Behavior Research Methods & Instrumentation
1982. Vol. 14 (2),281-289
SESSION XV
COMPUTERS IN PSYCHOPHYSIOLOGY:
A SYMPOSIUM
Douglas R. Eddy, Presider
The design, development, and implementation of a
microprocessor-based EeG analysis system
THOMAS M. MURRAY, JR.
Department ofElectrical Engineering, University ofLouisville, Louisville, Kentucky40208
This paper describes the research effort to design, develop, and implement a microprocessorbased ECG data acquisition, reduction, and analysis system for operation in real-time. This
unit can be attached to three-channel ECG carts and provide an immediate on-site analysis.
The basic design and development efforts include (1) the data acquisition, reduction, and matrix
assembly, (2) ECG arrhythmia, P-wave, and axis analysis, (3) ECG contour analysis, and (4) a
real-time three-channel data acquisition unit. Decision tables are used in the analysis. The implementation of these projects into a small microprocessor-based unit coupled to a three-channel
recorder is now in progress. This system, coupled with a small printer, will provide immediate
on-site ECG analysis for most cardiovascular dysfunctions.
For the past several years, computer-based analysis
has been widely applied to electrocardiograms (ECGs).
This analysis has proved beneficial to both the patient
and the physician. For the physician, it has meant less
time spent in hand analysis, more time devoted to
other diagnostic methods and treatment programs, and
more routine monitoring, since the reduced cost factor
and the faster interpretation rate allows for more frequent ECGs. The computer is capable of performing
ECG analysis 24 h/day. The readings are very exact
and always consistent, and repeated readings are constant
(Cornfield, Dunn, Batchlor, & Piperger, 1973).
The precision and accuracy with which the microcomputer can measure and compare ECG waveforms is
now being put to the test. Several efforts have preceded
the present effort to build a microprocessor into an ECG
cart along with all the memory and I/O devices necessary to perform a real-time ECG analysis (e.g., see
Anderson, 1977; Budde, 1977; Cox, 1978; Sanders,
1978). The microprocessor-based ECG cart, because of
its small size, is very portable and, therefore, can be
located anywhere, at a low cost. to provide immediate
on-line analysis.
in which it has been programmed (Rikli, Tolles, Steinberg,
Carberry, Freiman, Abraham, and Caceres, 1961).
Therefore, the ECG waveform must be classified into
clinical groups for pattern recognition. The most prominent characteristics used by the cardiologist are duration
and amplitude. The cardiologist also depends on the
slope to recognize and measure the ECG waveform
components. These three characteristics (amplitude,
duration, and slope) are therefore considered the basis
for the recognition and measurement of the ECG by a
microcomputer (Caceres, Steinberg, Abraham, Carberry,
McBride, Tolles, & Rikli, 1962).
The parameters selected for measurement in our work
are the P, Q, R, S, and T waves and the PR, QT, and RR
intervals. Table 1 lists the ECG parameters, chosen on
the basis of the information needed by an ECG analysis
program utilizing decision tables. These programs
DESIGN PARAMETERS
The advantage of using a microcomputer to perform
ECG interpretation is not only the capability for rapid
measurements and consistency, but also the speed with
which the results can be obtained due to the on-line
operation. However, the microcomputer can only
process the information provided to it in the manner
Copyright 1982 Psychonomic Society, Inc.
281
Table I
ECG Parameters
PA
PO
QA
QD
RA
RD
SA
SO
STO
QRS
TA
PR
QT
RR
P-Wave Amplitude
P-Wave Duration
Q-Wave Amplitude
Q-Wave Duration
R-Wave Amplitude
R-Wave Duration
S-WaveAmplitude
S-Wave Duration
ST Segment Onset
QRS Complex Duration
T-Wave Amplitude
PR Interval Duration
QT Interval Duration
RR Interval Duration
0005.7878/82/020281-09$01.15/0
282
MURRAY
·1
Rp
Tp
Qp
Figure 1. Relationship of parameters to ECG waveform.
provide P-wave, arrhythmia, and axis analysis and
determine ventricular conduction defects, preexcitation,
ventricular enlargement, and A-V conduction disorders.
The data defining the ECG waveform is collected by the
data acquisition program. Figure 1 illustrates the relationship between the parameters and the ECG waveform. The waveform recognition program uses quantitative criteria to automatically recognize and measure
these ECG parameters. A complete ECG requires the
measurement of the electrical activity of the heart using
12 different electrode leads attached to the body surface. These leads are known as I, II, III, aVR, aVL,
aVF, VI, V2, V3, V4, V5, and V6. Once the parameters have been collected for each of these leads, they are
presented in a matrix format to the analysis program.
The 12 different leads are represented by the columns of
the matrix, and the ECG parameters form the rows, as
shown in Figure 2 (Burch & Winsor, 1972).
Preprocessing
The transformation of an analog signal into a form
that can be easily handled by a digital computer creates
a significant problem for on-line interpretation of the
ECG waveform using a microcomputer. The main
problem lies in the fact that the microcomputernormally
has a limited amount of memory space immediately
available for storage of the collected data points. For the
presented application, three analog channels are sampled
simultaneously at sao samples/sec per channel. And to
properly define the ECG waveform, at least 4 sec of data
II
-~-------~--
PA
PO
OA
00
.05
.06
-.04
.02
PR
OT
RR
.13
.30
.75
RA
.72
RD
.07
SA
.00
SO
.00
STO -.01
.09
ORS
TA
.12
III
aVL
aVF
EeG Wavefonn Recognition
There are several reasons why it is very difficult to
program a computer to match the human ability to
recognize an ECG waveform. First, there is a tremendous
amount of variation in data from lead to lead, as well as
from patient to patient. These variations create the
VI
V2
V3
V4
V5
V6
----_.~------
.13
.09
.10 .11
.00 .00
.00
.00
.60 .06
.05 .02
-.09 -.20
.02
.07
-.06 -.06
.07 .09
.10 - .06
.15
.33
.75
aVR
must be taken from each channel (Steinberg, 1967).
.This creates a need for 1,500 words or bytes of memory
per second if every point is stored for all three channels.
Therefore, the minimum requirement for three ECG
leads is almost 6 KB of memory and, to examine all
12 leads, 4 X 6 KB, or 24 KB, of memory.
To reduce these data, a preprocessor program known
as AZTEC (amplitude-zone-time-epoch-coding) is incorporated into the system. The authors of this program
recognized that the ECG is composed of low-frequency
components (P and T waves, ST segments) and mediumfrequency components (QRS complexes). These signal
components normally have amplitudes ranging from low
frequencies (respiration at about .2 Hz) to high frequencies (muscle noise up to about 200 Hz). The
AZTEC program suppresses the low-amplitude signals
to reduce the effect of the undesirable signals (Cox,
Nolle, Fozzand, & Oliver, 1968). A more detailed
description of how the AZTEC program was utilized
can be found in Budde (1977).
The resulting data reduction from this software
preprocessor program is from a rate of 500 samples/sec
to an average of 25 word pairs. This represents a reduction of about 10 to 1. The program interprets highfrequency but low-amplitude noise simply as a line, as
long as the peak-to-peak amplitude does not exceed the
threshold. This method of data compression offers the
advantage of on-line smoothing as well as data reduction.
In other words, the undesirable noise, which is relatively
low in amplitude, is smoothed and the major ECG
components, which are relatively larger in amplitude,
are retained. The data reduction process is shown in
Figure 3. In addition, the line-slope coding permits
rapid searching of the stored data to locate the higher
frequency QRS complexes.
.16
.31
.91
-.06
.05
-.06
.00
.05
.02
-.64
.06
.01
.08
-.12
.12
.34
.68
.04 .03 .04 .05
.10
.06 .06
.05
.00 .00 .00 .00
.02 .00
.00 .00
.52 .00
.11
.06
.06 .00
.03 .01
.00 -.12 -.94 -.94
.00 .02 .07 .08
.05 -.03
.06 .09
.10 .02
.10 .09
.13
.06 .00
.16
.11
.33
.71
.21
.28
.75
.15
.00
.73
.13
.33
.72
.06 .04
.08 .06
.07
.04 .08 .07
.00 .00 -.08
.00
.00 .00
.02 .00
.00
.00 .69 .95
.00 .00
.04 .07
.81 -.79 -.06
.00
.10 .09 .02 .00
.08 -.03
.01 -.02
.09 .08 .07
.10
.07
.09
.06
.09
.15
.31
.75
.12
.29
.73
.14
.36
.73
DURATIONS ARE MEASURED IN SECONDS AND AMPLITUDES ARE MEASURED IN mV
Figure 2. ECG parameter matrix format.
.12
.32
.72
PA
PO
OA
00
RA
RD
SA
SO
STO
ORS
TA
PR
OT
RR
ECG ANALYSIS SYSTEM
,---._-------------'
ECG DATA SAMPLED AT 500 SAMPLES PER SECOND
1\
~~\/\v~ ~/
RESUL TING AZTEC REPRESENTATION
Figure 3. Data reduction.
(X2, Y2)
(n, Y1)
~
Xl
X2 - X"--_ _-I
X2
Figure 4. Normalization of ECG data.
need for a clinical understanding of the ECG waveform
components. Second, electrical noise on the ECG
leads can obscure small waveforms, such as the P wave
(Bonner & Schwetman, 1968a).
Several general approaches address the problem of
computer recognition of ECG waveform components.
First, the desired characteristics to be used in defming
the ECG patterns can be established upon the experience
of trained cardiologists. The cardiologists must develop
a conceptual organization of those steps in their logic
that are subjectively performed by them. Second, the
ECG waveform patterns can be based on purely mathematical techniques (e.g., by performing Fourier series
analysis on the collected data). A third approach, which
combines the advantage of the other two methods, is
a highly desirable technique in ECG waveform recognition. This fmal method of combining clinical criteria
with mathematically extracted parameters is the best
suited technique for computer recognition (Wartek,
Milliken, & Karchman, 1970).
Normalization
The true electrical zero must be determined for each
lead so that the amplitudes of the ECG waveform
283
components may be accurately measured. Establishing
the true electrical zero or baseline of the ECG waveform
has been an area of controversy created by the fact that
there are two regions in the ECG waveform at which the
baseline can be determined. The area between the T
wave and the P wave can be selected as the baseline, or
the point immediately before the QRS onset can be
chosen. The area between the T wave and the P wave
is often unpredictable, since the presence of U waves
or overlapping P and T waves can induce error. The
point immediately before the QRS onset is also imperfect due to the fact that it is affected by the presence
of a little bit of atrial repolarization. However, the
amount of error produced by the second case is small
and predictable, whereas the first case can produce
very unpredictable results. For this reason, the point
immediately before the QRS onset is selected as the
possible baseline value.
The possible baseline values for two consecutive
cycles can then be used to determine the true electrical
zero. If the difference between the baseline values for
two consecutive cycles is greater than .4 mV or 10 converter units, then the first QRS complex is eliminated.
If the difference between the baseline values for two
consecutive cycles is less than .1 mV or 3 converter
units, then the histogram method is used to determine
the baseline. For this method, a histogram is calculated
for the amplitude values in the region between the first
corner point of the first QRS complex and the first
corner point of the second QRS complex, and the most
popular amplitude is chosen as the baseline or true
electrical zero. Otherwise, a straight line must be fit
to the baseline estimates in order to normalize the
data (Figure 4). When the histogram method is used,
the collected data points are normalized to the most
popular amplitude. However, when a straight-line
fit must be used, the normalized value for the ith
data point is represented by the following formula:
Yi = Y, - (Y2 - Y1/X2 -X1XXj -Xl) - YI, where
Y] is the normalized amplitude of the ith data point
(Wartek et aI., 1970).
DATA ACQUISITION SYSTEM
The ECG waveform is an analog signal that reflects
the electrical activity of the heart. In order to allow the
microcomputer to analyze this signal, the signal must be
digitized. This entails sampling the signal and converting
the sampled voltage to a corresponding digital code so
the microcomputer can process the individual sample
points. The data acquisition system is shown in blockdiagram form in Figure 5 (Cox, 1978).
Sampling
According to information theory, a sampling rate
twice the highest frequency component will reproduce
284
MURRAY
INTERRUPT
CONTROLLER
LOW PASS
FILTERS
ECG
uP
CHINE
Figure 5. Block diagram of data acquisition system.
+5
>---'---~
DATA BUS
STATUS
TO DATA BUS
ANALOG REFERENC
Figure 6. Successive approximation circuit.
the original signal provided the sampling is optimally
coded. But if the sampling is not optimally coded, that
is, if the samples are not taken at the peak values of the
signal, then as a result, only the frequency is defined,
and not the amplitude of the signal. Consequently, in
practical systems, the sampling rate is usually made to
give 10 times the highest frequency to assure that the
critical points of the waveform are sampled.
Frequency analysis of the ECG shows that the
practical frequency range may be considered to approximately .4 to 80 cycles/sec, assuming the heart rate is
between 40 and 150 beats/min. Therefore, a sampling
rate of 500 samples/sec is sufficient.
the microcomputer completes the analog-to-digital
conversion. The sample command is used to initiate the
tracking mode of the sample-and-hold units. The sampleand-hold units implemented is the inexpensive analog
device Number AD582. The AD582 features the following: (1) 10-microsec acquisition time to .1%, (2) TTL
control, (3) 3·Y/microsec slew rate, (4) ±.Ol% linearity,
and (5) .05-mY/microsec droop rate.
Analog Multiplexer
An analog multiplexer is needed so that each sampled
value may be passed to the analog-to-digital converter
separately for digital conversion. A CMOS 4016 accomplishes this. The 4016 consist of four analog switches
that can be electrically controlled. After the hold
command has been issued to the sample-and-hold units,
the microcomputer tums on each channel one at a time
until all channels are converted to digital form.
Analog-to-Digital Converter
A successive-approximation analog-to-digital converter
circuit (Figure 6), operates by comparing an unknown
voltage to a series of binary weighted voltages. The
unknown input voltage is first compared with the most
significant bit (MSB). If it is less than the MSB, then it
is turned off. Otherwise, the MSB is left on. The remaining bits are tried in the Same manner until the least
significant bit (LSB) has been tried. Once the process
has been completed, the output register of the processor
contains the binary of the unknown inputs. Successiveapproximation converters are capable of high speeds
and high resolutions. Also, since the conversion process
is independent of the analog input, the conversion time
is constant.
MICROCOMPUTER ECG ANALYSIS APPROACH
Interrupt Controller
A priority interrupt system controls the process of
data acquisition. The highest priority interrupt is assigned
to the stop interrupt, which concludes the data sampling
operation and initiates the waveform 12-lead recognition program. The sample interrupt is assigned the
second highest priority. This interrupt is driven by an
external clock of 500 Hz. When the processor receives
a sample interrupt, it jumps to the control program and
samples three channels of the ECG waveform.
The priority interrupt system is based on the INTEL
8214 (priority interrupt control unit) and an 8-bit
latch (INTEL 8212). When an interrupt is received, a
restart instruction is placed on the data bus, which
causes control to be transferred to the address specified
by n times 0OO8A, where n is the priority of the interrupt encoded by the 8214.
Sample-and-Hold Units
Three sample-and-hold units are needed to sample
three leads of the ECG simultaneously. When the hold
command is issued from the microcomputer, the units
stop tracking the waveform and hold the signal until
Diagnostic procedures and evaluation of ECG patterns
are discussed and described in many textbooks, but it is
obvious that there is a difference between the logic
found in textbooks on ECG diagnosis and that which
can be used in computers. First of all, when a person
examines a record, there are many things he does subconsciously and easily, such as pattern recognition. The
textbook takes these abilities for granted; it is difficult
to tell a computer how to perform reliably the pattern
recognition needed as input to an ECG diagnosis. Many
checks need to be made in order to receive valid data
from the computer. On the other hand, the computer
will examine each beat thoroughly, whereas a person
might skim and miss significant differences (Bonner &
Schwetman, 1968b).
Another consideration in using textbook logic on
ECG diagnosis is that, for a computer, it is incomplete.
When a person performs a diagnosis, there are many
steps in such a manner and order that are not described
by textbooks, if they are mentioned at all. To simulate
these steps in a computer, many arbitrary decisions must
be made. For example, there are rules by which to deter-
ECG ANALYSIS SYSTEM
mine whether two complexes are similar, or whether
intervals are regular. A computer must have exact
instructions on what to do and when to do it. It has to
have an ordering of tests; in a textbook, there are usually
only lists of tests.
Because of this lack of exact tests for purposes of
diagnosing an ECG the Medical Systems Development
Laboratory of the U.S. Public Health Service (1969)
published a bulletin on computer-processed ECG diagnostic criteria.
This bulletin sets up the criteria for just above all
associated heart conditions. As an example, if the
statement "TACHYCARDIA" were printed on an ECG
computer printout, it would be due to the criterion
"rate exceeds 100 on any two leads." The condition
printed out is more severe than that which is suppressed
(i.e., "rate exceeds 100 on anyone lead").
Common Methods of ECG Analysis Programs
There are three common methods to implement the
criteria in a computer analysis of an ECG: a statistical
procedures approach, a logic tree approach, and an
approach utilizing concepts of binary logic.
Only two of the three methods could be used on a
microcomputer such as an 8080 system successfully.
Because programming is accomplished in assembler
language, and high- level math packages are not available,
this rules out any type of statistical approach. The
logic tree approach presents the problem that if a
mistake is made in the first tests, the effect is likely to
be more disastrous than if it had been made near the end
of the tree, when a final decision has almost been made.
Therefore, the program should be written so that the
more reliable tests are performed first. This is a very
natural way to think about programming the relations
between ECG data and ECG diagnostics, but it has a
rather unclear control path and usually produces a very
complicated program (Bonner & Schwetman, 1968a).
The binary logic system may be used to create decision
tables that provide a clear and very compact means for
expressing complex relations between ECG items and
ECG diagnostic categories (Wartek, Milliken, &
Karchman, 1971). This last method was the one chosen
to develop the ECG analysis program on the 8080
microcomputer system.
Decision Tables
Decision table methods are practical and have a tremendous impact on medical diagnosis. In the past years,
decision tables have been used effectively to assist in
commercial and scientific data problem solving. The
applicability of decision tables for medical diagnosis
stems from the fact that the diagnostic process is
primarily logical, rather than computational, and decision
tables are an ideal means for expressing complex logical
relations between symptoms and diseases in a compact
and readily understandable form (Wartek, 1970). How
decision tables were functionally developed for this
285
project and the analysis procedures are described by
Anderson (1977) and Sanders (1978).
The Use of Decision Tables in Medicine
The use of decision tables provides an excellent way
of describing the relationship between symptoms and
diseases. This relationship may be expressed as a sequence
of statements that fit the pattern "if ... and if ... and
if . . . [conditions are true 1, then . . . and ... and ...
[actions that are to be taken]" (Wartek, 1970). For
example, a rule may read if the RR intervals are regular,
and if the QRS complexes are similar, and if the heart
rate is less than 40 beats/min, then idioventricular
rhythm should be diagnosed.
Decision tables can be used as a medical diagnostic
tool in the following manner. Clinical data obtained
from a patient are compared with values listed in the
condition stub of the table. This is then checked with
the rules to make certain that all conditions are valid
for that rule. Having reached the first rule that matches
with the patient data vector, one may ignore any further
columns to the right. At this time, the actions dictated
by the rule that matches are then acted upon. If no
rules in this table are satisfied, then the ELSE rule comes
into action and the diagnostic process is transferred to
another part of the program (Wartek, 1970).
Incorporating Decision Tables Into a
Microcomputer Program
The use of decision tables in a microcomputer involves
translating them into machine-readable code by using a
"bit" (a binary digit). The descriptors are used and
ranked in this order: (1) ventricular regularity, (2) QRS
complexes similar, (3) ventricular rate, (4) presence or
absence of P waves, (5) association of P waves with
QRS, (6) atrial rate, and (7) QRS width.
The following is an example translation of nodal
rhythm into a decision table format. A description of
nodal rhytlun from a textbook might be: The rhythm
is regular with a rate of 40-60 beats/min. The characteristic ECG findings are P waves inverted in Leads II
and aVF and upright P waves in aVR (retrograde)
conduction). The PR interval is short, usually .12 sec
or less. The abnormal P wave is in front of the QRS
complex if the pacemaker is in the upper extremity of
the AV node. The P wave follows the QRS complexes
if the pacemaker is situated in the lower extremity of
the AV node. If located in the center of the node, the
P wave usually is lost in the QRS complex and becomes
invisible (Bernreiter, 1963). This, combined with U.S.
Public Health Service (1969) diagnostic criteria for
computer-processed ECG produces a decision table.
Two cases are created for nodal rhythm because of
the P wave that might or might not exist. This is a good
example of the provisions provided by decision tables.
If decision tables are ascertained for all arrhythmia
cases and then combined into one, many condition
checks would almost be the same. These duplications
286
MURRAY
need to be eliminated to make the decision tables efficient. Some type of compromise needs to be made, or the
conditions need to be rewritten so they are equivalent.
Writing these decision tables for the arrhythmia
analysis is an important step in which the concept of
ECG analysis becomes a manageable and usable object
for the computer.
ARRHYTHMIA DECISIONTABLES
Figures 7, 8, 9 show the decision tables developed
for analysis of ECG arrhythmias on the 8080 system.
These three decision tables are adapted from a FORTRAN
ECG analysis program developed by Wartek et al. (1971).
The number of conditions never exceeds eight in any of
the decision tables, because the 8080 system, with only
8-bit bytes, can only represent eight different conditions. This allows up to 256 rules to be developed from
these conditions in each decision table. However, as
previously stated, it is wise to keep the tables small and
link the tables together to get a more efficient algorithm
for processing the data through the truth tables.
P-WAVE AND AXIS ANALYSIS
The same method that was used in the translation of
the arrhythmia analysis of an ECG into decision tables
is used in the P-wave and axis analysis part of the pro-
gram. Once again, descriptors for the P-wave and axis
analysis are designed to mimic the way in which a
physician analyzes ECGs. The following descriptors are
used: P amplitude, P duration, S3 amplitude, R3 amplitude, Rl amplitude, R2 amplitude, and Sl amplitude.
From these descriptors of the P wave, it can be
determined whether right- or left-atrium enlargement
has occurred. Also, the QRS axis tells whether right or
left deviation has occurred. Figure 10 is the decision
table used in the ECG analysis program.
If a tracing makes it through the decision tables with
only "SINUS RHYTHM" or "NODAL RHYTHM"
printed out, then the ECG analysis program prints out
"ECG MEASUREMENTS WITHIN NORMAL LIMITS."
Otherwise, the program prints out the diagnostic statements from the rules that matched.
ECG CONTOUR ANALYSIS
The analysis system incorporates the updated diagnostic criteria of the American Heart Association
(Flowers, Note 1). Earlier computerized versions of an
ECG analysis system, such as the Caceres system, are
designed for use on large computers with the usual limitations of response time in a batch environment. The
design objective here is to provide a system that is not
dependent on a large computer but is completely dedicated to the analysis and to providing real-time response.
RULE NUMBER
DECISION TABLE FOR
_~TABLE NAME
I
DIAGNOSING ELECTROCARDIOGRAMS -----ARRJ!'L11!MIJ\lA,!3.T I
I 11z1314[E
RR intervals regular
__________________~ __________.,
!
QRS complexes -similar - - - ~ y,y~_ - 'I
ORS dura tions > O. 1Z sec
,!yTy!N ~ i -, I !
ili:jtfl-
u_
-~------------
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HR > lOa/min
p waves found
!!1L<" 40Lmirl __
-----
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- ----- ----- -------------------j ~-,=-f-- I - •-r----+-- t-r- j-.--
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-
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.--._-
---
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----~----------------
Ventricular Tachycardia
Idioventricular Rhythm
Go to Part II Arrhythmia
Go to Part III Arrhythmia
Go to P-wave and Axis analysis
----------
I
--------4
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---
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Figure 7. Decision table
XX
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arrhythmia I.
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EeG ANALYSIS SYSTEM
i TABLE NAME
DECIS ION TABLE FUR
DIAGNOSING ELECTROCARDIOGRAMS
i ARRHYTHMIA
c--,---r-.!\1&~lJtIBEffi=
PART II
,- -~---l:- f--- - 1'- j--~
~:!:"{~!.n II and posi tive P in AVR
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~o P-wave and Axis analysis
~i+
'=~fr=
--t-X
:-l--
1-
I
!ti~
--~-,-~--
-
~i
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X X-~--- - X-
Nodal Rhvthm First Degree AV block
Second Degree AV block
Third Degree AV block
'tt
--B--=+=t=
-- _.
-t+-
----
N-lir--:-~~
-~
- - 'y -
- - - C - -_ _ _ _ _ _
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Y
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I
,
Figure8. Decision table for arrhythmia II.
DECISION TABLE FOR
DIAGNOSING ELECTROCARDIOGRAMS
RR intervals regular
TABLE NAME
I ARRHYTHMIA PART III
nRS como Lexes similar
nRQ o~~n-aqao n ,"
)1,
1
o lIT
II'"
"
,
F
N N~_ l{ __ ~
N y,y
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-r-
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-
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HR < lOa/min
P waveS found
NNLy -.
i
+_ -
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I
I
"
I
i
:
,
i
I
I
Premature Ventricular Contrac r I ons
Ventricular Rhythm
Atrial Fibrillation
Atrial Tachyca rdia with Variable AV Block
Sinus Arrhythmia
Unidentifiable Rhythm
Go to P-wave and Axis Ana l ys is
End of analysis
-
X- -IX - e-,- X -
-
-
I
,
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-. - - ~
- - e- X - - X- XX Xj-
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X
I
X
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,
I
Figure9. Decision table for arrhythmia III.
I
I
I-
i
I
287
288
MURRAY
DECISION TABLE FOR
ITABLE NAME
DIAGNOSING ELECTROCARDIOGRAMS
P-WAVE AND AXIS
P amp > 0.25 mv in 2 limb leads
P dur > 0.14 sec in anv 2 leads or P or pI < 0.05 mv in VI
S3 > R3
R1 > R2 or R3
S1 > R1
R3 > R1 or R2
RULE NUMBER
1 2 3
5 6 7 8 E
y NN
NN YN
N Y Iv N N k>' N N
y N NN
N N Iv
NNY
YN NN
N N N N N r,. Y Y
N N N N N k>' y y
------
~------------
~-
Possible Ripht Atrium Enlornomen.
Possible Left Atrium Enlargement
Left Axis Deviation
~_Axis~v~tion __________
ECG Measurements Within Normal Limits
~"-f_a-",,.J,J~i~_ _ - - - - - - - - --_ .. - - - - - - - - - - - - 1-------------- - - - - - - - - ~--~--------
-- --- ---
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+-
Figure 10. Decision table for P-wave and axis analysis.
Four sets of decision tables are used for contour
analysis: Ventricular conduction defects, preexcitation,
ventricular enlargement, and A-V conduction disorders.
Six decision tables are required for the determination of
ventricular enlargement as defined by U.S. Department
of Health, Education, and Welfare (Note 2). The number
of conditions never exceeds eight in any of the decision
tables, because only eight conditions can be represented
by the g-bit byte of the g080 system.
Recall that the input to the contour analysis program
is the data array output of the ECG wave recognition
program and the arrhythmia, P-wave, and axis information determined by the analysis program. Assume that
the example ECG obtained from a 58-year-old male
has already been processed by the wave recognition
program and the arrhythmia, P-wave, and axis analysis
programs and the data matrix shown in Figure 2 has
been produced.
This information is then passed to the contour analysis
program. The first part of this program deals with
ventricular conduction defects. The set of decision
tables for ventricular conduction defects causes the
message "Intraventricular Block" to be printed out and
then initiates the preexcitation program segment. In
this example, the preexcitation program section cites no
affirmative results (i.e., an absence of W-P-W), and so
it would then proceed to initiate the ventricular enlargement program segment.
At this point, the six ventricular enlargement decision
tables are used. The contour analysis program, using a
subroutine, prepares that patient data vector (PDV)
according to the ventricular enlargement Part 1 decision
table.
The first step is to search the information for W-POW
and right bundle branch block. There is no indication
of either defect. The next condition deals with the
age of the patient. In this example, the patient is older
than 40 years of age. The fourth condition compares the
duration of the R wave with 40 msec. From the matrix,
it is found that the duration (RD) is not greater than or
equal to 40 msec. The next condition requires that the
QRS deviates toward the right. The last condition
involves a simple computation. The ratio (R amplitude
of Lead VI/S amplitude of Lead VI) is computed and
compared to 1.0. In this situation (.11/-.94) < 1.0.
This PDV is compared with ventricular enlargement
table Part 1.
Five more PDVs are created and checked against the
five remaining tables. When Table VI is reached, the
program calls the A-V conduction section of the contour analysis program into execution until all four sets of
decision tables have been examined. If there is an abnormal problem, it is printed out (e.g., ''PROBABILITYRIGHT VENTRICULAR ENLARGEMENT"). When the
program is finished, the words "INTERPRETAnON TO
BE CHECKED BY CARDIOLOGIST" is printed.
ECG ANALYSIS SYSTEM
CONCLUSION
This paper describes the research effort to develop a
microprocessor-based unit and programs to provide
arrhythmia P-wave and axis analysis and contour analysis
of a standard patient ECG. The contour analysis program will determine ventricular conduction defects,
preexcitation, ventricular enlargement, and A-V conduction disorders. Decision tables are used to establish a
set of decision rules that can be logically linked together
for ECG analysis. These tables provide the means to
implement minimum-size microcomputer software programs. The microcomputer, with these programs and the
data acquisition and compressions programs, can be
attached to a standard three-channel ECG cart and
provide immediate on-site ECG interpretation.
REFERENCE NOTES
1. Flowers, N. Personal communication, 1977.
2. U.S. Department of Health, Education, and Welfare. Diagnosticstatements and criteria for ECAN evaluation study (Working document). Washington, D.C: Author, 1977.
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