J. Biomedical Science and Engineering, 2013, 6, 47-60 JBiSE
http://dx.doi.org/10.4236/jbise.2013.612A007 Published Online December 2013 (http://www.scirp.org/journal/jbise/)
Effect of cardiac ventricular mechanical contraction on the
characteristics of the ECG: A simulation study
Ismail Adeniran1, Jules C. Hancox2, Henggui Zhang1,3*
1School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
2Department of Physiology and Cardiovascular Laboratories, School of Medical Sciences, Bristol, United Kingdom
3School of Computer Sciences and Technology, Harbin Institute of Technology, Harbin, China
Email: *henggui.zhang@Manchester.ac.uk
Received 22 October 2013; revised 25 November 2013; accepted 8 December 2013
Copyright © 2013 Ismail Adeniran et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction: The 12-lead electrocardiogram (ECG)
is the most widely-used tool for the detection and di-
agnosis of cardiac conditions including myocardial
infarction and ischemia. It has therefore been a focus
of cardiac modeling. However, the most contempo-
rary in silico ECG investigations of the intact heart
have assumed a static heart and ignored the mecha-
nical contraction that is an essential component of
cardiac function. The aim of this study was to utilize
electromechanically coupled human ventricle models
to explore the consequences of ventricular mechanical
contraction on the ECG profiles. Methods and Re-
sults: Biophysically detailed human ventricular cell
models incorporating contractile activity and a stretch-
activated current (Isac) were incorporated into a 3D
human ventricular model within a human torso, from
which 12-lead ECGs were computed at a stimulation
rate of 1 Hz. Compared to the static model, ventricu-
lar contraction without Isac had little effect on the
QRS complex, but shifted the T-wave peak leftwards
and reduced its peak amplitude. With Isac, ventricular
mechanical contraction increased the QRS duration
by 23% and QT interval by 5%. Conclusion: Me-
chanical contraction of the heart has a significant
effect on the morphology and characteristics of the
ECG particularly on the T-wave. The alteration of
the cell membrane kinetics by stretch via Isac further
exacerbates these effects. Our simulation data suggest
that mechanical contraction should be considered in
the interpretation of ECGs in pathological conditions,
especially those in which mechanical contraction of
the heart is impaired.
Keywords: Ventricles; Mechanical Contraction;
Electrophysiology; Simulation; ECG
The 12-lead electrocardiogram (ECG) is used to under-
stand a person’s cardiac electrical activity by measuring
the electrical potential on the body surface in order to
obtain diagnostic information on the status of the heart.
Common ECG monitoring’ uses include the detection of
complex arrhythmias, shortened or prolonged QT inter-
vals, ST-segment elevation and ischemia monitoring
[1-3]. The 12-lead ECG system consists of six frontal
plane leads (Lead I, Lead II, Lead III, aVR, aVL and
aVF) and six chest leads (V1 - V6) [1-3]. Though being
the most widely-used cardiac diagnostic tool, the ECG
has some shortcomings, e.g., low sensitivity for detecting
acute inferior myocardial infarction (its sensitivity is
only approximately 60%) [4]. Complex ECG patterns
associated with left bundle branch block (LBBB), ventri-
cular-paced rhythm (VPR) and left ventricular hypertro-
phy (LVH) reduce the ability of the ECG to detect acute
coronary ischemic change and acute myocardial infarc-
tion [5].
Biophysically detailed computer models of the heart
that relate cardiac cellular mechanisms to a clinical mea-
surement of ECG may establish a correlation atlas be-
tween ECG characteristics and various pathological con-
ditions. This may be beneficial for mitigating the short-
comings of the use of clinical ECGs for diagnosing car-
diac diseases. However, the majority of cardiac model-
ling studies have focused mainly on the electrical activity
of the heart, with an assumption that the heart is station-
ary in the thorax [6-10]. This assumption ignores the fact
that the heart is a mechanical pump, which undergoes
rhythmic mechanical contractions in response to cardiac
electrical excitation waves. The mechanical contraction
alters the geometry of the heart as well as the cardiac
*Corresponding author.
I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60
electrical excitation via the mechanism of mechano-
electric feedback (MEF) [11-14].
It can be anticipated that the motion of the heart dur-
ing the cardiac cycle alters the relative position of the
ECG leads on the body surface to the electrical signal
sources in the heart, as well as the anisotropic conductiv-
ity of the electrical propagation within the torso; all of
which may have influences on the ECG, therefore, pro-
ducing differences to the ECG characteristics is compar-
ed to a stationary heart, particularly during the T-wave
when the heart is subjected to maximum systolic pressure
[2,15]. In addition, in response to changes in volume load
or contractile state (changing geometry), the heart regu-
lates its electrical activity via MEF [11,16,17], which
activates stretch-activated channels (SACs) [18,19] that
regulate cardiac cell action potentials (APs), such as
prolongation or shortening [13,20,21] of AP duration
(APD), changes of AP morphology and AP refractory
properties (such as the diastolic depolarisation and pre-
mature excitation) [11,22-24]. However, it is incomplete-
ly understood how these changes of cardiac geometry
and electrophysiology influence the body surface ECG.
Therefore, the aim of this study was to investigate the
consequences of ventricular wall motion on the 12-lead
ECG in the absence and presence of a stretch-activated
current (Isac) during the cardiac cycle.
2.1. Single Cell Electromechanical Model
For simulating electrophysiology (EP), we utilized the
O’Hara-Rudy (ORd) human ventricular single cell model
[25], which was developed from undiseased human ven-
tricle data and recapitulates human ventricular cell elec-
trical and membrane channel properties, as well as the
transmural heterogeneity of ventricular action potential
(AP) across the ventricular wall [25]. The ORd model
also reproduces Ca2+ versus voltage-dependent inactiva-
tion of L-type Ca2+ current and Ca2+/calmodulin-de-
pendent protein kinase II (CaMK) modulated rate de-
pendence of Ca2+ cycling [25]. For simulating cellular
mechanical properties, we used the Rice et al. myofila-
ment (MM) model [26]. This model was chosen as it is
based on the cross-bridge cycling model of cardiac mus-
cle contraction and is able to replicate a wide range of
experimental data including steady-state force-sarcomere
length (F-SL), force-calcium and sarcomere length-cal-
cium relationships [26].
The intracellular calcium concentration i
from the EP model was used as the coupling link to the
MM model. i
produced as dynamic output from
the EP model during the time course of the AP served as
input to the MM model from which the amount of Ca2+
bound to troponin was calculated. The formulation of the
myoplasmic Ca2+ concentration in the EP model is:
iCaipCaCabNaCa imyo
nsr ss
updiff Ca
myo myo
 
where Cai
is the buffer factor for i
, IpCa is the
sarcolemmal Ca2+ pump current, ICab is the Ca2+ back-
ground current, INaCa,i is the myoplasmic component of
Na+/Ca2+ exchange current, Acap is capacitive area, F is
the Faraday constant, vmyo is the volume of the myoplas-
mic compartment, vnsr is the volume of the network sar-
coplasmic reticulum compartment, vss is the volume of
the subspace compartment, Jup is the total Ca2+ uptake
flux, via SERCA pump from myoplasm to the network
sarcoplasmic reticulum and Jdiff,Ca is the flux of the diffu-
sion of Ca2+ from the subspace to the myoplasm.
is formulated as:
Ca Ca
Cai mm
where [CMDN] and [TRPN] are the calmodulin and tro-
ponin Ca2+ buffers in the myoplasm respectively, and
Km,CMDN and Km,TRPN are the half-saturation concentra-
tions of calmodulin and troponin respectively.
In the original ORd model, Eq.2 considers Ca2+ bind-
ing to both calmodulin and troponin. However, as the
MM model implements actual regulatory sites for the
apparent Ca2+ binding to troponin, Eq.2 was modified to:
Cai m
Now, the EP model only handles Ca2+ binding to
calmodulin with the MM model handling Ca2+ binding to
troponin. The flux of the binding of Ca2+ to troponin via
the MM model was incorporated into the EP model via
Eq.1 as follows:
iCaipCaCabNaCa imyo
nsr ss
updiff Ca
myo myo
 
 
where JTro p is the flux of Ca2+ binding to troponin. The
combination of all state variables from the EP model
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I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60 49
with the MM model and the substitution of Eq.3 and
Eq.4 for Eqs.1 and 2 yielded a human ventricular myo-
cyte electromechanical cell model.
2.2. Stretch-Activated Current
In accord with previous studies [19,27-31], we incorpo-
rated a stretch-activated current (Isac) into the electrome-
chanics model using the following formulation:
acsac mmsac
IGPVE (5)
where Gsac and Esac are the maximum channel conduc-
tance and reversal potential of the SAC respectively. In
the electromechanics model, Esac was typically set to
6.3 mV and describes the experimentally observed de-
polarising effect of the channel [32,33]. Vm is the mem-
brane potential and Pm is the channel’s open probability
modelled as:
1e e
1/2 are the strain (with an explicit depend-
ence on the sarcomere length) and half-activation strain
respectively, ke = 0.02 [19,29,34] is the activation slope.
The SAC is assumed to be permeable to Na+, K+ and
Ca2+ [19,30,35] in the ratio 1:1:1 with Isac therefore de-
fined as:
acsacNasac Ksac Ca
where Isac,Na, Isa c,K and Isac,Ca are the contributions of Na+,
K+ and Ca2+ to Isac.
2.3. Tissue Mechanics Model
We modelled cardiac tissue mechanics within the theo-
retical framework of nonlinear elasticity [36,37] as an in-
homogeneous, anisotropic, nearly incompressible nonlin-
ear material similar to previous studies [27,38-42]. We
used a two-field variational principle with the deforma-
tion u and the hydrostatic pressure p as the two fields
[37,43,44]. p is utilised as the Lagrange multiplier to
enforce the near incompressibility constraint. Thus, the
total potential energy functional for the mechanics
problem is formulated as:
int ext
upup u 
int up is the internal potential energy or
total strain energy of the body and
ext u is the ex-
ternal potential energy or potential energy of the external
loading of the body. As in previous studies [38,41,42,45],
in the absence of body forces, and assuming that the
body is always in instantaneous equilibrium and no iner-
tia effects, the coordinates of the deformed body satisfies
the steady-state equilibrium equation with near incom-
pressibility enforced.
According to standard variational principles, equilib-
rium is derived by searching for critical points of (Eq.8)
in suitable admissible displacement and pressure spaces
. The corresponding Euler-Lagrange equa-
tions resulting from (Eq.8) lead to solving the problem
Find (u,p) in ˆˆ
such that:
det ˆ
:dd ,
WxId uvx
 
 
det1 d0,qIdu xqp
 
where and
are the admissible variation spaces
for the displacements and the pressures, respectively.
Id u
 is the deformation gradient, v is a test
function and is the material stored energy function
and corresponds to the density of elastic energy locally
stored in the body during the deformation.
With the axes of the geometry aligned to the underly-
ing tissue microstructure [50,51], the second Piola-
Kirchhoff stress tensor S, obtained from the directional
derivative of (Eq.8) in the direction of an arbitrary vir-
tual displacement and which relates a stress to a strain
measure [37,43] and a manipulation of Eq.9 is defined
Active Tension
where W is a strain energy function that defines the con-
stitutive behaviour of the material, E is the Green-La-
grange strain tensor that quantifies the length changes in
a material fibre and angles between fibre pairs in a de-
formed solid, C is the Right-Cauchy green strain tensor,
p is a Lagrange multiplier (referred to as the hydrostatic
pressure in the literature) used to enforce incompressibil-
ity of the cardiac tissue, SActive Tension is a stress tensor in-
corporating active tension from the electromechanics cell
model and enables the reproduction of the three physio-
logical movements of the ventricular wall: longitudinal
shortening, wall thickening and rotational twisting [52-
For the strain energy function W, we used the Guc-
cione constitutive law [59] given by:
WC (12)
2 113223323
41221 1331
 
Following previous work [27,60], ,
10.831 kPaC
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I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60
2, 3, 4
. ij are the compo-
nents of the Green-Lagrange strain tensor.
2.4. Tissue Electrophysiology Model
The monodomain representation [61-63] of cardiac tissue
was used for the electrophysiology model with a modifi-
cation (the incorporation of the Right Cauchy Green de-
formation tensor C), which allows the monodomain
equation to take into account the effect of the deforming
tissue, similar to previous studies [38,42,64]:
DV 
nD V
 
m ion
t 
where Cm is the cell capacitance per unit surface area, V
is the membrane potential, Iion is the sum of all trans-
membrane ionic currents from the electromechanics sin-
gle cell model, Istim is an externally applied stimulus and
D is the diffusion tensor. In simulations, intracellular
conductivities in the fibre, cross-fibre and sheet direc-
tions were set to 3.0, 0.1 and 0.31525 ms·mm1 respec-
tively. These gave a conduction velocity of 65 cm·s1 in
the fibre direction along multiple cells, which is close to
the value 70 cm·s1 observed in the fibre direction in
human myocardium [65].
2.5. Tor so Model
The electrical potential in the torso (Figure 1(B)) is ob-
tained via the Poisson equation:
where DT is the torso conductivity and VT is the electrical
potential in the torso. The torso is modelled as a passive
conductor surrounded by air. Consequently, the normal
component of its outer surface is zero leading to the first
boundary condition:
where n is the outward unit normal on the torso surface,
T is the outer surface of the torso. The torso is a con-
ductor surrounding the ventricles. Therefore, from the
conservation of charge and current, on the boundary be-
tween the ventricles and the torso, the normal component
of the current in the ventricles must equal the normal
component of the current in the surrounding torso. Since
there cannot be a discontinuity in the potentials on the
boundary of two directly connected volume conductors,
i.e., the ventricles and the torso, this condition implies
the following boundary condition:
 (17)
where VVENTRICLES is the electrical potential on the sur-
face between the ventricles and the torso. The torso con-
ductivity was set to 0.3 ms·mm1.
2.6. Computing the 12-Lead ECGs
We computed the ECGs according to the standard defini-
tions [3,66] (Figure 1(B)). The limb leads were calcu-
lated as follows:
where LA, LL and RA refer to the left arm, left leg and
right arm respectively and
X is the potential of the ap-
propriate lead. The augmented limb leads were calcu-
lated as:
Lead Lead
Lead Lead
1Lead Lead
 
The precordial leads, V1 - V6 are located over the left
chest as shown in Figure 1(B). Their potentials are
measured directly from their locations.
2.7. Computational Methods
2.7.1. Ge ometry and Meshes
The 3D simulations were carried out on a DT-MRI re-
constructed anatomical human ventricle geometry (Fig-
ure 1(A)), incorporating anisotropic fibre orientation
(Figure 1(A)), from a healthy 34-year old male. This had
a spatial resolution of 0.2 mm and approximately 24.2
million nodes in total and was segmented into distinct
ENDO (60%), MCELL (30%) and EPI (10%) regions
(Figure 1(A)). The chosen cell proportion in each region
reflects experimental data for cells spanning the left ven-
tricular wall of the human heart [67]. The conditional
activation sites were determined empirically across the
ventricle wall and were validated by reproducing the
activation sequence and QRS complex in the measured
64-channel ECG [68] of that person (Figure 1(A)).
2.7.2. Solving the Electrome chanics Problem
The electromechanics problem consists of two sub-
problems: the electrophysiology problem and the me-
chanics problem. The electrophysiology problem Eq.10
was solved with a Strang splitting method [69] ensuring
that the solution is second-order accurate. It was discre-
tised in time using the Crank-Nicholson method [70],
which is also second-order accurate and discretised in
space with Finite Elements [48,49,70,71]. Iion in Eq.10
epresents the single cell electromechanics model from r
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I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60
Copyright © 2013 SciRes.
Figure 1. Schematic diagram of the 3D electromechanical ventricular system. (A) Left and right ventricular single cell electrome-
chanical models incorporated into a 3D human ventricular geometry with fibre orientations and segmented into distinct right and left
ventricular endocardial, mid-myocardial and epicardial regions resulting in the electrical activation sequence of mechanically con-
tracting 3D human ventricles. (B) Thorax model constructed from CT images with embedded ventricles (top) and electrode place-
ments for 12-lead ECG computation (bottom).
which the active tension input to the tissue mechanics
model for contraction is obtained. The system of ordi-
nary differential equations (ODE) composing Iion was
solved with a combination of the Rush-Larsen scheme
[72] and the CVODE solver [73,74].
a) Solve the electrophysiology problem for tmechanics =
1 ms with C as input and active tension Ta as output
(telectrophysiology = 0.01 ms).
b) Project Ta from the electrophysiology mesh onto the
mechanics mesh.
The mechanics problem Eq.8 was also solved using
the Finite Element Method using the automated scientific
computing library, FEniCS [75]. The resulting nonlinear
system of equations was solved iteratively using the
Newton method to determine the equilibrium configura-
tion of the system. The value of the Right Cauchy Green
Tensor C was then used to update the diffusion coeffi-
cient tensor in (Eq.14). Over a typical finite element
domain, P2 elements [48,49,71] were used to discretize
the displacement variable u, while the pressure variable p
was discretised with P1 elements [48,49,71]. This P2 - P1
mixed finite element has been proven to ensure stability
[75-77] and an optimal convergence rate [71,76,78].
c) Solve the mechanics problem with Ta as input and C
as output.
3.1. Single Cell Electromechanical Simulations
3.1.1. Si m ulations without I n cor porati on o f Isac
We first investigated at the cellular level, how mechani-
cal contraction (via the MEF mechanism) affected car-
diac electrical activity. Without consideration of Isac, the
simulated electrical and mechanical behaviors at a
stimulation frequency of 1 Hz are shown in Figure 2 for
the ENDO, MCELL and EPI cell types (Figure 2). Fig-
ure 2(A) shows the simulated action potentials (APs) for
the three cell types. The computed action potential dura-
tion at 90% repolarization (APD90) was 228 ms for the
EPI cell, 339 ms for the MCELL and 269 ms for the
ENDO cell. Figure 2 also shows the corresponding
The algorithm for solving the full electromechanics
problem is as follows:
1) Determine the initial deformation and obtain the
value of the Right Cauchy Green Tensor C.
2) While time < tend:
I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60
Figure 2. Simulation of ventricular electromechanical
characteristics (without Isac). (A) Action potentials in
the EPI (blue), MCELL (green) and ENDO (red) cell
models. (B) Ca2+ transients in the EPI (blue), MCELL
(green) and ENDO (red) cell models. (C) Sarcomere
length in the EPI (blue), MCELL (green) and ENDO
(red) cell models. (D) Active force in the EPI (blue),
MCELL (green) and ENDO (red) cell models. Values
are normalised to MCELL maximum active force for
each cell type.
(Figure 2(B)), the sarcomere length (SL)
shortening (Figure 2(C)) and the active force (Figure
2(D)). The correlation between the action potential and
the i
agrees with experimental data [15,19,20,
22,23,25,26,79]. Of note is the fact that the i
amplitude is smallest for the ENDO cell (Figure 2(B))
despite it having a greater APD90 than the EPI cell (Fig-
ure 2(A)). This was because the model considered a
greater amount of i
buffered by Ca2+/calmodu-
lin-dependent kinase II (CaMK) in the ENDO cell type
as compared to the EPI cell type [25] as observed in un-
diseased non-failing human ventricles. This observation
was also consistent with the observation from the ORd
electrophysiology model [25]. Consequently, the ampli-
tudes of the SL shortening (Figure 2(C)) and active force
(Figure 2(D)) in the ENDO cell type are the smallest
among the three cell types. The simulated larger
(and hence greater contractility) in the MCELL
compared to the EPI and ENDO cells was also consistent
with experimental data [79].
We further investigated the force-frequency relation-
ship (FFR) of the electromechanics model. The FFR was
obtained by stimulating the single cell at different fre-
quencies for 1000 beats until steady state was reached.
The maximum force developed at each stimulation fre-
quency was recorded and plotted against the stimulation
frequency. Results from the EPI cell model are shown in
Figure 3 (results from the other two cell types were
similar). In the considered frequency range, 0.5 - 3 Hz,
the simulated FFR showed the Bowditch staircase or
Treppe effect [80-82], which matched experimental data
As the normal heart rate is near 1Hz, all subsequent
simulations in this study were carried out at 1 Hz (Figure
3; dashed vertical blue line).
3.1.2. Simulations with Incorporation of Isac
We then investigated how Isac affected the cardiac elec-
trical and mechanical activity at the single cell level.
Figure 4 shows the results from the three cell models,
with consideration of Isac that are permeable to Na+, K+
and Ca2+ with a permeability ratio Na+:K+:Ca2+ = 1:1:1.
Compared to the case in which Isac was absent, Isac pro-
duced an elevation in the resting potential for the EPI,
0 1 2 3
Frequency (Hz)
Normalised Force
Ca i
Figure 3. Plot of steady state normalised ac-
tive force vs. heart rate using the EPI cell
model. Red continuous line represents the
WT electromechanics model while symbols
represent experimental data from non-failing
control preparations of human myocardium.
Experimental data from Mulieri et al. [81].
The blue, vertical dashed line indicates the
FFR at 1 Hz.
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Figure 4. Single cell effects of Isac on the electromechanics model. (Ai-Ci) Action po-
tentials without stretch (black) and with stretch (red) in the EPI, MCELL and ENDO cell
models. (Aii-Cii) Ca2+ transients without stretch (black) and with stretch (red) in the EPI,
MCELL and ENDO cell models. (Aiii-Ciii) Sarcomere length without stretch (black)
and with stretch (red) in the EPI, MCELL and ENDO cell models. (Aiv-Civ) Active
force without stretch (black) and with stretch (red) in the EPI, MCELL and ENDO cell
models. Values are normalised to maximum active force for each cell type with stretch.
MCELL and ENDO cells (the resting potential changed
from 87.5 mV to 85.9 mV) (Figures 4(Ai)-(Ci)). This
is consistent with experimental observations [13,35,83,
84]. Isac also shortened the AP duration (EPI APD90
changed from 228 ms to 223 ms (Figure 4(Ai)), MCELL
APD90 from 339 ms to 333 ms (Figure 4(Bi)) and ENDO
APD90 from 269 ms to 257 ms) (Figure 4(Ci)). This is
also consistent with previous experimental studies which
showed a stretch-related APD shortening [12,13,21,22,84]
and control data from a previous modelling study from
our laboratories on electromechanical consequences of
the Short QT Syndrome [27]. In the model, the most sig-
nificant consequences of inclusion of Isacwere upon
increase in 2
Ca i
would then increase via
Ca i
the activation of the reverse-mode of the Na+-Ca2+ ex-
changer leading to greater contractility as has been dem-
onstrated in several studies [19,27,86-90]. For extensive
coverage of further SAC and mechano-electric feedback
mechanisms, see the study by Youm et al. [19].
3.2. 12-Lead ECG
At the intact tissue level, we used the 3D heart-torso
model to investigate the functional impact of the me-
chanical contraction of the heart on the body surface po-
tential, and therefore upon the characteristics of the
12-lead ECG. In simulations, the 12-lead ECG was ob-
tained by incorporating the single cell electromechanics
model into 3D anatomical ventricular geometry within
the torso (see Figure 1). Three settings were considered:
1) static ventricles, 2) contracting ventricles with no Isac
and 3) contracting ventricles with Isac. The results ob-
tained from settings 2 and 3 were compared with those
from setting 1.
Ca i
and the contractile activity. Isac increased the
amplitude by 60% in the EPI; 23% in the
MCELL and 72% in the ENDO cell model (Figures
4(Aii)-(Cii)), which consequently led to greater SL
shortening (Figures 4(Aiii)-(Ciii)) and greater contrac-
tile force by 42% in the EPI, 3.5% in the MCELL and
119% in the ENDO cells (Figures 4(Aiv)-(Civ)). The
increase of the i
amplitude can be attributed to
the permeability of the SACs to Na+, K+ and Ca2+, the
activation of which brought more Na+ and Ca2+ into the
cell that increased i
and i
. These results
are similar to those observed previously with a different
human ventricular cell model [27]. During stretch, the
Figure 5 shows the time course of simulated 12-lead
ECGs. Compared to the static heart, contraction without
Isac had minimal effect on the QRS complex in all the
leads—its duration was decreased by 1.95%; and the
S-wave component was elevated by 1.86% (Figure 5).
I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60
Electrical only
Electrical + Mechanical
Electrical + Mechanical + Stretch
aVF V3
V1 V2
100 ms
Figure 5. 12-lead ECG recordings from static ventricles without stretch (green), contracting ventricles without stretch (black) and
from contracting ventricles with stretch (red).
Detailed analysis of the active force generated by a left
ventricular (LV) cell during contraction showed non-
notable developed active force during the QRS complex
as indicated by the dashed line in Figure 6. This is in
agreement with clinical observations as during the QRS
complex period of the cardiac cycle, the heart is under-
going isovolumic contraction [2,91,92] with little active
force developed particularly during the QR component.
There was a leftward shift in the peak of the T-wave and
a reduction in its peak amplitude (Tpeak) by 18% (Figure
5); but the QT interval was unchanged. Clinically, the
T-wave is the period of ventricular repolarisation, during
which maximum systolic pressure occurs in a single car-
diac cycle [2,93,94].
Inclusion of Isac had more marked effects on the char-
acteristics of the ECGs, such as the width and amplitude
of the QRS and the QT interval. In simulations, inclusion
of Isac in the contraction heart model reduced the ampli-
tude of the R-wave by 17% as compared to the static
heart model. It also produced a more negative S-wave
(by 59%), a wider QRS duration (by 18%), an elevated
ST segment, a prolonged QT interval (by 5%) and an
increased Tpeak (by 45%). Ta b l e 1 summarizes the ECG
properties for all three scenarios investigated.
Table 1. Effects of contraction with and without Isac on the
Electrical +
Electrical +
Contraction + Stretch
QRS (ms) 97.4 95.5 119.4
QT (ms) 361.1 361.1 380.6
Tpeak – Tend (ms)41.1 41.1 60.6
Tpeak amplitude
(%) 100% 82.4% 144.5%
4.1. Summary of Major Findings
At present, the 12-lead ECG is an invaluable and the
most widely used tool for the detection and diagnosis of
a broad range of cardiac conditions including myocardial
infarction, ischemia, conduction and bundle branch
blocks [2,92]. The widespread use of ECGs is based on
comprehensive understanding of the correlation between
cardiac electrophysiology and characteristics of ECGs.
Changes in cardiac electrophysiology (e.g. changes in
cellular membrane ion channel properties and/or inter-
cellular electrical coupling) due to various cardiac dis-
eases alter cardiac excitation wave propagation, leading
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I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60 55
Electrical only
Electrical + Mechanical
Electrical + Mechanical + Stretch
100 ms
Figure 6. ECG and active force recordings from a left
ventricular cell in static ventricles without stretch
(green), contracting ventricles without stretch (black)
and from contracting ventricles with stretch (red).
to an altered electrical field in and around the heart that
varies with time during the cardiac cycle [2,15,92]. Such
a changed electrical field surrounding the heart is re-
flected by changes in the ECG characteristics. Another
important characteristic of the heart is the muscle con-
traction and relaxation, which also varies with time dur-
ing the cardiac cycle [2,15,92]. In addition, in response
to mechanical events such as stretch and volume load,
the heart regulates its own electrical activity via MEF
[2,18,19]; which includes the activation of SACs [18,19,
95]. So far, how cardiac mechanical contraction influ-
ences ECG characteristics is less well-understood. In the
present study, we have incorporated biophysically de-
tailed coupled electromechanical ventricular cell models
into a 3D-anatomical human ventricle situated within a
human thorax to investigate the effects of mechanical
contraction with and without Isac on the 12-lead ECG.
Our simulations suggest that: 1) contraction without con-
sideration of Isac has no significant effect on the QRS
complex, no effect on the QT interval, but shifts the
T-wave leftwards and reduces Tpeak (Figure 5); 2) con-
traction with consideration of Isac reduces the R-wave
amplitude, widens the QRS complex duration, increases
Tpeak significantly, shifts the T-wave rightwards and in-
creases the QT interval (Figure 5). These findings are
dependent on the degree of stretch and are influenced by
the magnitude of Isac. Several aspects of our findings
merit more detailed discussion.
4.2. Mechanistic Insights
With mechanically contracting ventricles without con-
sideration of Isac, the QRS complex was not affected sig-
nificantly, either in duration (1.95% decrease) or mor-
phology (Figure 5). Contraction during the QRS com-
plex is isovolumic [2,91,92], thus explaining the insig-
nificant effect of contraction on the ECG during this pe-
riod. Contraction of each myocyte in the intact tissue is
not necessarily isometric, however, as each undergoes
different length changes with time. Some myocytes con-
tract isotonically, some isometrically whilst others con-
tract eccentrically [2,91,92,94]. Therefore, even though
the ventricular volume does not change substantially, the
ventricle chamber geometry changes considerably [2,94,
96-98]. Consequently, compared to a static heart, the
position of the cardiac electrical sources, the distance of
the ventricles from the body surface and the varying
anisotropic conductivity due to MEF are altered in a
mechanically contracting heart; hence, there was a 1.95%
decrease in QRS duration without Isac. With considera-
tion of Isac, these differential changes in myocyte lengths
and hence ventricular geometry are exacerbated; hence,
there was a greater and more significant effect of con-
traction with Isac on the QRS complex (Figure 5). By the
Frank-Starling law, with the increased stretch of the
myocardial fibers during diastole by Isac, contractility
would increase [2,94,99].
During the T-wave, ventricular contraction attains a
maximum, after which ventricular pressure declines with
ventricular repolarisation causing a decline in the active
force of the myocytes [2,94,96-98]. The ventricular
pressure during this period is ~30% greater than during
the QRS complex [2,94,96-98]. Therefore, during con-
traction, the aforementioned changes in ventricular ge-
ometry, the distance of the ventricles from the body sur-
face and the varying anisotropic conductivity due to
MEF are altered markedly compared to a static heart.
This results in a marked effect on the T-wave; without
Isac, it is shifted leftwards and the Tpeak is reduced in am-
plitude by 18%. With Isac, the myocardial fibers are
stretched during diastole leading to increased contractil-
ity, which in turn increases systolic pressure [2,94,99]
leading to a greater effect on the T-wave (Figure 5); Tpeak
is increased in amplitude significantly by ~45% and QT
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I. Adeniran et al. / J. Biomedical Science and Engineering 6 (2013) 47-60
interval is increased by ~5%.
4.3. Relevance to Previous Studies
Our simulations suggest that ventricular contraction al-
ters 12-lead ECG morphology. This is consistent with
previous studies [100-103]. In their study, Wei et al. [100]
used MRI time sequences of the motion of a human ven-
tricular geometry to compute 12-lead ECGs by mounting
the geometry in a model of the human body. They found
that ventricular motion reduced Tpeak with minimal
change to the QRS complex. However, they did not con-
sider the incorporation of Isac. Xia et al. [101] also ob-
tained similar results with a human whole heart geometry
mounted in a human torso but their simulations lacked
the use of biophysically detailed electromechanical sin-
gle cell models and the incorporation of Isac. Therefore,
the present work is the first study to investigate the ef-
fects of Isac on the characteristics of simulated body sur-
face 12-lead ECGs using anatomically detailed and me-
chanically contracting ventricles. The findings from the
present study improve our understanding of the effects of
electrical-mechanical coupling on the characteristics of
ECG. This was achieved by 1) using biophysically de-
tailed human ventricular myocyte models [25] coupled
with the Rice et al. myofilament model [26]; employed
with and without Isac and 2) by demonstrating the impor-
tance of ventricular motion on the morphology, proper-
ties and subsequently interpretation of the 12 lead ECG.
4.4. Limitations
In addition to acknowledged limitations of both the ORd
electrophysiology model [25] and the Rice et al. [26]
myofilament model, Isac density was based on prior stud-
ies [19,28-31], due to lack of experimental data on the
Isac from human ventricular myocytes. Additionally, the
simulations here were performed at a single, physiologi-
cally relevant frequency (1 Hz), but rate-dependent dif-
ferences in MEF have not been pursued in this initial
study Experimentally observed effects of cycle length
(restitution curve) [104] have also not been studied.
These rate-dependent phenomena would constitute a val-
uable future line of investigation. It should also be ac-
knowledged that Isac. may not be the only mechanism
responsible for MEF (e.g. [105]) and that due to an ab-
sence of functional data in human myocytes, the elec-
tromechanical model lacks stretch sensitive K+ channels
(e.g. TREK). The model also lacks interactions between
myocytes and fibroblasts, which have been proposed to
contribute to ventricular MEF [106,107]. Whilst it would
be useful for such components to be incorporated into
future models, the advantage of the approach adopted
here is that it has been possible to isolate and attribute
with confidence electrical changes to the incorporation of
Isac. Finally, the use of a ventricular computational fluid
dynamics model to determine pressure boundary condi-
tions would allow a more realistic pressure profile. Al-
though it is important that these potential limitations are
stated, they do not fundamentally influence the principal
conclusions of this study.
With the use of a biophysically detailed electromechani-
cal human ventricular single cell model incorporated into
a thorax-mounted human ventricular geometry, we have
shown that the morphology and properties of the ECG
are dependent on ventricular contraction. We have also
shown that cellular stretch incorporated at the single cell
level as an SAC has a significant influence on the ECG
in a mechanically contracting ventricle.
This work was supported by project grants from Engineering and
Physical Science Research Council UK (EP/J00958X/1; EP/I029826/1),
the British Heart Foundation (FS/08/021), the Natural Science Founda-
tion of China (61179009) and the University of Manchester.
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