0269624 (Zhang), hereby
incorporated herein by reference in its entirety. Briefly, the LSC
is computed from the first derivative of the n-second cardiac
signal segment. The number of first derivative signal points having
a low value, e.g. an absolute value less than a low slope
threshold, is counted. The LSC is the ratio of this number of low
slope signal points to the total number of first derivative signal
points during the n-second segment. The low slope threshold used to
compute the number of low slope signal points below the threshold
can be determined from the first derivative signal. For example,
the low slope threshold may be defined as a percentage, e.g., 10%,
of the maximum peak of the first derivative signal.
[0054] Comparison of the LSC histograms of FIGS. 4A (VF) and 4B
(fast VT), show that the there is a clustering of the LSC values
below approximately 0.6 during VF segments and a clustering of the
LSC values above approximately 0.55 for fast VT segments. As such,
a LSC threshold between approximately 0.5 and approximately 0.6
provides some degree of separation between VF and fast VT, however
some overlap of the LSC values between these two rhythms exists.
Separation of these two rhythms, however may not be of interest in
some embodiments since both rhythms may be considered treatable (or
shockable) rhythms.
[0055] The histogram in FIG. 4C of LSC values during SVT presents a
clustering of LSC values above approximately 0.7. As such, a
threshold between approximately 0.6 and approximately 0.7 would
provide separation of VF from SVT with relatively high confidence.
A threshold of approximately 0.75 would provide separation of fast
VT from SVT with relatively high confidence. In both cases,
however, a considerable number of values overlap between the
different rhythms.
[0056] In order to further improve the confidence of a
population-based threshold criterion, two or more signal
characteristics may be plotted in a multi-dimensional plot to
identify clusters of signal characteristic values that can be
separated by a threshold defined as a continuous or discontinuous,
linear or non-linear function of the plotted signal
characteristic(s).
[0057] FIG. 5A is a two-dimensional plot 410 of LSC plotted as a
function of spectral width (SW) for cardiac signal segments during
different cardiac rhythms, obtained from a population of patients.
SW is an approximation of the signal bandwidth. SW may be defined
as the fundamental period (i.e., the inverse of the fundamental
frequency or heart rate (HR)) minus the mean period (the inverse of
the mean frequency). Mean frequency (MF) is calculated as the ratio
of the mean absolute amplitude of the first derivative of the
n-second segment to the mean absolute amplitude of the n-second
segment, and the ratio is roughly proportional to the frequency of
the dominant sinusoidal component in the 3-second segment.
[0058] A two-dimensional, linear threshold 412 (or a non-linear
threshold) may be defined which separates the clustered points. In
this example, when a threshold for LSC is defined as a linear
function of SW, any LSC value falling below the threshold 412 is
associated with a VF or fast VT rhythm. Virtually all points below
threshold 412 correspond to treatable rhythms. As such, a
corresponding cardiac signal segment may be classified as a
"treatable" segment for use in cardiac event detection using
threshold 412 in defining a classification criterion.
[0059] If the LSC falls above the threshold 412, there is less
certainty of the rhythm type since there is considerable overlap
between the values for fast VT points (treatable) and SVT points
(non-treatable). The illustrative threshold 412 is therefore used
to define a one-way classification criterion requiring the LSC to
be less than the threshold 412. If the LSC and the SW for a cardiac
signal segment results in a point less than threshold 412, the
signal segment can be classified as "treatable" with a high degree
of certainty since very few SVT points fall below the threshold
412.
[0060] If the LSC and the SW result in a sample point greater than
the threshold 412, the threshold comparison does not meet the
one-way classification criterion. Because there is considerable
overlap between fast VT points and SVT points above the threshold
412, this result is inconclusive for segment classification. No
segment classification would be made, and the discrimination
algorithm would advance to the next threshold comparison in a
sequence of one-way population-based threshold criteria.
[0061] FIG. 6 is a flow chart 500 of one embodiment of sequential
comparisons using population-based thresholds in an algorithm for
detecting a cardiac event. At block 502, the population-based
classification criteria are stored. The criteria are derived
empirically from historical clinical data corresponding to all
rhythm classifications obtained from a population of patients. The
population may be as few as one patient but is typically a larger
number of patients.
[0062] As will be described below, a population-based threshold
criterion is stored for each classification comparison in a
sequence of comparisons. Each threshold criterion is defined as a
"one-way" criterion as described above.
[0063] At block 504, a cardiac signal segment is acquired and
computation of morphology metrics begins at block 506. As indicated
previously, all metrics or signal characteristics needed for
performing all threshold comparisons in a sequence of comparisons
may be computed in advance or computed only if needed as the
algorithm advances through the sequence.
[0064] In the illustrative embodiment, a sequence of
population-based threshold comparisons, which require relatively
low processing power and time, is performed at blocks 508 through
518. If a classification criterion is satisfied, the segment is
classified at block 522 or 520 without advancing through any
remaining comparisons of blocks 510 through 518.
[0065] In the flow chart 500, specific examples of one-way
threshold comparisons of signal characteristics are listed. These
specific examples are intended to be illustrative and not limiting.
In various embodiments, different signal characteristics and
combinations of signal characteristics could be selected for use.
Furthermore, a given classification criterion may include one or
more threshold comparisons.
[0066] As used herein, a one-dimensional threshold comparison
refers to the comparison between a single signal characteristic and
a population-based threshold defined as a single fixed value. A
one-dimensional threshold criterion is defined independent of any
other signal characteristics.
[0067] A two-dimensional threshold comparison refers to the
comparison between a signal characteristic and a threshold that is
depending on a second signal characteristic, different than the
first signal characteristic, computed for the same time segment.
The threshold may be defined as a function of the second signal
characteristic computed for the same time segment. A
two-dimensional threshold can be defined as a linear or non-linear
function of the second signal characteristic such as the
two-dimensional, linear threshold 412 shown in FIG. 5A.
Alternatively, a threshold may be defined for the first signal
characteristic that is constrained by a threshold requirement
placed on the second characteristic. This type of two-dimensional
threshold comparison will be described below in conjunction with
FIG. 5B.
[0068] A higher order multi-dimensional comparison could also be
defined in which a first signal characteristic is compared to a
threshold defined as a function of two or more different signal
characteristics, which may be a polynomial or higher order
function, computed during the same cardiac signal segment.
Alternatively, a first signal characteristic may be compared to a
fixed value threshold with constraints placed on two or more other
signal characteristics as well in order for the classification
criterion to be satisfied.
[0069] At block 508, a two-dimensional threshold comparison is made
based on the example graph shown in FIG. 5A. The LSC computed for
the current n-second signal segment is compared to a threshold
defined as a linear function of SW computed for the same n-second
segment. In the equation in block 508, the constant A and the
coefficient B are determined empirically from the plotted patient
population data for providing separation of treatable and
non-treatable rhythms with high confidence.
[0070] The comparison made at block 508 is a one-way criterion for
classifying treatable cardiac signal segments. If the criterion is
satisfied, the segment is classified as treatable at block 520. No
further comparisons at decision blocks 510 through 518 are made. If
the criterion is not satisfied, i.e. if the LSC is greater than or
equal to A-B*SW, the segment is not classified. A non-treatable
classification is not made because a LSC greater than or equal to
the threshold does not distinguish between fast VT (treatable) and
SVT (non-treatable) with an acceptable level of confidence. As seen
in FIG. 5A, considerable overlap exists between the cluster of fast
VF points and the cluster of SVT points. The comparison performed
at block 508 is therefore an example of a one-way treatable rhythm
classification criterion defined as a two-dimensional,
population-based threshold comparison.
[0071] At block 510, a one-way treatable rhythm classification
criterion is defined as a non-linear two-dimensional threshold
comparison. In this example, SW is compared to a fixed,
population-based threshold C and this requirement is constrained by
the requirement that the normalized mean rectified amplitude (NMRA)
is less than a different fixed, population-based threshold D. In
this case, the thresholds for the two different signal
characteristics SW and NMRA are determined from a two-dimensional
plot of SW vs. NMRA. Two fixed threshold levels may be defined for
the two different signal characteristics when a particular rhythm
type exhibits a clustering of points in a particular quadrant of
the plot area. As such, the criterion in block 510 can be referred
to as a two-dimensional criterion in that a requirement is placed
on both signal characteristics based on a correlation of the two
signal characteristics found by a clustering of data points in a
two-dimensional plot of the two characteristics.
[0072] FIG. 5B is a two-dimensional plot 420 of NMRA as a function
of SW. In this example, a non-linear threshold 422 separates the
clustering of treatable rhythm points (including both VF and fast
VT points) occurring in the upper left portion of the plot area.
Below and to the right of the non-linear threshold 422, points
associated with both fast VT (treatable) and SVT (non-treatable)
rhythms overlap. As such, this non-linear threshold 422 based on
the correlation of NMRA and SW provides a high confidence in
separating a high frequency of treatable rhythms from all
non-treatable rhythms but lower confidence in separating a high
frequency of non-treatable rhythms from all treatable rhythms.
Threshold 422 is used as a one-way threshold for classifying
treatable rhythms at block 510 in FIG. 6. When satisfied, the
segment is classified as treatable at block 520. When not
satisfied, no classification is made and the process advances to
the next criterion at block 512.
[0073] The comparison at block 512 in FIG. 6 is an example of a
non-treatable rhythm classification criterion that is defined as a
one-dimensional threshold comparison. Referring again to FIG. 5B,
if the SW is greater than the threshold 424, virtually all plotted
points are associated with SVT (non-treatable). As such, this
one-dimensional threshold that is independent of other morphology
metrics provides separation of non-treatable rhythms from treatable
rhythms with a high degree of confidence. The constant L in block
512 is a population-based threshold, such as threshold 424 shown in
FIG. 5B, derived empirically from historical clinical data to yield
separation of non-treatable rhythms from treatable rhythms with a
high degree of confidence.
[0074] At block 514, another example of one-way classification
criterion defined as a non-linear two-dimensional threshold
comparison is provided. In this case, the criterion is a
non-treatable segment classification criterion. The thresholds M
and N are empirically derived, population-based thresholds
determined from a two-dimensional plot of NMRA vs. signal overall
variability (SOV). A plot of empirically measured NMRA as a
function of SOV resulted in a cluster of non-treatable rhythm
points in an upper left quadrant of the plot area. The non-linear
threshold thus requires NMRA to be greater than a population-based
threshold and SOV less than a population-based threshold, wherein
these thresholds are derived from the correlation of the plotted
SOV vs NMRA. The threshold M applied to NMRA can be said to be
constrained by the further requirement of SOV being greater than N
in order for the classification criterion to be satisfied and
result in a segment classification.
[0075] In one embodiment, SOV is calculated as the ratio of the sum
of the absolute differences between signal sample point amplitudes
of an n-sec segment waveform and the corresponding time-shifted
n-sec segment waveform to the sum of the absolute values of the
sample points in the n-sec segment. To illustrate, a 3-second
segment is acquired and the RR intervals are measured and ordered
from smallest to largest in a 12 RR interval buffer. The mean of
the first 6 RR intervals (the smallest RR intervals in the buffer)
is computed and the 3-second segment is shifted in time by half of
the mean RR interval. The difference between each signal sample
point in the original segment and the aligned signal sample point
in the time-shifted segment is computed. The absolute values of the
differences are summed SOV is then computed as the ratio of this
sum of absolute differences to the sum of the absolute values of
all of the sample point values in the original 3-second
segment.
[0076] The threshold comparison at block 516 is another example of
a two-dimensional, treatable segment classification criterion. In
this example, LSC vs. RR interval variability (RRV) for a
population of patients experiencing different rhythm types reveals
a separation of VF from SVT points when RRV is greater than a
linear threshold defined as a function LSC. The values for the
coefficient P and constant Q are determined from the empirical data
to provide separation of treatable rhythms from non-treatable
rhythms with a high degree of confidence. Considerable overlap
between fast VT and SVT points precludes separation of
non-treatable from treatable fast VT rhythms making this a one-way,
treatable segment classification criterion.
[0077] A final comparison in the sequence of population-based
threshold criterion is applied at block 518. At block 518, a
non-treatable segment classification criterion is defined by the
one-dimensional, population-based threshold applied to NMRA. If the
criterion is satisfied, the segment is classified as non-treatable
at block 522 and is otherwise not classified.
[0078] The comparisons made at blocks 510 through 518 allow
classification of a given n-second segment with a high degree of
confidence and minimized computational burden by performing the
comparisons that result in the highest frequency of classifications
being made first. A majority of cardiac signal segments will be
classified by the time the six comparisons performed at blocks 508
through 518 are completed (or earlier). Many segments will not
require all six comparisons to be made since once a classification
criterion is satisfied, no further comparisons in the sequence are
made for that segment. After classifying the segment at either
block 520 or 522, the next segment is acquired at block 504 and the
comparison sequence starts again at block 508.
[0079] If none of the classification criterion are satisfied after
completing the sequence of population-based threshold comparisons,
the segment will remain unclassified due to the one-way nature of
the threshold criteria. The process advances to more
computationally intensive discrimination comparison(s) at block
524, which may involve computing a patient-specific threshold. When
using a patient-specific threshold, these comparisons will require
computing measurements over more than one cardiac cycle or n-second
signal segment in order to obtain previous measurements from which
a patient specific threshold is computed. A current measurement
compared to the patient-specific threshold is computed from a
most-recent cardiac signal segment and compared to a
patient-specific threshold computed from an earlier-occurring
cardiac signal. Specific examples of a patient-specific threshold
comparison will be described in conjunction with FIG. 8.
[0080] FIG. 7 is a flow chart 600 of a method for establishing a
sequence of population-based threshold comparisons for use in
cardiac rhythm discrimination. At block 602, historical cardiac
signal segments are collected from a population of patients. The
cardiac signal segments are each classified at block 604 according
to a cardiac rhythm classification algorithm or manually by an
expert. A classification algorithm used here may be an automated
rhythm that requires high processing burden to achieve high
accuracy since the process shown in FIG. 7 is performed primarily
by an external computer processor. Automatically classified
segments may be additionally verified by an expert. Alternatively,
classification may be done exclusively by an expert.
[0081] At block 606, morphology metrics are computed for each
cardiac signal segment classified at block 604. In one embodiment,
LSC, SW, NMRA, SOV and RRV are computed for each segment. Other
embodiments may include any of these metrics and/or other
interval-based or morphological characteristics of the signal
segment. At block 608, the morphology metrics are plotted for the
classified signal segments. Both one-dimensional histogram plots
(e.g. as shown in FIGS. 4A-C) for each type of cardiac rhythm
and/or 2D scatter plots of one metric plotted as a function of a
second metric (e.g. as shown in FIGS. 5A-B) may be generated. In
one embodiment, multiple two dimensional combinations of the
morphology metrics listed above are plotted. In alternative
embodiments, 3D plots or other even higher dimensional combinations
of the morphology metrics may be generated.
[0082] At block 610, one-way classification threshold criterion are
set based on the generated plots. One-dimensional thresholds may be
selected visually by observing peaks and valleys between rhythm
classifications in single-variable histogram plots. Two-dimensional
thresholds may be selected by observing separation of clusters of
treatable and non-treatable points in 2D scatter plots. Thresholds
set at block 610 may alternatively be set automatically using an
algorithm that identifies a threshold above or below which a high
percentage (e.g. approximately 95%) of points will be classified
correctly as either treatable or non-treatable.
[0083] The percentage of all segments actually resulting in a
classification in response to a given threshold comparison will
vary. Ideally, the percentage of segments correctly classified is
high as well as the percentage of total segments classified.
However, in selecting the threshold at block 610, a primary goal is
to set a threshold that yields a high confidence in accuracy of the
resulting classification. Obtaining a high classification yield
(i.e. high percentage of all segments classified after performing a
one-way threshold comparison) will be achieved through selecting
metrics that present a high degree of separation between rhythm
types and ordering the threshold comparisons in a sequence that
most rapidly classifies the highest percentage of segments possible
using the fewest population-based threshold comparisons.
[0084] The thresholds set at block 610 may include one-dimensional
thresholds and two-dimensional thresholds. Thresholds may be
defined to separate non-treatable segments with a high degree of
certainty or to separate treatable segments with a high degree of
certainty. Because considerable overlap may occur between some
rhythm types, such as fast VT and SVT, any given threshold
criterion is generally defined for use in treatable segment
classification or non-treatable segment classification, but not
both (i.e. a one-way classification criterion) as described
above.
[0085] At block 612, the threshold resulting in the highest yield
or highest frequency of segment classifications is identified and
will be the first threshold comparison performed in a sequence of
comparisons for cardiac signal classification. In one embodiment,
the threshold comparison that results in the highest frequency of
treatable rhythm segments being classified is identified at block
612 to be used as the first comparison in the sequence.
Identification of a threshold comparison that identifies the
highest frequency of treatable rhythm classifications after just
one threshold comparison may allow faster and more efficient
detection of a treatable cardiac event. In other embodiments, the
threshold comparison resulting in the highest yield of classified
segments, either treatable or non-treatable segments, may be
identified at block 612.
[0086] For example, the first comparison determined at block 612
may be a classification criterion that results in at least 50% of
the treatable rhythm segments being correctly classified. None of
the non-treatable rhythm segments may be classified after the first
threshold comparison. Priority is given to classifying the highest
percentage of treatable segments as quickly as possible in order to
advance efficiently toward cardiac event detection.
[0087] At block 614, the remaining thresholds are ordered based on
classification yield. In this way, a sequence of population-based
threshold comparisons is identified that results in classification
of the highest possible percentage of segments upon each
consecutive comparison in the sequence. This ordering of the
threshold comparisons results in the quickest segment
classification resulting in the most computationally efficient
cardiac event detection. The ordering is not necessarily dependent
on whether the classification is for a treatable or a non-treatable
segment. In one embodiment, the goal of the comparison sequence is
to classify the segment as either treatable or non-treatable using
the fewest comparisons possible. The comparisons are selected to be
computationally relatively simple comparisons involving one or
two-dimensional thresholds applied to signal morphology metrics
that, at least for the initial threshold comparisons, do not pose
high computational burden on the IMD processor. In other
embodiments, the comparison sequence may be prioritized to classify
the highest percentage of treatable rhythm segments on each
consecutive threshold comparison.
[0088] As the sequence progresses, the classification criterion may
provide a lower classification yield and/or become more
computationally complex. A final population-based threshold
comparison may involve a relatively more complex algorithm. In one
embodiment, an overall probability-correlation based method for
classifying signal segments may be used as a final population-based
threshold comparison in the sequence of classification criteria.
The probability-correlation based method may generally correspond
to methods disclosed in U.S. patent application Ser. No.
12/415,445, hereby incorporated herein by reference in its
entirety. Briefly, the probability of a segment being a treatable
segment is computed for each of the computed morphology metrics.
The correlation coefficient between pairs of the metrics is then
computed such that an overall treatable probability can be
computed. The overall treatable probability is computed by summing
the products of probability-based coefficients and correlation
coefficient differences. This overall treatable probability is then
compared to a population-based threshold.
[0089] At block 616, the population-based thresholds derived at
block 610 from the patient population data and the comparison
sequence determined at block 614 are stored at block 616 as a
comparison sequence for use in classifying cardiac signal segments.
This sequence may then be used in combination, if needed, with
subsequent cardiac signal classification criteria which rely on
patient-specific thresholds. The comparison sequence may be
programmed into an IMD for use in a cardiac event detection
algorithm.
[0090] FIG. 8 is a flow chart 700 of one method for detecting a
cardiac event using sequential discrimination of cardiac signal
segments. At block 701, the population-based classification
thresholds and an established sequence of population-based
threshold criteria is obtained through empirical analysis of
historical patient population data as described above in
conjunction with FIG. 7. At block 702, the thresholds and
corresponding comparison sequence is stored in an IMD being
implanted in a patient for use in cardiac signal
classification.
[0091] At block 704, a cardiac signal segment is sensed, and
morphology metrics are computed at block 706. At block 708, the
sequence of classification comparisons is initiated. Threshold
comparisons at block 710 are used to determine if a one-way
classification criterion in the sequence of criteria is met at
block 712 as described previously. If a segment classification
criterion is satisfied at block 712, the segment is classified at
block 714. All remaining comparisons in the stored sequence are
cancelled at block 716. A determination is made at block 726
whether cardiac event detection criteria are met. If a cardiac
event is detected, the episode is stored and a therapy is delivered
as appropriate at block 728.
[0092] If the entire sequence of population-based threshold
criteria is applied (block 718), without yielding a segment
classification, a patient-specific threshold comparison is made to
classify the cardiac signal segment. A two-way patient-specific
threshold comparison involves computing a characteristic of the
cardiac signal during a most recent cardiac signal segment or
portion thereof and computing the same characteristic for a
previous portion of the same or an earlier cardiac signal segment.
This allows a patient-specific change in the cardiac signal to be
evaluated to determine if the change corresponds to a change from a
non-treatable to a treatable rhythm.
[0093] At block 720, a rate-based or morphology based
patient-specific threshold is computed. Computation of a
patient-specific threshold includes computing a metric of the
cardiac signal sensed in the patient and establishing the threshold
using that metric. The patient-specific threshold may be computed
for an earlier portion of the cardiac signal or the same portion of
the cardiac signal from which the comparative measure is being
taken.
[0094] If the patient-specific threshold is computed for an earlier
portion of the cardiac signal, the same metric used to compute the
threshold may be updated for a most recent cardiac signal segment
and compared to the threshold. The patient-specific threshold
determined from an earlier-occurring portion of the cardiac signal
is used in a comparison to detect a change that has occurred over
time in a given metric that indicates that the patient's rhythm has
deteriorated to a treatable rhythm. The time period over which the
change occurs may be as little as from one cardiac beat to the next
or within several cardiac beats, for example up to 12 beats. The
patient-specific threshold may be computed as a percentage or range
of a rate or morphology metric determined for the earlier time
interval, which may be within seconds or minutes of the current
cardiac signal segment. At block 721, a comparison between the
patient-specific threshold and the same metric computed (i.e.,
updated) for a most recent cardiac signal segment is made.
[0095] In one illustrative embodiment, a patient-specific
evaluation of the cardiac signal is performed at block 721 to
detect the onset of a treatable rhythm at block 722. The onset of a
treatable rhythm is generally marked by an increase in rate, a
decrease in RR interval variability, and the onset of an R-wave
morphology associated with a treatable rhythm.
[0096] In this example, an increase in rate is determined using a
patient-specific RR interval threshold. A rate increase may be
detected by comparing a recent mean RR interval computed over a
most recent time interval to a patient-specific threshold computed
as a mean RR interval determined over a different, earlier time
interval. If this increase in rate is one indication that a
treatable rhythm onset is likely to have occurred.
[0097] A patient-specific threshold may also be defined based on a
signal characteristic measured for the same time interval as the
metric being compared to the patient-specific threshold. In this
case, the metric being compared to the threshold is a different
metric or characteristic of the signal than the metric used to
compute the threshold, but would typically have the same or similar
units of measure. For example, a measurement of RR interval
variability for the most recent RR intervals is computed as the
difference between the most recent maximum and minimum RR
intervals, e.g. the maximum and minimum RR intervals out of the
most recent 4 RR intervals or other number of recent intervals. If
the RR interval variability is less than a percentage of the mean
RR interval determined for the same most recent RR intervals, this
low RR interval variability alone or combined with an increase in
rate can be used to detect the onset of a treatable rhythm at block
722.
[0098] Alternatively, a patient-specific RR interval range
threshold may be computed as a difference between a maximum and
minimum interval for a previous time interval and compared to the
RR interval range computed for a most recent time interval. If an
increase in rate is detected based on a patient-specific rate
threshold (as opposed to a nominally defined or population based
threshold) and a decrease in RR interval variability based on a
patient-specific variability threshold is detected, a treatable
rhythm onset may be detected at block 722.
[0099] Additionally or alternatively, rate onset detection at block
720 may require the detection of a change in R-wave morphology. An
R-wave morphology metric may be computed for a most recent R-wave
or group of R-waves and compared to the same metric computed for a
previous R-wave or group of R-waves. If the R-wave morphology
metric for the most recent R-wave(s) exhibits a change compared to
the patient-specific threshold computed from an earlier time
interval, the beat morphology change supports the detection of a
treatable rhythm onset at block 720. The detection of a treatable
rhythm onset using a rate onset metric and a beat morphology onset
metric may use methods generally disclosed in U.S. patent
application Ser. No. 12/430,301, hereby incorporated herein by
reference in its entirety.
[0100] A patient-specific morphology based threshold may be related
to a specific beat feature, such as a slope, amplitude, slew rate,
width, or the like. Other morphology-based patient-specific
thresholds may be determined using an overall beat morphology, such
as a wavelet analysis as generally described in U.S. Pat. No.
6,393,316 (Gillberg), hereby incorporated herein by reference in
its entirety.
[0101] The two-way threshold comparison performed at block 721 will
result in a classification of the cardiac signal segment. If the
treatable rhythm onset detection criteria are not satisfied at
block 722, based on the comparison at block 721, the segment is
classified as non-treatable. If the treatable rhythm onset
detection criteria are satisfied at block 722, the segment is
classified as treatable at block 725. As such, after completion of
the patient-specific threshold comparison, all cardiac signal
segments will be classified.
[0102] Classification criteria applied at block 722 which includes
at least one patient-specific threshold criterion, will always
result in a segment classification at one of blocks 724 or 725. In
contrast, a classification criterion applied at block 712 that is
defined using a population-based threshold may or may not result in
a segment classification. Additional comparisons must be performed.
The patient-specific threshold is used in a two-way classification
criterion that results in a classification of all remaining
segments that have not been classified after performing the one-way
population-based threshold comparisons.
[0103] If a non-treatable classification is made at block 724, a
cardiac event will not be detected for the current cardiac signal
segment. The process returns to block 704 to begin the process of
classifying the next cardiac signal segment.
[0104] If the segment is classified as treatable at block 725, the
segment may cause a cardiac detection threshold to be met at block
726. If so, the detected event is stored at block 728 and a therapy
may be delivered if appropriate. If the cardiac event detection
threshold is not met at block 726, the process returns to block
704.
[0105] Thus, a medical device and associated method for detecting
cardiac events have been presented in the foregoing description
with reference to specific embodiments. It is appreciated that
various modifications to the referenced embodiments may be made
without departing from the scope of the disclosure as set forth in
the following claims.
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