Relation between depolarization and repolarization phases in body surface QRST integral map

Abstract

The aim of the study was to assess the relations between QRST and STT, QRS integral maps in three groups: healthy volunteers, patients without arrhythmia and patients with implanted cardioverter-defibrillator (ICD). The obtained results showed that the mean correlation coefficient between STT and QRST integral maps was highest in the group of healthy subjects while the mean correlation coefficient between QRS and QRST integral maps was lowest in the same group of subjects. The relation of depolarization phase with repolarization phase and the influence of depolarization sequence on repolarization sequence is noticeably disrupted in patients with impaired systolic function. In such cases the QRST integral maps seems to reflect depolarization-repolarization disorder rather than only repolarization dispersion.

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Relation between Depolarization and Repolarization
Phases in Body Surface QRST Integral Map

M Fereniec1, M Kania1, G Stix2, T Mroczka1, R Maniewski1

1Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland
2Medical University of Vienna, Department of Cardiology , Vienna, Austria
Abstract
The aim of the study was to assess the relations
between QRST and STT, QRS integral maps in three
groups: healthy volunteers, patients without arrhythmia
and patients with implanted cardioverter-defibrillator
(ICD). The obtained results showed that the mean
correlation coefficient between STT and QRST integral
maps was highest in the group of healthy subjects while
the mean correlation coefficient between QRS and QRST
integral maps was lowest in the same group of subjects.
The relation of depolarization phase with repolarization
phase and the influence of depolarization sequence on
repolarization sequence is noticeably disrupted in
patients with impaired systolic function. In such cases the
QRST integral maps seems to reflect depolarization-
repolarization disorder rather than only repolarization
dispersion.

1. Introduction
The area under the QRST complex at body surface
ECG lead is used to define the ventricular gradient what
was proposed by Wilson in 1934 [1]. It is a measure of
the electrical forces produced by local variations in the
recovery process and do not depend on the course of the
excitatory process over the ventricular muscle in the
normal heart state. As it was shown in Ramanathan work
[2], in the healthy heart the recovery process is dominated
by local repolarization variations due to fast spread of
activation, where the Purkinje system plays key role. That
is why body surface maps of QRST area are used to
assess primary repolarization changes i.e. independent of
the activation sequence. The QRST integral maps are also
used to assess the repolarization inhomogeneity [3-7].
However ischemia, in particular myocardial infarction,
can cause changes in the activation process which might
affect the action potential shape in the activation phase
[8] what can change relation between activation and
recovery processes. In the present study the QRST, STT
and QRS integral maps are analyzed in both healthy and
patients groups.
2. Methods
The group of 82 subjects consisting of 25 healthy
subjects and 57 patients with heart disease (coronary
artery disease mostly): 23 pts without ventricular
arrhythmia and 34 pts threatened by ventricular
arrhythmia with implanted cardioverter – defibrillator
(ICD) was studied (Table 1).
Table 1. General information about studied group.






ICD 34


High Resolution multi-lead ECG system Active Two
(Biosemi) with 64 surface leads was used to record ECG
signals. Active electrodes (with preamplifier placed
beside the AgCl2 sensor) were applied to the anterior and
posterior chest. Lead
SippensGroenewegen [9] was used (Figure 1). The 64
unipolar ECGs were simultaneously recorded for 15
minutes in an unshielded environment. Next, signals were
amplified and digitized with 4096 Hz sampling frequency
and 24 bits amplitude resolution and sent to the computer
via optical fiber.
location proposed by
FRONTBACK
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Figure 1. Lead arrangement around the torso. Standard
ECG leads are marked by squares.
NUMBER
25
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AGE
49±15
52±10
65±12
NORM
NON-ICD
ISSN 0276−6574
439
Computers in Cardiology 2007;34:439−442.

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The raw ECG data were filtered using LP Butterworth
filter limiting frequency to 300 Hz and decimate filter
decreasing sampling frequency to 1024 Hz. Wilson's
central terminal was subtracted as the reference point for
all measured chest potentials. Next, all signals (if heart
rate was varied with the standard error ±2 bpm) were
simultaneously averaged in time using cross-correlation
function. The QRS complex was used as the template for
cross-correlation function calculations. To receive low
noise level both the number of averaged cycles and
correlation coefficient were fitted. The level of noise was
measured on the 20 ms isoelectric U-P interval, before
the begining of the next P wave. PQ interval was not used
as isolelectric line due to atrial repolarization presence,
what was confirmed in work [10]. The number of
averaged cycles and the value of the correlation
coefficient depended on the noise level of the averaged
signal. In the whole studied group the number of
averaged cycles varied from 10 to 150 cycles and the
correlation coefficient from 0.96 to 0.99. The received
noise level was 0.1 to 1.8 たV.
Algorithm applied to detect ECG characteristic points
was based on Singular Value Decomposition (SVD)
method proposed by Acar in [11]. As a result of SVD
decomposition of ECG signals’ matrix M into unitary
matrices U and V and diagonal matrix ぇ:

UM
∑=
The reduced- space data matrix S was obtained by
projecting matrix M down into the reduced space defined
by only the first 3 left - singular vectors of unitary matrix
U. From this three vectors of matrix S (S1, S2, S3) the
Root Mean Square signal was calculated (Figure 2). R –
peak was marked as the maximum of the RMS signal, T
maximum was marked in relation to R peak. The P wave
onset and U wave offset were calculated by inspecting
the stationarity of the time relation between vectors S1
and S2 in respectively defined time windows. Next the
onset of Q wave and the offset of S wave were calculated
on the difference signal of RMS signal using
thresholding.
T
V
(1)

Figure 2. Waves ends detection on RMS signal.
The T wave end was established as the minimum of RMS
signal in time window in relation to T-wave maximum.
Then in each lead individual waves ends were established
in relation to global ends of waves using thresholding (Q
onset, S offset) or extrema searching procedures ( P, R, T,
extrema).
3. Results
The integral maps of QRS, STT and QRST complexes
were obtained by calculating for each lead the algebraic
sum of all instantaneous potentials from the wave onset
to the wave end multiplied by the sampling interval. The
values were plotted on the diagram representing the
thoracic surface and iso-integral maps were drawn.
Below the QRS, STT and QRST integral maps of the
healthy person and patient after myocardial infarction
without implanted ICD are shown (Figure 3).


QRS integral map QRS integral map

STT integral map STT integral map

QRST integral map QRST integral map
A B
Figure 3. The QRS, STT and QRST integral maps for
A – healthy person, B – MI patient without ICD.

In Figure 4 the QRS, STT and QRST integral maps
for two patients after myocardial infarction with
implanted ICD are presented.
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QRS integral map QRS integral map

STT integral map STT integral map

QRST integral map QRST integral map

Figure 4. The QRS, STT and QRST integral maps for
two MI patients with implanted cardioverter-defibrillator.

The correlations between QRST, STT and QRST
integral maps were calculated in three groups: healthy
volunteers, patients after myocardial infarction (MI) and
patients with implanted cardioverter-defibrillator (ICD).
Results are presented in Table 2.

Table 2 STT-QRST and QRS-QRST correlation
coefficient values in studied groups.

STT_QRST CORR QRS_QRST CORR
NORM 0.94±0.06 0.48±0.51
NON-ICD 0.50±0.55 0.54±0.53
ICD 0.38±0.56 0.57±0.58

The statistical significance was studying using Mann-
Whitney test. The significant differences (p<0.01) were
observed in values of STT_QRST between healthy
volunteer group and both patients groups.
4. Discussion and conclusions
In presented study the relations between QRST and
STT, QRS integral maps were examined. In healthy
subjects the correlation coefficients between spatial
distribution of QRST and STT integrals were very high
(mean 0.94) what reveals the dominant influence of
repolarization phase on the QRST area. Similar
conclusion was drawn by Ramanathan in work [2]. They
studied the activation and repolarization processes on the
surface of the heart applying inverse solutions to BSPM
recordings. They have shown that the local repolarization
is the major determinant of the repolarization sequence in
the normal human heart and that activation sequence
slightly influences the repolarization sequence. In
patients without ICD the correlation coefficient between
body surface QRST and STT integral maps was 0.50 and
in group of patients with ICD the correlation coefficient
was 0.38.
These findings might be explained by changes in
ischemic cells’ action potential shape and duration.
Geselowitz in his work has shown that the spatial
variations of the ventricular gradient are related to the
local spatial variation of action potential area and hence
to changes in action potential associated with cardiac
pathologies such as ischemia or infarction [12]. In
ischemic cells an increase in resting potential, a decrease
in peak amplitude, an increase in rise time of the upstroke
as well as change in duration cause the change in the
action potential area what influences the QRST area.
In summary one may state that the distribution of
QRST area on the body surface reveals the local
repolarization distribution for healthy subjects but for
patients with myocardial infarction the QRST area might
be dominated by activation process i.e. QRS area,
especially in patients with implanted cardioverter –
defibrillators.
Acknowledgements
This work was supported by research project financed
by Polish Ministry of Science and Higher Education (No.
3 T11E 005 30) and by Ernst Mach scholarship financed
by the Austrian Ministry of Education, Science and
Culture.
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Address for correspondence:

Małgorzata Fereniec
ul. Ks Trojdena 4, 02-109 Warsaw, Poland
malgosia@ibib.waw.pl
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