Espinosa Reynoso

G W Schneider

JJ C Militello

S E Greenhut

T A Nappholz

CiteSeerX

Evaluation of an Event Classifier for
Pacemaker Ventricular Evoked Response Detection
SE Greenhut, TA Nappholz, GW Schneider, C Militello, JJ Espinosa Reynoso
Micro Systems Engineering, Englewood, Colorado, USA
Abstract
In order to improve depolarization waveform
classification, a new classifier for pacing evoked
response vs. lead polarization was developed. The
classifier was based on identification of polarization
characteristics, considering deviations to be indicative of
capture. Waveform classifier features were trained using
saline tank and acute patient (pt) data. A total of 31 pts
with 9 lead models were studied acutely prior to
pacemaker implant. Following placement of lead(s), pts
were paced at 1.0–4.8V (bipolar and unipolar), paced at
specific AV delays to induce fusion, and paced to
determine pacing threshold using an external pacemaker.
The first 12 pts were part of the training set and the
remaining 19 pts represented the test set. Over 24,000
ventricular pacing pulses were analyzed. Test set
sensitivity was 99.5% and specificity was 97.2%.  In
conclusion, the capture event classifier showed improved
fusion classification and a greater lead tolerance.
1. Introduction
Historically, clinicians measure the pacing threshold at
pacemaker follow-up by visual electrocardiographic
determination of pacing depolarization (capture) while
varying the pulse amplitude.  The pacing output
amplitude is then typically set to approximately twice the
pacing threshold measured in volts.
During the past several years, pacemakers have
become available that automatically and continuously
evaluate the success of the ventricular pacing pulse to
depolarize myocardium [1,2].  This capability has
permitted such pacemakers to automatically set the pulse
output to meet the changing pacing threshold.
Additionally, if non-capture is detected, the device
quickly issues a back-up pace at higher pacing output.
This feature is designed to conserve battery charge so
that pacemaker longevity is enhanced and increase the
likelihood of ventricular capture in the event of
increasing pacing threshold.  The pacing threshold may
change for a variety of reasons, including lead
maturation, changes in medication, lead micro-
dislodgment, pathologic changes or physiologic changes
such as exercise [3,4].
Although pacemakers that provide automatic output
are available, fusion of intrinsic and paced
depolarizations has caused misclassification rates of up to
25% [1].  True capture waveforms that are misclassified
as non-capture (e.g., as a result of fusion) cause
unnecessary back-up pacing pulses and unnecessary
threshold searches that waste the battery charge that the
feature is designed to preserve.
 Additionally, the pacing lead selection of previous
devices has been limited to leads specifically treated to
attain low polarization artefact and to unipolar pacing [5].
The event classifier described here was designed to
reduce the incidence of false non-capture detection due to
fusion, permit the use of leads with greater polarization
artefact, and permit the use of all pacing/sensing polarity
combinations.
2. Methods
2.1. Event classifier
An event classifier was developed to separate
ventricular evoked response and lead polarization artifact.
In order to maximize the detection of fusion
depolarizations as capture, polarization artifact was
characterized through the use of 6 waveform features.
The observation that polarization artifact waveform
morphology is relatively constant (if visible) between
leads and pulse amplitudes was exploited by classifying
waveforms that were not similar to polarization artifact as
capture.  For lower polarization leads and lower pacing
amplitudes, the polarization artifact is typically non-
existent at normal pacemaker amplification and therefore
poses no difficulty for the simplest of waveform
classifiers.  However, using higher polarization leads
and/or higher pacing amplitudes, a more sophisticated
event classifier is required.  An exemplary polarization
waveform for these conditions is shown in Figure 1.
Figure 2 shows a typical evoked response waveform.
0276−6547/02 $17.00 © 2002 IEEE 157 Computers in Cardiology 2002;29:157−160.
The morphology of evoked response depolarization
waveforms may vary significantly due to fusion with
intrinsic depolarizations, variations in pulse amplitude (a
result of additive polarization effects), and patient-to-
patient variability.  The waveform features are indicted in
Figures 1 and 2 (upper case) and are defined as follows:
INGR1: Sum of the positive differences* until the first
negative difference.
INGR2: Sum of the absolute value of all differences
starting with the first negative difference.
CNT1: Number of samples in INGR1.
CROSS: Flag = 1 if at least one sample following
CNT1 samples is a positive difference.
MAX_POS: Maximum positive difference for all
samples following the first x samples.
NEG_AMP: Maximum negative difference.
*Amplitude difference = sample amplitude – blanking
period amplitude.
Seven parameters are defined to classify waveforms
given the measured features above.  These parameters
(lower case italics shown in Figures 1 and 2) are:
w: Width of analysis window.
x: Number of samples to exclude before measuring
MAX_POS.
w1: Limit on CNT1.
zn: Absolute limit on NEG_AMP.
zp: Limit on MAX_POS.
a1: Lower limit on AREA* (below which is non-
capture).
a2: Higher limit on AREA (above which is capture).
*AREA may equal INGR1 + INGR2 or INGR2
depending upon the value of CNT1 (see Figure 3).
Once the waveform features are calculated, the
algorithm of Figure 3 classifies events as capture/non-
capture.  A sufficiently negative amplitude immediately
classifies a waveform as capture.  Otherwise, a low area
classifies a waveform as non-capture.  If the positive
portion of the waveform is wide and high, or the area is
large, capture is classified.  In the case of medium area, a
cross from negative to positive values indicates capture.
In other cases, non-capture is classified.
2.2. Procedure
Patients of at least 18 years of age and undergoing
initial pacemaker implant or replacement were included
in the study.  An external pacemaker was connected to
atrial (if present) and ventricular intracardiac leads.  The
hardware was controlled using software residing on a PC
which simulated the pacemaker microprocessor.  The PC
allowed real-time display of intracardiac electrograms
and markers, plus stored data for offline analysis.
A pacing/sensing analyzer was used to measure pacing
thresholds.  Once adequate lead placement was
determined, the external pacemaker was connected to the
implanted lead(s) and indifferent electrode temporarily
placed in the pulse generator pocket.
Three procedures were attempted on each patient:
1. Fixed rate overdrive A/V or ventricular
pacing.  Unipolar and bipolar pacing/sensing
at 1.0, 2.0, 3.6, and 4.8V.
2. A/V pacing at 5 different AV delays designed
to invoke fusion.  (In patients with atrial and
ventricular leads and AV conduction.)
3. Automated threshold search decrementing
from 3.6 and 4.8V with unipolar and bipolar
pacing/sensing.
Blanking Level
Pace Pulse
Blanking Period (~20ms)
CNT1
INGR1 INGR2
Crossing
MAX_POS
x
zn
w1
zp
w
Figure 1.  Schematic polarization waveform with measured waveform features in upper case and waveform
discrimination parameters in lower case italics.  Informational messages are indicated by mixed upper and lower case.
158
Blanking Level
Pace Pulse
Blanking Period (~20ms)
INGR2
Crossing
zn
w1
zp
w
x
MAX_POS
NEG_AMP
Figure 2.  Schematic evoked response waveform with measured waveform features in upper case and waveform
discrimination parameters in lower case italics.  Informational messages are indicated by mixed upper and lower case.
NEG_AMP >
zn
?
CNT1 > w1
?
AREA = INGR1 + INGR2
AREA = INGR2
AREA < a1
?
CNT1 > w1
?
MAX_POS <
zp
?
AREA > a2
?
CROSS
?
Non-Capture Capture
NO
NO
YES
NO
NO
NO
NO YES
YES
YES
NO
YES
YES
YES
Figure 3.  Flow chart of event classifier algorithm.
2.3. Data analysis
Electrogram data recorded were replayed offline to
generate the set of waveform features.  The features were
imported to an Excel spreadsheet that determined
capture/non-capture classification.  The electrogram data
were visually inspected to determine the true
classification and false positives/false negatives were
identified.
3. Results
3.1. Clinical data
There were 21 male and 10 female patients studied
that ranged in age from 45 – 90 years (76+9 years).
Twelve patients were tested in DDD mode and 19 in VVI
mode.  A total of 33 leads were tested as follows:
Biotronik PX BP (19), Biotronik SX BP (3), Medtronic
4092 (3), St. Jude 1472T (2), Medtronic 5028 (2),
Intermedics (1), Biotronik RX BP (1), Medtronic 4058M
(1), and CPI 4261 (1).  A total of 3 chronic leads were
studied with 2 patients having chronic leads explanted
and replaced with acute leads.
3.2. Detection accuracy
Adjustments were made to the event classifier
algorithm during data collection of the first 12 patients
which comprised the training set.  The algorithm was
frozen and the 19 additional patients were designated the
test set.  There were a total of 24,246 ventricular
depolarizations analyzed for correct determination of
capture.
The training set results are shown in Table 1.  The
159
sensitivity and specificity of detection were 98.8% and
100%, respectively.  Sixty-eight of the false negatives
occurred in Patient 4.  They were a result of a recurrent,
unusual evoked response waveform with low amplitude.
The remaining 15 were a result of fusion.  Of the patients
in the training set, 1 patient had sensitivity less than 99%
and 0 patients had specificity less than 99%.
Table 1.  Training Set Detection Accuracy
Detected as
Non-Capture
Detected as
Capture
True Non-Capture 113 0
True Capture 83 6588
The test set detection accuracy is given in Table 2.
The sensitivity and specificity of detection were 99.5%
and 97.2%, respectively.  All 88 false negatives were a
result of fusion.  Of the patients in the test set, 1 patient
had sensitivity less than 99% and 2 patients had
specificity less than 99%.
Table 2.  Test Set Detection Accuracy
Detected as
Non-Capture
Detected as
Capture
True Non-Capture 173 5
True Capture 80 17293
4. Discussion
An event classifier algorithm for the discrimination of
capture and non-capture to permit an automatic capture
control algorithm for implanted pacemakers has been
described.  The algorithm was shown in acute clinical
studies to be sensitive to the detection of capture even in
the presence of fusion, while being specific to the
detection of non-capture in a variety of leads, polarities,
and pacing amplitudes.
The only detection errors in the training set were a
large number of false negatives in Patient 4.  The
algorithm parameters were not adjusted to accommodate
these capture waveforms because it was thought that
doing so would cause the detection of true non-capture to
be compromised.  Therefore, Patient 4 was considered an
outlier for evoked response detection.  It was not
expected that the event classifier would be adequate for
100% of patients.  A separate, signal quality check (SQC)
algorithm (not described here) was designed to
automatically select patients with adequate evoked
response and lead polarization characteristics prior to
activation of the automatic capture control algorithm.  It
is expected that the SQC algorithm would determine
Patient 4 to be unacceptable for further event classifier
analysis and therefore not permit automatic capture
control in this patient.
Detection of true capture as non-capture leads to
wasted battery charge through the issuance of
unnecessary backup pulses and threshold searches.
However, detection of true non-capture as capture causes
the absence of a ventricular depolarization, and in the
absence of an intrinsic rhythm potentially leads to
ventricular asystole.  The 5 false positives in the test set
occurred in 2 patients with high polarization leads
(Medtronic 4092 and CPI 4261).  All had
uncharacteristically large negative artifact at low pacing
amplitudes with no accompanying positive-going artifact.
The 4092 lead had all 4 false positives in bipolar
pacing/sensing configurations only.  No errors occurred
during unipolar pacing and/or sensing configurations.
This appeared to be a result of a low microsurface area
ring electrode.  The 4261 lead had its single false positive
in unipolar pacing/sensing.  Again, the SQC algorithm is
designed to reject patient/lead/polarity combinations that
would cause the event classifier to incorrectly detect non-
capture as capture resulting from high polarization
artifact, as is the case with these two combinations.
A limitation of the study was its acute nature.  The
classifier is currently being evaluated in chronically
implanted pacemakers as part of the PACC multicenter
clinical study.
References
[1] Clarke M, Liu B, Schueller H, et al.  Automatic adjustment
of pacemaker stimulation output correlated with
continuously monitored capture thresholds: A multicenter
study.  European Microny Study Group.  PACE 1998;
21:1567-1575.
[2] Suri R, Harthorne JW, Galvin J.  Automatically optimizing
pacing output: An excellent idea, but with potentially lethal
pitfalls. PACE 2001; 24[Pt. I]:520-523.
[3] Dohrmann ML, Goldschlager NF.  Myocardial stimulation
threshold in patients with cardiac pacemakers: Effect of
physiologic variables, pharmacologic agents, and lead
electrodes.  Cardiol Clin 1985; 3:527-537.
[4] Preston TA, Fletcher RD, Lucchesi BR, et al.  Changes in
myocardial threshold.  Physiologic and pharmacologic
factors in patients with implanted pacemakers.  Am Heart J
1967; 74:235-242.
[5] Lau C, Nishimura SC, Yee R, et al.  Intraoperative study of
polarization and evoked response signals in different
endocardial electrode designs.  PACE 2001; 24:1055-1060.
Address for correspondence.
Saul Greenhut, Ph.D.
Micro Systems Engineering
8 Inverness Drive East
Suite 140
Englewood, CO 80112
greenhuts@msei-co.com
160


Evaluation of an Event Classifier for Pacemaker Ventricular Evoked Response Detection

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Evaluation of an Event Classifier for Pacemaker Ventricular Evoked Response Detection

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