Detecting abnormal activation of heart

Abnormal cardiac activity in a patient may be detected by acquiring an electrocardiogram waveform associated with a first level of physiologic activity of the patient and an electrocardiogram waveform associated with a second, different level of physiologic activity of the patient. QRS complexes of the electrocardiogram waveforms are compared to identify an abnormal portion of the QRS complex of the electrocardiogram waveform associated with the second level of physiologic activity. The abnormal portion of the QRS complex is processed to detect abnormal physiologic activity.

BACKGROUND 
The invention relates to detecting abnormal activation of the heart. 
Abnormal activation of the heart may be indicative of myocardial ischemia 
or myocardial infarction. Myocardial ischemia, which is also known as 
reduced myocardial blood flow, may be indicative of coronary artery 
disease. Myocardial ischemia may be provoked under clinical conditions by 
applying stress to a patient, and may be indicated by localized changes in 
heart function due to localized changes in myocardial blood flow. 
Accordingly, myocardial ischemia and related coronary artery disease may 
be diagnosed by measuring such localized changes in heart function. 
Myocardial infarction is a more severe condition. Myocardial infarction may 
result in a complete loss of activation of areas of the heart. This 
results in loss of blood flow to areas of the heart. 
Localized changes in heart finction resulting from myocardial ischemia or 
myocardial infarction may include electrical changes in the heart. 
Electrical activity of the heart generates an electrical potential on the 
body surface. At any given location on the body, this potential includes 
contributions from every region of the heart, with the contribution from a 
particular region being inversely proportional to the square of the 
distance from the region to the location on the body. Given the anatomy of 
the heart and chest, the potentials at most locations on the body surface 
effectively represent summed electrical activity from a large region of 
the heart. 
The body surface electrocardiogram (ECG) is a measure of electrical 
activity of the heart. The ECG provides a measure of the potential 
difference between two points on the body surface as a continuous finction 
of time. The ECG is routinely measured using standard ECG electrodes. 
SUMMARY 
The invention features detecting abnormal activation of the heart as an 
indicator of myocardial ischemia or myocardial infarction. To this end, 
the invention includes measuring abnormal signals in the QRS complex of an 
electrocardiogram ("ECG") during periods of differing cardiac activity, 
such as may be associated with physiologic stress testing. The QRS complex 
of an ECG beat corresponds electrically to contraction of the ventricles 
of the heart. 
Under conditions of normal myocardial blood flow, the net conduction vector 
of ventricular depolarization (i.e., the spread of a "wave" of electrical 
activation through the heart) is smooth and regular. In general, this 
results in a body surface ECG having a QRS complex with a regular pattern. 
Myocardial insufficiency due to myocardial ischemia results when there is 
an obstruction in a coronary artery within the heart such that the heart 
demands more blood flow than the obstructed artery can provide. When this 
occurs, a small area in the immediate vicinity of the coronary obstruction 
receives inadequate blood flow. When the conduction vector (i.e., the 
electrical activation wave) arrives at this area, there may be an abrupt 
change in the net direction of the conduction vector. This direction 
change may result in a step change in the body surface potential. High 
pass filtering of the ECG may transform this step change into a notch or 
slur on the QRS complex. However, the notch or slur may be too small to be 
observable in the filtered ECG. In general, notches or slurs in the QRS 
complex may be referred to as abnormal intra-QRS potential ("AIQP") 
signals. 
Thus, the area of myocardial insufficiency may disorder the depolarization. 
If this disordering is confined to a local region, it will result in a 
notch or slur in the filtered ECG. If the disordering becomes more global 
in nature, the shape of the QRS complex may be altered dramatically so as 
to result in a directly observable gross effect in the ECG. 
Physiologic stress testing or other techniques for increasing the level of 
cardiac activity may be used to induce myocardial insufficiency associated 
with myocardial ischemia and coronary artery disease. In general, a 
physiologic stress test includes a rest stage in which an ECG having a 
normal QRS complex may be measured, and progressive stages of exercise 
during which changes may manifest in the QRS complex. These changes may 
result in notches and slurs that are difficult to detect because they are 
too small to be discerned visually from the standard ECG and because they 
are masked by noise. 
During an acute myocardial infarction, ventricular tissue is progressively 
starved of blood, which eventually results in scarring of the tissue. 
While ischemic tissue may be partially conducting, scar tissue is 
non-conducting. As such, AIQP signal levels will increase during 
progressive myocardial ischemia associated with myocardial infarction, but 
may cease to increase in the presence of scarring. Accordingly, myocardial 
infarction may be diagnosed and monitored by measuring changes in AIQP 
signals over time. 
Large-scale notches and slurs (&gt;50 .mu.V) have been associated with 
disruption in ventricular conduction due to scarring after myocardial 
infarction. However, small-scale changes in notches and slurs (&lt;50 .mu.V) 
in the presence of either myocardial ischemia or acute myocardial 
infarction have not been reported in the clinical literature. Ordinarily 
such signals are invisible in the standard ECG, as they are below the 
level of noise ordinarily experienced with exercise or other activity. 
They are also indistinguishably superimposed on the large, normal 
component of the QRS complex. 
In one aspect, generally, the invention features detecting abnormal cardiac 
activity in a patient. Electrocardiogram waveforms associated with first 
and second levels of physiologic activity of the patient are acquired and 
QRS complexes of the electrocardiogram waveforms are compared to identify 
an abnormal portion of the QRS complex of the electrocardiogram waveform 
associated with the second level of physiologic activity. The abnormal 
portion of the QRS complex is then processed to detect abnormal cardiac 
activity. 
Levels of physiologic activity may correspond to physiologic states induced 
by factors such as sleep, exercise, emotion, and patient motion or 
position. Characteristics of the heart rate may be indicative of the level 
of physiologic activity. 
Embodiments of the invention may include one or more of the following 
features. The patient's heart may be stressed to produce the second level 
of physiologic activity. For example, a physiological stress test may be 
performed on the patient. In this instance, the first level of physiologic 
activity may be associated with a rest stage of the stress test, and the 
second level of physiologic activity may be associated with an increased 
exercise stage of the stress test. 
Subensembles of beats in the electrocardiogram waveforms corresponding to 
the first and second levels of physiologic activity may be produced. Each 
subensemble of beats may be processed to produce a representative beat. 
QRS complexes of the representative beats may be processed to identify an 
abnormal portion of the QRS complex of the electrocardiogram waveform 
associated with the second level of physiologic activity. The 
representative beats may be produced by computing a measure of statistical 
central tendency from the subensemble of beats, such as by subensemble 
averaging or generation of median beats. 
The QRS complexes may be compared by subtracting a QRS complex of the 
electrocardiogram associated with the first level of physiologic activity 
from a QRS complex of the electrocardiogram associated with the second 
level of physiologic activity. A model of the QRS complex of the 
electrocardiogram waveform associated with the first level of physiologic 
activity may be generated. The model then may be subtracted from the QRS 
complex of the electrocardiogram associated with the second level of 
physiologic activity. 
Processing of the abnormal portion of the QRS complex may include 
determining a root-mean-square amplitude, energy, or duration of the 
abnormal portion. The processing also may include measuring timing or 
bandwidth of the abnormal portion relative to the QRS complex. 
Electrocardiogram waveforms associated with other levels of physiologic 
activity of the patient also may be acquired. The QRS complexes of these 
waveforms may be compared to identify abnormal portions of the QRS 
complexes that then may be processed. Processing may include comparing the 
abnormal portions of the QRS complexes, measuring a parameter of each of 
the abnormal portions of the QRS complexes and comparing parameters 
associated with different QRS complexes, or identifying a trend in the 
abnormal portions for changing levels of physiologic activity. 
Myocardial ischemia may be detected based on the abnormal portions of the 
QRS complexes. For example, a quantitative measure of a degree of 
myocardial ischemia may be determined. Similarly, a location of the 
myocardial ischemia may be determined based on the abnormal portion of the 
QRS complex. 
In another aspect, generally, the invention features detecting myocardial 
infarction in a patient by continuously acquiring electrocardiogram 
waveforms associated with physiologic activity of the patient. QRS 
complexes of the electrocardiogram waveforms are compared to identify 
abnormal portions of the QRS complexes. Trends in the abnormal portions 
are identified to detect myocardial infarction. 
In another aspect, generally, the invention features detecting abnormal 
cardiac activity in a patient after physiologically stressing the patient. 
An electrocardiogram waveform is acquired from the patient, and a model of 
a QRS complex of the electrocardiogram waveform is generated. The QRS 
complex of the electrocardiogram waveform is then compared to the model of 
the QRS complex to identify an abnormal portion of the QRS complex. The 
abnormal portion is then processed to detect abnormal cardiac activity. 
Other features and advantages of the invention will be apparent from the 
following description, including the drawings, and from the claims.

DESCRIPTION 
Referring to FIG. 1, a ECG system 100 that may be used to measure 
myocardial ischemia during physiologic stressing of a patient's heart 
includes a set 105 of electrodes 110. The electrodes may be standard ECG 
electrodes, or may be an array of electrodes applied to cover the 
anterior, lateral and posterior areas of the torso. The electrodes 
function separately from one another, but may be physically affixed 
together to form a flexible band or other arrangement. The system 100 
further includes a set of leads 115 that connect the electrodes to a 
system controller 120. The controller includes signal conditioning 
circuitry 125 and a processor 130. The circuitry 125 receives analog 
signals from the leads 115 and provides conditioned digital signals to the 
processor 130. The processor 130 processes the conditioned signals to 
produce results that the processor then provides to a connected display 
135 or to an output device 140, such as a printer. The processor may 
optionally control physiologic stress of the patient's heart by 
controlling an exercise device, such as a treadmill 145 having 
programmable slope and walking speed, through control signals supplied 
through a lead 150. Similarly, an optional recording device 155 (FIG. 1A) 
of an ambulatory system may be used to record signals from the leads for 
an extended period of time (e.g., 24 hours). The recording device 155 then 
is connected to the controller 120 to permit the controller 120 to process 
the recorded data. 
Referring to FIG. 2, the controller 120 processes ECG data according to a 
procedure 200. Initially, the controller 120 records a continuous ECG from 
the body surface using the electrodes 110 (step 205). The recorded ECG may 
correspond, for example, to a physiologic stress test that includes a rest 
period, stages of increasing stress, and a recovery period. This type of 
ECG may be analyzed to detect myocardial ischemia due to coronary artery 
disease. The recorded ECG also may correspond to a resting ECG recorded 
from a supine patient. A resting ECG may be recorded routinely or in an 
emergency room when an acute myocardial infarction is suspected. 
Similarly, the recorded ECG may be produced using a bedside monitor in a 
hospital. This may be done, for example, after a revascularization 
procedure, such as balloon angioplasty to open a previously occluded 
artery, in which case analysis of the ECG may identify reocclusion of the 
artery. The recorded ECG also may be produced using an ambulatory recorder 
to, for example, assess transient myocardial ischemia. Typically, an ECG 
produced using an ambulatory recorder will include twenty four hours or 
more of ECG data. 
Next, the controller 120 selects subensembles of beats from the recorded 
ECG (step 210). The entire ECG recording is an ensemble of beats, and the 
controller 120 divides the ensemble into subensembles. Examples of 
subensembles may include: stages of a stress test; regular intervals 
(e.g., five to fifteen minute intervals in an emergency room); and periods 
of the day in, for example, ambulatory or hospital monitoring. Similarly, 
subensembles may be selected based on levels of physiologic activity 
associated with recorded data. By selecting subensembles, abnormal intra 
QRS potential ("AIQP") signals measured in each subensemble can be 
compared to each other. In addition, reference AIQP signals and signal 
levels can be designated, and time trends of AIQP signals changing in 
appearance or otherwise can be monitored and graphed. 
The controller 120 then produces a representative beat for each subensemble 
(step 215). In general, a representative beat is a low-noise beat obtained 
by combining a collection of beats that have been time-aligned. Time 
alignment may be performed at the QRS complex. See Lander et al., 
"Principles and Signal Processing Techniques of the High-Resolution 
Electrocardiogram", Prog. Cardiovasc. Dis. 35(3):169-188, 1992, which is 
incorporated by reference. Referring to FIG. 3, the QRS complex 300 of an 
ECG beat 305 corresponds to ventricular contraction. Techniques for 
combining beats to form a representative beat include median beat 
formation and subensemble averaging. 
Finally, the controller 120 selects the QRS complexes of the representative 
beats to detect AIQP signals (step 225), and analyzes the AIQP signals to 
detect abnormal activation of the heart (step 230). During physiologic 
stress testing or under other conditions (e.g., ambulatory monitoring) in 
which the patient's cardiac level changes with time, the controller 120 
may process the representative beats by subtracting a reference beat from 
a beat under consideration. For example, during physiologic stress 
testing, the QRS complex of the reference beat, QRS.sub.ref, may be 
defined as the representative beat for the initial rest period of the 
stress test. As such, the AIQP signal, AIQP.sub.i, in the QRS complex, 
QRS.sub.i, of a subensemble i may be expressed as: 
EQU AIQP.sub.i =QRS.sub.i -QRS.sub.ref 
The AIQP signals for different stages of the stress test may be analyzed to 
automatically detect myocardial ischemia. For example, large changes in 
the magnitude of the AIQP signals as the level of exercise increases may 
be indicative of myocardial ischemia while small changes are not 
indicative of ischemia. Similarly, an AIQP signal having a value that 
exceeds a clinically determined threshold may indicate a risk of 
arrhythmia while an AIQP signal having a smaller value may be indicative 
of no such risk. A position within the QRS complex of the peak value of 
the AIQP signal may be indicative of the location of an occluded artery. 
For example, a peak in the first half of the QRS complex may be indicative 
of an occlusion in the left anterior descending artery while a peak in the 
second half may be indicative of an occlusion in the right coronary 
artery. A peak near the middle of the QRS complex may be indicative of an 
occlusion of the left circumflex artery. There may be considerable overlap 
between these regions. As an alternative to automatic processing, or in 
addition to such processing, the AIQP signals may be displayed on the 
display 135 to permit a clinician to determine whether the AIQP signals 
are indicative of myocardial ischemia and coronary artery disease. 
The processing approach described above assumes that neither the AIQP 
signal nor significant noise is present in the reference beat. Another 
approach that is less sensitive to noise in the reference beat processes 
the representative beats by subtracting a model, QRS.sub.ref.sup.M, of the 
reference beat from a beat under consideration: 
EQU AIQP.sub.i =QRS.sub.i -QRS.sub.ref.sup.M. 
Similarly, a model, QRS.sub.i.sup.M may be generated for each beat under 
consideration and subtracted from the beat: 
EQU AIQP.sub.i =QRS.sub.i &lt;QRS.sub.1.sup.M. 
In general, the QRS complex can be modelled as the response of the cardiac 
system to an impulse stimulus at the atrioventricular node. Thus, the QRS 
complex may be expressed as the impulse response of a system represented 
by a linear difference equation: 
##EQU1## 
where y(n) is a discrete-time signal of N samples, n equals 0, 1, 2, . . . 
, N-1, .delta.(n) is the unit impulse, and the model is defined by the set 
of coefficients a.sub.i and b.sub.j. 
An AIQP signal may be defined as the difference between the original QRS 
complex and the modelled QRS complex. As shown in FIG. 4, a small slur 350 
in the QRS complex 355 is not visibly apparent. However, subtracting a 
modeled QRS complex 360 from the original QRS complex results in an AIQP 
signal 365 having a readily-apparent peak 370 corresponding to the slur. 
The model of the reference beat may be produced according to the procedure 
400 illustrated in FIG. 5. Initially, the onset and offset times of the 
QRS complex are found in the representative beat to produce a QRS waveform 
(step 405). The QRS onset time is computed from an absolute spatial 
velocity vector (ASVV). The ASVV is formed by simple differencing three 
ECG leads and combining them to form a vector magnitude. Any three leads 
that are approximately orthogonal to each other may be used. The QRS onset 
is defined by searching forward in time for the point in the PR interval 
(see FIG. 3) of the ECG waveform where the ASVV signal level increases 
abruptly, indicating the onset of the QRS complex. 
The QRS offset time is determined from an ECG waveform filtered using a 
highpass filter having a cutoff frequency of 40 Hz. The filtered ST 
segment of the ECG waveform is searched backward in time until a 
significant increase in filtered ECG signal level is detected, indicating 
the end of the QRS complex. 
Next, a Discrete Cosine Transform (DCT) is used to produce an 
energy-compacted form of the QRS waveform in the frequency domain (step 
410). The DCT produces damped cosinusoidal waveshapes that are well suited 
for representation by rational transfer functions of relatively low order. 
For example, given a discrete signal x(n) of N samples, where n equals 0, 
1, 2, . . . , N-1, the DCT of x(n) may be expressed as: 
##EQU2## 
for k=0, 1, . . . , N-1, and 
##EQU3## 
An impulse response model is then generated from the DCT using an iterative 
technique (step 415). The linear difference equation noted above may be 
expressed in the z-domain as: 
##EQU4## 
where .delta.(n) is the unit impulse, and na and nb are, respectively, the 
number of poles and zeros of the model. 
Empirical analysis has shown that models having from approximately seven to 
twelve poles and zeroes (e.g., na equals ten and nb equals ten) provide 
good results in testing ECGs. Models having smaller numbers of poles and 
zeroes (e.g., na equals four and nb equals four) may not provide 
sufficient resolution. Similarly, models having larger numbers of poles 
and zeroes (e.g., na equals seventeen and nb equals seventeen) tend to 
incorporate noise and AIQP signals into the model. 
For example, as shown in FIG. 6, three models for a QRS waveform 500 having 
a distinctive notch and slur 505 in the second half of the QRS waveform 
produce significantly different results. A low order model 510 (na=4, 
nb=4) produces a residual signal 515 (the difference between the modelled 
waveform 510 and the original waveform 500) that extracts the notch and 
slur effectively but has a poor overall fit to the whole QRS waveform. 
This results in spurious residual signals, particularly at the onset and 
peak of the QRS waveform. A high order model 520 (na=17, nb=17) accurately 
models both the normal and abnormal components of the QRS, so that the 
residual signal 515 is featureless. An intermediate order model 530 
(na=10, nb=10) represents a compromise between these two extremes. The 
notch and slur are apparent in the residual signal 535 associated with 
this model, along with a small signal error at the beginning of the QRS 
complex. 
If the z-transform variable in the z-domain expression is replaced with the 
unit forward shift operator q (such that q.sup.-1 y(n)=y(n-1), etc.), 
parameters A(q) and B(q) that correspond in the frequency domain to the 
functions A(z) and B(z) may be produced. These parameters may be modelled 
using an autoregressive model with an exogenous input (ARX). The ARX model 
describes a system with an input-output relationship that may be expressed 
as: 
EQU A(z)y(n)=B(z)u(n)+e(n) 
where u(n) is the input (in this case an unit impulse, u(n)=.delta.(n)), 
y(n) is the output (the DCT of the QRS waveform), and e(n) represents a 
non-predictable white noise term. The ARX model is advantageous in that it 
defines a linear regression having parameters that can be estimated 
analytically. The ARX model can be expressed as: 
##EQU5## 
where .theta.=a.sub.1, a.sub.2 . . . a.sub.na, b.sub.0, b.sub.1, . . . 
b.sub.nb !.sup.T is the parameters vector and 
EQU .phi.(n)=-y(n-1) . . . -y(n-na) u(n) u(n-1) . . . u(n-nb)!.sup.T 
is the regression vector. From the DCT of the QRS waveform, y(n), and an 
impulse input, the predicted signal y(n) can be expressed as a linear 
regression: 
EQU y(n)=.phi..sup.T (n).theta. 
Considering signals of N samples (n=1,2, . . . ,N) and a model of order na 
and nb for A(z) and B(z), respectively, the coefficients of these 
polynomials may be estimated using the well-known prediction error method, 
in a way that minimizes the model fitting error (.epsilon.(n)=y(n)-y(n)) 
in the least square sense, with the loss function: 
##EQU6## 
where p is the greater value between na and nb. The data of N samples 
include a pre-windowing of p zeros so that the summation starts at the 
first sample of the signal. The loss function may be written in a matrix 
form as: 
EQU V(.theta.)=(y-.PHI..theta.).sup.t (y-.PHI..theta.) 
where 
EQU y=y(p+1) y(p+2) . . . y(N)! 
and .PHI. is the matrix of regression vectors given by 
##EQU7## 
Minimizing the loss function, the parameters may be estimated using the 
expression: 
EQU .theta.=(.PHI..sup.T .PHI.).sup.-1 .PHI..sup.T y 
assuming that the matrix .PHI..sup.T .PHI. is non-singular. 
Referring again to FIG. 5, once the model of the DCT of the reference QRS 
waveform is produced (step 415), it is converted to the time domain using 
an inverse DCT (IDCT) (step 420). The IDCT of X(k) may be expressed as: 
##EQU8## 
This transformed model may then be subtracted from the representative 
beats for different stages of the stress test to produce the AIQP signals. 
This procedure is illustrated graphically in FIG. 7. 
Another processing approach assumes that AIQP signals change progressively 
over time during the recording period. An example of when this assumption 
may apply is when the AIQP signals are monitored during a period of 
suspected acute myocardial infarction. As an infarction evolves, the AIQP 
signals change due to changes in ventricular conduction patterns caused by 
conduction blockage and scar tissue formation. In general, the AIQP 
signals may be monitored under these conditions by trending values 
recorded in successive representative beats formed from subensembles 
obtained during successive recording periods. This approach may use the 
model of a reference QRS waveform, as described above, or may use a model 
of the QRS waveform for each beat under consideration. 
Referring again to FIG. 2, the AIQP signals may be analyzed by quantifying 
them and by determining their timing. An AIQP waveform, AIQP(t), can be 
quantified by calculating its root-mean-squared ("RMS") QRS complex as: 
##EQU9## 
Where T is the number of time samples in the QRS complex. The timing, 
t.sub.0, of the AIQP signal can be determined tentatively as the time 
value corresponding to the weighted center of the AIQP signal such that: 
##EQU10## 
The RMS and timing values may be determined for each representative beat 
and analyzed and graphed as a trend. FIG. 8 shows such a trend of 
AIQP.sub.rms values during balloon angioplasty. There is a pre-inflation 
control period, followed by 5 minutes of balloon inflation, followed by 
balloon deflation. The AIQP signals have small amplitudes during the 
pre-inflation period. Their amplitudes rise sharply during balloon 
inflation and persist in the immediate aftermath of balloon deflation. ECG 
signals and associated AIQP signals for the angioplasty procedure are 
shown in FIG. 9. AIQP signals were present in twelve of twelve subjects 
analyzed. In contrast, ST segment deviation occurred later into the period 
of balloon inflation and was not always present. Only seven out of twelve 
subjects studied had clinically significant ST segment deviation. 
AIQP timing may be correlated with the arterial location of the balloon. 
FIG. 10 shows the timing of AIQP signals within the QRS complex for the 
left anterior descending ("LAD") artery, left circumflex ("LCX") artery, 
and right coronary artery ("RCA"). As would be expected from anatomy, 
occlusion of these arteries is associated with disruption of ventricular 
conduction in the early, mid and late portions of the QRS complex, 
respectively. 
A variety of techniques may be used to reduce noise in the ECG signal and 
thereby improve the system's ability to detect the AIQP signals. For 
example, referring to FIG. 11, each electrode 110 may be a multi-segment 
electrode 1200 that includes a center segment 1205 and four exterior 
segments 1210a, 1210b, 1210c and 1210d that together surround the center 
segment 1205. The position of the center segment 1205 corresponds to the 
average of the positions of the exterior segments 1210a, 1210b, 1210c and 
1210d. The diameter of the region defined by the exterior segments is on 
the order of 2 and 1/8 inches. 
The multi-segment electrode 1200 is configured for use with a connector 
1215 that is attached to a lead 1220. To this end, the electrode 1200 
includes a connection tail 1225. The connection tail 1225 includes five 
connection holes 1230 for attachment to the connector 1215. Four of the 
connection holes 1230 are arranged in a square configuration, with a fifth 
connection hole being located in the center of the square. Each hole 1230 
passes through an extension 1235, or trace, of a segment of the electrode 
1200. The construction and operation of a connector comparable to the 
connector 1215 is discussed in detail in U.S. application Ser. No. 
08/724,885, entitled "ELECTRODE CONNECTOR" and filed Oct. 3, 1996, which 
is incorporated by reference. 
Referring to FIG. 12, the multi-segment electrode 1200 is formed on a 
basepad 1240. The basepad is made from an insulating, flexible film, such 
as polyester film. The basepad 1240 is shaped to include a section 1245 
corresponding to each segment of the electrode and a section 1250 
corresponding to the connection tail 1225. 
As shown in FIG. 13, the segments 1205, 1210a, 1210b, 1210c and 1210d are 
formed by printing on the surface of the basepad 1240 with a conductive 
material 1255, such as silver-chloride ink. The extensions 1235 are formed 
in the same manner. Next, as shown in FIG. 14, a layer of insulating 
material 1260 is deposited on the silver-chloride ink 1255 that defines 
the extensions 1235. With the exception of portions of the extensions that 
will be adjacent to the holes 1230, the insulating material covers the 
entire surface area of the extensions. 
Referring to FIG. 15, a layer of plastic flexible foam 1265 is attached to 
the section 1245 of the basepad 1240 corresponding to the segments of the 
electrode. The foam is positioned on top of the silver-chloride ink 1255 
and the insulating material 1260 so that the ink and the insulating 
material are sandwiched between the foam and the basepad. The foam 
includes cutout sections 1270 that correspond to the electrode segments 
1205, 1210a, 1210b, 1210c and 1210d. The cutout sections form wells that 
hold electrically conductive gel 1275 (FIG. 16). The gel 1275 provides a 
conductive path from the patient's skin to the silver-chloride ink that 
defines each electrode segment. 
the holes 1230 are formed through the basepad 1240 and the silver-chloride 
ink to produce the electrode 1200 illustrated in FIG. 11. Referring to 
FIG. 16, in storage and prior to use, a cover 1280 is attached to the 
adhesive surface of the foam 1265 to keep the electrode clean prior to use 
and to prevent the conductive gel 1275 from drying. 
The patient's heart may be stressed using a controlled protocol. The 
protocol may consist either of exercise or of pharmaceutical stress 
testing. For example, the patient may be exercised using the treadmill 
145. Alternatives to the treadmill, such as climbing and bicycle 
ergometers, also may be used. In general, the stress protocol will have 
several stages, including control or warm-up stages, stages featuring 
progressively heavier stress, a relaxation stage, and a recording stage 
occurring between fifteen minutes and twenty four hours after the test. 
Recording of ECG signals may take place during any or all of these stages. 
The processor may generate a set of localized ECG signals. As shown in FIG. 
17, the processor may generate the localized ECG signals according to a 
procedure 1500. Initially, the ECG signals are digitized by the signal 
conditioning circuitry 125 (step 1505). Digitizing the signals prior to 
generating the localized ECG signals allows signals from different 
segments to be weighted in a flexible way, depending on their geometry or 
location, and permits an impedance measurement, produced from one or more 
electrodes or electrode segments, to be combined with the ECG signals to 
remove baseline noise. Localized ECG signals also could be generated by 
combining analog signals recorded from individual sensing elements of the 
electrodes or electrode segments prior to digitizing those signals. 
Next, noise introduced by, for example, motion artifact, muscular activity 
or ECG baseline wander, is removed using impedance measures recorded from 
one or more electrodes and other techniques (step 1510). Techniques for 
reducing noise in ECG signals are described in U.S. application Ser. No. 
08/557,883, entitled "USING RELATED SIGNALS TO REDUCE ECG NOISE" and filed 
Nov. 14, 1995, which is incorporated by reference. 
The localized ECG signals then are produced from the noise-reduced ECG 
signals (step 1515). Each localized ECG signal corresponds to an 
approximation of the surface differential signal, also known as the 
Laplacian signal, which is defined as: 
##EQU11## 
where .phi.(x,y) is the body surface potential, .rho..sub.projected is the 
summed charge density of cardiac sources distant from the surface, and 
.epsilon. is the permittivity of the body considered as a volume 
conductor. See R. Plonsey, "Laws governing current flow in the volume 
conductor", in The Theoretical Basis of Electrocardiogaphy, C. V. Nelson 
and D. B. Geselowitz Eds., Clarendon Press, Oxford pp. 165-174, 1976, 
which is incorporated by reference. For a multi-segment electrode having 
four exterior segments, the localized ECG signal (S.sub.L) is produced as: 
EQU S.sub.L =S.sub.C -(S.sub.E1 +S.sub.E2 +S.sub.E3 +S.sub.E4)/4, 
where S.sub.C is the signal produced by the center segment and S.sub.E1, 
S.sub.E2, S.sub.E3 and S.sub.E4 are the signals produced by the exterior 
segments. 
After the localized ECG signals are produced, the signal-to-noise ratios of 
the signals are enhanced (step 1520). Enhancement of the signal-to-noise 
ratio includes estimating a representative localized ECG signal during 
each period of the stress test. A localized ECG signal may be modelled as: 
EQU x(t)=s(t)+n(t) 
where x(t) is the ECG signal, s(t) is the cardiac signal component, and 
n(t) is noise. 
In one approach to enhancing the signal-to-noise ratio, a median beat is 
computed for an ensemble of beats. With reference to the ECG waveform of 
FIG. 3, the beats of the ensemble are time-aligned by determining fiducial 
points of the QRS complex of each beat and aligning the beats relative to 
the fiducial points. For each time increment, corresponding values for the 
ensemble of beats are ranked according to their magnitudes. The median 
beat is then computed by selecting the median value for each time 
increment. A signal-to-noise-ratio-enhanced ECG waveform x'(t) composed of 
median beats produced in this manner is substituted for the ECG waveform 
x(t) in subsequent processing. 
The median beat may be computed using a moving-average procedure in which 
the oldest beat is dropped from the ensemble when a new beat is added. 
This procedure ensures that sudden changes in the ECG waveform resulting 
from, for example, increases in noise do not distort the median beats that 
comprise the noise-reduced waveform. The median beats may be further 
immunized from corruption due to noise by limiting the change in the value 
of a time sample between median beats to a maximum value, such as, for 
example, 10 .mu.V. The median beat computation is useful when the initial 
signal-to-noise ratio is poor and there are only a small number of beats 
available for processing. 
An alternative approach to enhancing the signal-to-noise ratio is to 
perform ensemble averaging of the available beats. For an ensemble of I 
time-aligned beats, the ensemble average, x'(t), is given by: 
##EQU12## 
where the subscript i refers to the beat number in the ensemble (1 to I), 
s(t) is the repetitive component of the cardiac signal, and n'(t) is the 
noise of a typical beat, assuming that the noise is distributed as a 
Gaussian across the ensemble and is uncorrelated with the cardiac signal. 
See P. Lander et al., "Principles and Signal Processing Techniques of the 
High Resolution Electrocardiogram", Prog. Cardiovasc. Dis. 35(3):169-188, 
1992, which is incorporated by reference. The averaging process attenuates 
the noise in a statistically predictable fashion, without affecting the 
cardiac signal component. 
Ensemble averaging produces a good unbiased estimate of the cardiac signal 
component. An enhancement of this approach is to reject beats that are 
adversely noisy from the ensemble average on the basis of signal variance 
measurements. See P. Lander et al., "Principles and Signal Processing 
Techniques of the High Resolution Electrocardiogram", Prog. Cardiovasc. 
Dis. 35(3):169-188, 1992. As also noted in that reference, another 
enhancement is to apply an a posteriori Wiener filter to the ensemble 
average to improve the mean-squared error of the cardiac signal estimate. 
Next, the localized ECG signals are normalized (step 1525). (Normalization 
also could be performed before the signal-to-noise ratios of the ECG 
signals are enhanced.) The normalization step adjusts the scale of the 
localized ECG signal to obtain a uniform measurement of ST segment 
deviation that can be related to myocardial ischemia. Normalization is 
achieved by multiplying the ECG waveform by a normalization, or scaling, 
function. This results in localized ECG signals that are comparable to 
each other, either between different locations on the body surface or 
between different patients. 
In a first method of normalization, the localized ECG signals are scaled by 
the maximum peak-to-peak value of the QRS complex (see FIG. 3) at the 
center segment of the corresponding multi-segment electrode. Other 
suitable scale factors include amplitudes recorded from the QRS or T wave 
or the ECG value anywhere within the ST segment. The scale function also 
may be derived from a bipolar ECG, multipolar ECG, or the Laplacian ECG 
itself. An alternative approach is to scale all localized ECG's from one 
patient with a single value recorded from the set of unipolar, multipolar, 
or Laplacian ECG's recorded from the body surface of that patient. For 
example, this value may be the maximum value of the peak-to-peak amplitude 
of the QRS complex for any electrode segment, or the maximum value of the 
ST segment of the ECG for any electrode segment. 
Other embodiments are within the scope of the following claims.