# MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through
electrophysiological simulations

Karli Gillette  Gottfried Schatz Research Center: Division of Medical Physics
and Biophysics, Medical University of Graz, Graz, Austria  Matthias A.F. Gsell
Gottfried Schatz Research Center: Division of Medical Physics and Biophysics,
Medical University of Graz, Graz, Austria  Claudia Nagel  Institute of
Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe,
Germany  Jule Bender  Institute of Biomedical Engineering, Karlsruhe Institute
of Technology (KIT), Karlsruhe, Germany  Bejamin Winkler  Physikalisch-
Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
Steven E. Williams  King’s College London, London, United Kingdom  University
of Edinburgh, Edinburgh, United Kingdom  Markus Bär  Physikalisch-Technische
Bundesanstalt, National Metrology Institute, Berlin, Germany  Tobias Schäffter
Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin,
Germany  King’s College London, London, United Kingdom  Biomedical
Engineering, Technische Universität Berlin, Einstein Centre Digital Future
Olaf Dössel  Institute of Biomedical Engineering, Karlsruhe Institute of
Technology (KIT), Karlsruhe, Germany  Gernot Plank  Gottfried Schatz Research
Center: Division of Medical Physics and Biophysics, Medical University of
Graz, Graz, Austria  corresponding authors: Axel Loewe
(publications@ibt.kit.edu), Gernot Plank<EMAIL_ADDRESS> Axel
Loewe  Institute of Biomedical Engineering, Karlsruhe Institute of Technology
(KIT), Karlsruhe, Germany  corresponding authors: Axel Loewe
(publications@ibt.kit.edu), Gernot Plank<EMAIL_ADDRESS>
###### Abstract

Mechanistic cardiac electrophysiology models allow for personalized
simulations of the electrical activity in the heart and the ensuing
electrocardiogram (ECG) on the body surface. As such, synthetic signals
possess known ground truth labels of the underlying disease and can be
employed for validation of machine learning ECG analysis tools in addition to
clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical
data or even replace them completely during training leading to improved
performance on real-world clinical test data.

We thus generated a novel synthetic database comprising a total of 16,900 12
lead ECGs based on electrophysiological simulations equally distributed into
healthy control and 7 pathology classes. The pathological case of myocardial
infraction had 6 sub-classes. A comparison of extracted features between the
virtual cohort and a publicly available clinical ECG database demonstrated
that the synthetic signals represent clinical ECGs for healthy and
pathological subpopulations with high fidelity. The ECG database is split into
training, validation, and test folds for development and objective assessment
of novel machine learning algorithms.

## Background & Summary

The 12 lead ECG is a standard non-invasive clinical tool for the diagnosis and
long-term monitoring of cardiovascular disease. To support cardiac disease
classification and interpretation of 12 lead ECGs in clinical practice,
algorithms based on machine learning are increasingly utilized. Training of
these algorithms requires large databases of 12 lead ECGs that have been
labeled according to desired disease classifications with high accuracy and
represent the target population. The most extensive publicly available
database for such purpose to date is PTB-XL [1].

Clinical 12 lead ECG databases like PTB-XL, however, have several limitations
reducing efficacy of machine learning algorithms [2]. As the databases are
typically attained from multiple medical centers, different filtering levels
may be applied to reduce noise. Labeling uncertainties may arise due to
differences in expertise or judgment between clinicians. Patient enrollment
can also lead to both gender bias [3] and uneven representation of certain
cardiac diseases [4]. Furthermore, such databases provide limited insight into
the underlying mechanisms of cardiovascular disease. Databases of synthetic
ECGs have the potential to either complement and enrich [5, 6], or in the long
run to even replace [7], clinical datasets to overcome such limitations.

Currently, no sizeable and open synthetic ECG databases are available due to
the high computational cost and limitations in modeling complete four-chamber
cardiac electrophysiology _in silico_ at scale. While four-chamber cohorts
exist for the modeling of cardiac electrophysiology [8], such cohorts are not
suited for the generation of large ECG databases due to a lack of controllable
electrophysiology or limited anatomical variation. Separate models of atrial
and ventricular electrophysiology that are individually more detailed and
steerable can be later joined together to capture the P wave and the QRST
complex within the 12 lead ECG, respectively, and overcome such limitations.

We thus aimed to assemble the first public database of labeled synthetic 12
lead ECGs by joining two independent multi-scale models of atrial and
ventricular electrophysiology used to compute P waves and QRS complexes,
respectively. This approach provides a complete chain of traceability from the
anatomical and electrophysiological input parameters of the model to the final
12 lead ECGs. Common diseases were modeled mechanistically in addition to
normal healthy control within the synthetic database. Within the ventricular-
torso model, the pathologies of myocardial infarction (MI) and complete bundle
branch block of both the left ventricle (LBBB) and the right ventricle (RBBB)
were modeled. The MI class comprised 6 sub-classes pertaining to the three
predominant arteries of right-anterior descending (RAD), left anterior
descending (LAD), and left circumflex (LCX) [9] each with two different
transmural extent. The diseases fibrotic atrial cardiomyopathy (FAM), complete
interatrial conduction block (IAB) and left atrial enlargement (LAE) were
modeled within the atria. Also, 1st degree AV block (AVB) was modeled as an
atrio-ventricular (AV) conduction-based disease. In this way, the chosen
pathologies cover a wide range of both atrial and ventricular diseases
representing conduction disturbances as well as structural remodeling for
which established modeling approaches published in previous work could be
resorted to. A total of 16,900 synthetic ECGs equally distributed into the 8
groups (healthy control and 7 cardiac pathologies) were made publicly
available in the MedalCare-XL database. This MedalCare-XL dataset is publicly
available under the Creative Commons Attribution 4.0 International license
[10]. Thus, we provide a large and balanced ECG dataset with precisely known
ground truth labels of the underlying pathology as derived from the
mechanistic multi-scale simulations.

Validation of the synthetic ECG database was performed using two approaches to
analyze to what extent the synthetic ECG database could represent clinical ECG
databases. First, we tested the MedalCare-XL data set of simulated ECGs by
comparing the statistical distribution of crucial ECG features extracted from
MedalCare-XL with the same features taken from the clinical PTB-XL data base
for normal healthy ECGs and for different pathology classes. The comparison
showed excellent qualitative agreement, while still exhibiting quantitative
differences that provide a starting point for future improvement of the
underlying models as well as of the quality of future simulation data bases.
Second, two clinical Turing tests were also conducted to evaluate the ability
of the generated synthetic ECG signals to represent clinical signals
undergoing ECG diagnostics by cardiologists. The first test required trained
cardiologists to determine the origin of both measured and simulated 12 lead
ECGs under normal healthy control. The second test additionally involved
pathology classification. Both tests were performed on a subset of 50
synthetics ECG signals extracted from the database and mixed with 50 clinical
signals taken from PTB-XL [1]. Altogether, the MedalCare-XL data base provides
the first example for a large-scale data set of physiologically-realsitic
simulated ECGs.

## Methods

We separate the genesis of the 12 lead ECG into P waves and the QRST complex,
modeled by two separate atrial and ventricle-torso models. Generation of the
anatomical model cohorts and the simulation of electrophysiology to mimic a
large patient population is described for both the atrial and ventricular
models. Having run single beat simulations for P waves and QRST complexes
separately in the two independent models, both signal parts had to be merged
in a post-processing step to obtain an ECG of a full heart cycle comprising
one P wave, one QRS complex and one T wave. Subsequently, the single heartbeat
was repeated with varying RR intervals to account for heart rate variability
(HRV) to obtain a time series signal of 10 $\mathrm{s}$ length. A visual
overview of the pipeline for generating the synthetic 12 lead ECG database is
visualized in Figure 1.

### Anatomical Model Populations

#### Ventricles

A cohort of anatomically-specific ventricular-torso models was generated for
13 healthy subjects (8 M, 5 F) ranging from 30 to 65 years of age. All
subjects were part of a clinical study approved by ethical review board at the
Medical University of Graz (EKNr: 24–126 ex 11/12). Written and informed
consent for each subject was attained at the time of the study. Two separate
MRI scans of the full torso and whole heart were sequentially acquired using
standardized protocols at 3T (Magnetom Skyra, Siemens Healthcare, Erlangen,
Germany). The torso MRI (1.3 x 1.3 x 3.0 ${\mathrm{mm}}^{3}$) was acquired in
four overlapping stacks using a non-ECG gated 3D T1-weighted gradient-echo
sequence. The whole heart MRI (0.7 x 0.7 x 0.7 ${\mathrm{mm}}^{3}$) was
acquired using an ECG-gated, fat-saturated, T2-prepared, isotropic 3D
gradient-echo sequence. Respiratory navigators were employed to gate the MR-
acquisition under free-breathing to end-expiration. MRI-compatible electrodes
for recording the 12 lead ECG of each subject were left intact during image
acquisition. Intensity thresholding techniques implemented in _Seg $3D$_ [11]
were used to segment each torso MRI into heart, lungs, and general torso
tissue. Segmentation of the cardiac MRI was automatically performed using a
two-kernel convolutional neural network. The network was tailored for MRIs
from the original network implemented for computed tomography images [12].
Segmented structures included blood pools, ventricles, and general atrial
tissue. To automatically register the four-chamber heart segmentation into the
torso, an iterative closest point algorithm was utilized in _Seg $3D$_ [11,
13]. Anatomical meshes were generated automatically from the joint
segmentations using the Tarantula software meshing package [14]. Target
resolutions within the cardiac and torso surfaces of $1.2\text{\,}\mathrm{mm}$
and $4.0\text{\,}\mathrm{mm}$ were prescribed, respectively. All models within
the cohort were equipped with universal ventricular coordinates (UVCs) to
allow for automated manipulation of all geometric-based entities [15, 16]. The
entire framework for the generation of the ventricular-torso model cohort is
described in detail in Gillette et al.[15]. The ventricular-torso model cohort
comprising geometries $\Gamma_{V,i},i\in[1,13]$ is visualized in Figure 2.

#### Atria

An overview of the anatomical model cohort generated for the atrial
simulations is shown in Figure 3. A total of 125 anatomical models
$\Gamma_{A,h,i},i\in[1,80]$ and $\Gamma_{A,LAE,i},i\in[1,45]$ of the atrial
endocardium were derived from a bi-atrial statistical shape model [17, 18].
The endocardial surfaces were augmented with a homogeneous wall thickness of
$3\text{\,}\mathrm{mm}$, rule-based myocardial fiber orientation, tags for
anatomical structures and interatrial connections as described by Azzolin et
al.[19, 20]. Out of these 125 geometries, 80 models exhibited left and right
atrial volumes in physiological ranges reported for healthy subjects [21]. In
these geometries, 10 different fractions from 0 to 45 % of the atrial
myocardial tissue volume were additionally replaced by fibrotic patches as
described previously [22] to model atrial cardiomyopathy. The remaining 45
anatomical models were generated by constraining the coefficients of the
statistical shape model such that left atrial volumes were increased to value
ranges typically observed in left atrial enlargement patients [21].
Additionally, 25 torso geometries $\Gamma_{T,i},i\in[1,25]$ were obtained by
modifying the coefficients of the two leading eigenmodes in the human body
statistical shape model constructed by Pishchulin et al. [23]. In this way,
height, weight and gender differences were represented in the anatomical torso
model cohort. By applying random rotation angles
$\alpha_{x},\alpha_{y},\alpha_{z}$ and translation parameters
$t_{x},t_{y},t_{z}$ in ranges summarized in Table 4 to the atrial geometry,
heart location and orientation variability were additionally accounted for in
the virtual population.

### Simulation Protocol and Parameters

#### Ventricles

Under normal healthy control, activation of the ventricles was assumed to be
Durrer-based [24], where the His-Purkinje System was modeled assuming 5
fascicular sites of earliest breakthrough on a fast-conducting endocardium.
Three fascicular sites were placed in the left ventricle (LV) on the anterior
endocardium $\vec{x}_{lv,ant}$, posterior endocardium $\vec{x}_{lv,post}$, and
the septum $\vec{x}_{lv,sept}$. Activation of the right ventricle (RV) was
controlled using a site corresponding to the moderator band
$\vec{x}_{rv,mod}$. An additional site $\vec{x}_{rv,sept}$ was also placed on
the right-ventricular septum. All fascicular sites were defined in UVCs. The
RV moderator band was placed in the middle of the RV free wall. The transmural
depth of the remaining fascicular sites was assumed to be constant at $20\,\%$
of the ventricular free wall. The fascicles were assumed to be of disc-like
shape with a transmural thickness of $0.5\,\%$ of the ventricular wall, and a
radius controlled through additional parameter $\vec{r}$ that related to
endocardial extent. Activation was assumed to be simultaneous, apart from a
prescribed delay $\vec{t}_{mod}$ in the activation of the RV moderator band
site.

To modulate the fast spread of conduction on the endocardial surface of the
ventricles modulated by the His-Purkinje System, a fast-conducting endocardium
was also included that spanned from the middle $10\,\%$ to $90\,\%$ of the
ventricular mesh along the apico-basal direction. Details of the His-Purkinje
representation are available in Gillette et al. [15]. An isotropic conduction
velocity of $2.0\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ was prescribed
within the fast-conducting endocardium [25].

Myocardial fiber directions were applied using a rule-based method [26] that
assumed principal fiber directions rotate radially from $60.0^{\circ}$ on the
endocardium to the epicardium $-60.0^{\circ}$ [27]. Corresponding sheet fiber
directions of $-65.0^{\circ}$ and $25.0^{\circ}$ were applied, respectively
[27]. Conduction velocity along the principal direction of myocardial fibers
of $0.6\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ was applied with an off-
axis conduction velocity ratio of 4:2:1 [28]. Conductivity within the
myocardium was set according to Roberts et al. [29]. All remaining
conductivities within the volume conductor containing lungs, blood pools,
atria, and general torso tissue were set according to Keller et al. [30].

Ventricular myocyte electrophysiology was modeled using the Mitchell-Schaeffer
ionic model $\vec{i}_{sinus}$ [31]. A resting membrane voltage of
$-86.2\text{\,}\mathrm{mV}$ and a peak action potential voltage of
$40\text{\,}\mathrm{mV}$ was assumed. Gradients in action potential duration
(APD) within the myocardium, needed to establish physiological T waves, were
generated by utilizing a known relationship between the $\tau_{close}$
parameter and APDs. A linear combination of the UVCs weighted with given
weights $\vec{q}_{w}$ was first computed at each node of the mesh. The
weighted UVC gradients were mapped into a range between $APD_{min}$ and
$APD_{max}$ to generate an APD map within the entirety of the ventricles.
Values for the gradients and the APD are derived from the literature [32, 33,
34]. In total, variation in electrophysiology during normal healthy control
was controlled through 20 variable parameters summarized in the parameter
vector $\vec{\omega}_{qrs}$ for the QRS complex:

$\displaystyle\vec{\omega}_{qrs}=\\{\ \vec{x}_{lv,ant},\ \vec{x}_{lv,post},\
\vec{x}_{lv,sept},\ \vec{x}_{rv,mod},\ \vec{x}_{rv,sept},\ \vec{t}_{mod}\ \\}$
(1)

and $\vec{\omega}_{t}$ for the T wave:

$\displaystyle\vec{\omega}_{t}=\\{\ \vec{i}_{sinus},\ APD_{min},\ APD_{max},\
\vec{q}_{w}\ \\}.$ (2)

All geometric-based parameters could be mapped into the mesh using $k$D-trees
implemented in _meshtool_ [35]. Parameters relating to both the QRS complex
and T wave under normal healthy control were varied in physiological ranges to
generate variation in the QRST complex as reported in Table 1 and Table
Figures & Tables, respectively. Sampling through the ranges for each of the
parameters was done using Latin Hyper Cubes.

The two pathologies of BBB and MI were then modeled in the ventricles
alongside normal healthy control. Pathologies of LBBB and RBBB were included
in the ventricular-torso model. To cause a complete branch block, all
fascicular root sites within either the LV or the RV were neglected to inhibit
activation. All other relevant electrophysiology parameters were allowed to
vary in the same ranges as reported for normal healthy control above.

A MI stemming from occlusion of one of the three primary arteries of RAD, LAD,
and LCX was inserted into the ventricles. For each of the arteries
$\nu\in\\{RAD,LAD,LCX\\}$, a core center $\vec{x}_{\nu,mi}$ was defined using
the apico-basal and rotational UVC coordinate values that were bounded
according to recommendations of affected regions on the clinical 17-segment
model determined by the American Heart Association (AHA) [9]. Namely, the LAD
was restricted to the anterior-anteroseptal region spanning the entire apico-
basal extent. Both the RAD and LCX extended less apically, and were confined
to the lateral wall and the inferior-inferioseptal regions, respectively. For
each artery, the infarct was either assumed to span the entirety of the
ventricular wall or transmural extent of $30\%$ from the endocardium, giving
rise to a transmural extent value $\rho_{n,mi}$ such that $n\in\\{0.3,1.0\\}$.
The outer $5\,\%$ of the infarct area was allocated to be border zone (BZ),
and the remaining area was defined as the infarct core. All scars were assumed
to be left-sided, thus presenting only in LV.

From each infarct center, an Eikonal activation map was computed within the
ventricular geometry assuming the same conduction velocity and off-axis ratios
as assigned in the general myocardium during normal healthy control. An
infarct geometry was taken by thresholding the activation map according to the
computed time that generated a radius of distance $d_{co}$. The infarct core
was assumed to be electrically inert, while the conduction velocity in the BZ
was set to $0.15\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ with an off-
axis ratio of 1.0 [36]. The conductivity within the BZ was set to the same
values reported for the healthy myocardium. Parameters for the Mitchell-
Schaeffer ionic model within the BZ $\vec{i}_{bz}$ were manually adjusted
using bench leading to characteristic action potential changes during MI [37].
In total, the MI class comprised 6 sub-classes. The parameters varied to
induce various degrees and positions of MI $\vec{\omega}_{\nu,mi}$ included:

$\displaystyle\vec{\omega}_{mi}=\\{\ \vec{x}_{\nu,mi},\rho_{n,mi}\ d_{co}\
\\}:\ \nu\in\\{RAD,LAD,LCX\\},\ n\in\\{0.3,1.0\\}$ (3)

Parameters were similarly varied using Latin Hyper Cubes through ranges based
on clinical observation for characteristic occlusion sites and action
potential changes (Table 3).

Transmembrane voltages were simulated using the efficient reaction-Eikonal
method in the monodomain formulation without diffusion [38]. Electrical
potentials of each electrode on the torso surface were recovered from
transmembrane voltages using lead fields precomputed once for every model
[39]. A ventricular 12 lead ECG (QRST complex) was generated by simulating a
ventricular beat for $450\text{\,}\mathrm{ms}$. All simulations were run using
the _CARPentry_ cardiac solver [40] and the _openCARP_ simulation framework
[41, 42] on a desktop machine with 24 cores, parallelized into 3 threads.

#### Atria

Local activation times in the atria were obtained by solving the Eikonal
equation with the Fast Iterative Method [43] and the Fast Marching Method
[44]. Excitation was initiated at the sinoatrial node with an exit site
located at the junction of crista terminalis and the superior vena cava.
Locally heterogeneous conduction velocity $\mathrm{CV_{[Region]}}$ and
anisotropy ratios $\mathrm{AR_{[Region]}}$ for
$\mathrm{[Region]}\in\mathrm{\\{bulk\leavevmode\nobreak\
tissue,interatrial\leavevmode\nobreak\ connections,crista\leavevmode\nobreak\
terminalis,pectinate\leavevmode\nobreak\ muscles,inferior\leavevmode\nobreak\
isthmus\\}}$ were modeled as summarized in Table 4. The spatio-temporal
distributions of transmembrane voltages $\mathrm{TMV}(t,x)$ were subsequently
derived from the computed activation times by shifting pre-computed
Courtemanche et al. action potential templates $\mathrm{TMV}(t)$ in time.
Remodeling of cellular electrophysiology was applied in fibrotic regions as
described below. For all simulations except for those of fibrotic atrial
cardiomyopathy, the baseline parameters of the Courtemanche et al. model
remained unchanged in all atrial regions. The atria were placed inside a torso
geometry and were rotated ($\alpha_{x},\alpha_{y},\alpha_{z}$) and translated
($t_{x},t_{y},t_{z}$) around and along all three coordinate axes to account
for additional anatomical variability in the cohort. The forward problem of
electrocardiography was solved with the infinite volume conductor method (for
the normal healthy control cases and fibrotic atrial cardiomyopathy) or the
boundary element method (for interatrial conduction block and left atrial
enlargement). Single beat 12 lead ECGs of the P wave lasting
$150\text{\,}\mathrm{-}$$200\text{\,}\mathrm{ms}$ were subsequently extracted
at standard electrode positions. In total, variation during healthy sinus
rhythm simulations was controlled through the parameters summarized in the
following vector

$\omega_{P}=\\{\vec{\mathrm{CV}}_{[Region]},\alpha_{x},\alpha_{y},\alpha_{z},t_{x},t_{y},t_{z},\vec{\lambda}_{T,i},\vec{\lambda}_{A,i},\\}.$
(4)

For simulations of fibrotic atrial cardiomyopathy, nine different fractions
from 5 % to 45 % of the healthy atrial myocardial volume were replaced by
fibrotic tissue as described in detail by Nagel et al. [22] in the same 80
atrial anatomical models that were employed for the healthy control
simulations. In fibrotic patches, 50 % of the cells were modeled as passive
conduction barriers by removing the affected elements from the volumetric
meshes. In the remaining 50 % of the fibrotic cells, conduction velocity was
reduced by a factor of 0.2 and 0.5 compared to the healthy baseline values in
Table 4 in transversal and longitudinal fiber direction, respectively. In this
way, anisotropy ratios were increased by a factor of 2.5, which typically
facilitates functional reentry in patients with atrial fibrillation. To
account for paracrine cytokine remodeling effects in fibrotic regions, maximum
ionic conductances of the Courtemanche et al. cell model were rescaled
(0.6$\times g_{Na}$, 0.5$\times g_{K1}$, 0.5$\times g_{CaL}$).

For left atrial enlargement simulations, 45 additional atrial geometries were
derived from the bi-atrial statistical shape model. Constraints were applied
to the coefficients of the leading eigenmodes to generate anatomical atrial
models with systematically increasing left atrial volumes [6]. Different
rotation angle combinations and conduction velocity variations were applied
for the simulations as reported in Table 4.

Complete interatrial conduction block was modeled by inhibiting conduction
propagation through the elements in Bachmann’s bundle at the junction between
the left and the right atrium in the same 80 bi-atrial geometries that were
used for the control simulations. Different combinations of rotation angles
and spatial translations of the atria within the torso were applied for the
ECG calculations.

### Synthesization of Complete ECGs

Signal components were synthesized to a full ECG using a heart rate
variability (HRV) model to obtain 10 s recordings in accordance with the
standard clinical 12 lead ECG. As atrial and ventricular ECGs were carried out
using different forward calculation methods, the amplitudes of P waves and
QRST complexes needed to be scaled prior to concatenation to ensure that
signal amplitudes of single waveforms are consistent within one heartbeat.
Thus, maximum P wave and R peak amplitudes were extracted in lead II of all
clinical recordings from healthy subjects in PTB-XL [1] using ECGdeli [45].
Based on these values, a multi-variate normal distribution was set up
representing the relation between P wave and R peak amplitudes in clinical
ECGs. In this way, the simulated QRST complex could be scaled with a factor
sampled from this multi-variate probability distribution to match the
amplitude of the simulated P wave. A PQ interval complying with the simulated
P wave duration was selected like-wise by drawing from a multi-variate normal
distribution generated from clinical P wave duration and PQ interval values.
Finally, the P waves and the scaled QRST complexes were concatenated using a
sigmoid shaped segment of a length determined by the difference of PQ interval
and P wave duration. When synthesizing ECG segments for the 1st degree AV
block class, the PQ interval was sampled from the range $>$200 $\mathrm{ms}$.

To account for heart rate variability in the simulated 10 $\mathrm{s}$ ECGs,
we refrained from simply repeating the concatenated single heart beat multiple
times. Instead, the heart rate variability model developed by Kantelhardt et
al. [46] was used to generate a series of RR intervals for an average heart
rate within physiological ranges (50-90 bpm) determined from the QT interval
of the respective simulation run using the multi-variate normal distribution.
For each heart beat holding a different RR interval, the signal was shrunk or
stretched in the [QRSoff, Toff] interval, again by sampling values from a
multi-variate normal distribution derived from clinical QRS duration, QT- and
RR interval values. After adding a sigmoidal shaped TP segment to connect
subsequent heart beats in the defined RR interval, we obtained the final 10
$\mathrm{s}$ 12 lead ECG. The raw ECG signal was superimposed with realistic
ECG noise as reported by Petranas et al. [47]. The amplitudes of the noise
vectors were scaled based on a chosen signal to noise ratio between 15 and 20
dB.

## Data Records

The MedalCare-XL dataset is publicly available under the Creative Commons
Attribution 4.0 International license [10]. Approximately 1,300 ECGs of
10$\mathrm{s}$ length for each disease class are stored in csv format. Rows
1-12 contain the 12 leads of each ECG following the order I, II, III, aVR,
aVL, aVF, V1-V6. All signals are sampled at 500 $\mathrm{Hz}$, amplitudes are
in $\mathrm{mV}$. Each signal is available in three different versions:
’run_*_raw.csv’ contains the noise-free synthesized ECG, ’run_*_noise.csv’
contains the synthesized ECG with superimposed realistic ECG noise [47],
’run_*_filtered.csv’ contains the bandpass filtered version (Butterworth
filters of order 3, cut off frequencies of 0.5 $\text{\,}\mathrm{Hz}$
(highpass) and 150 $\text{\,}\mathrm{Hz}$ (lowpass)) of the synthesized ECGs
with superimposed noise. For meaningful machine learning approaches, the
signals are split in suggested subsets for training, validation and testing
depending on the atrial and ventricular anatomical models the single
simulation runs were based on to make sure each anatomical model is only
contained in one of the subsets. Example ECGs of lead II for each disease are
shown in Figure 4 (A). In Figure 4 (B), exemplary ECGs for each MI pathology
class are shown corresponding to different occlusion sites and degrees of
transmurality.

## Technical Validation

We have employed two different approaches for the technical validation of the
MedalCare-XL dataset of simulated, synthetic 12 lead ECGs as described in the
following. For a validation of the complete dataset, the statistical
distribution of ECG features extracted separately for each class (healthy
control and specific pathologies) from the records in the MedalCare-XL
database were compared to the distributions of the corresponding features
extracted from the clinical PTB-XL that were recently summarized in the PTB-
XL-Feat dataset [48]. In addition, we performed several so-called clinical
Turing tests, where the ability of expert cardiologists to distinguish the
simulated ECGs from clinical ECGs was evaluated again with representative
samples from the MedalCare-XL and PTB-XL databases as described in detail
below.

### Feature Distribution

To validate the simulated data against the statistical properties of
clinically recorded ECGs, interval and amplitude features were extracted from
the synthetic dataset and from PTB-XL using ECGdeli [45] and compared to one
another. Figure 5 shows the probability density functions for 6 timing and 5
amplitude features extracted from lead II of all ECGs in the healthy clinical
and virtual cohort. Except for the T wave amplitudes, the feature values for
the synthetic signals lie within the clinical and physiological ranges.
However, the feature distributions from the healthy and the virtual data do
only coincide for the QRS duration. All other simulated timing and amplitude
features only cover a subset of the clinically observed ranges.

In Figure 6, a comparison of feature distributions for healthy and
pathological ECGs in the virtual cohort (top panel) and the clinical cohort
(bottom panel) is visualized for timing or amplitude features that are
clinically considered for a diagnosis of the respective disease. It is
apparent that the change in feature values extracted from healthy and diseased
ECGs is consistent between the simulated and the clinical data even though
absolute feature ranges sometimes deviate.

### Clinical Turing Tests

We aimed to ensure that the synthetic ECG signals correspond to the clinically
measured signals with respect to ECG features which are characteristic for
healthy cases. If cardiologists are not able to distinguish between measured
and simulated ECG signals, this will increase confidence in the _in-silico_
model as a surrogate for real clinical data. Therefore such a test can be
considered as a clinical Turing test. For this, cardiologists were asked to
perform an online Turing test to evaluate and to provide feedback on both
healthy and pathological ECGs. A first clinical Turing test was conducted to
determine the ability of the synthetic 12 lead ECGs within the database to
pass as real clinical signals. In a second test, cardiologists were asked to
determine the pathology of the signals as conducted routinely in ECG
diagnostics. Under all clinical Turing tests, the PTB-XL [1] database served
as the basis for the measured signals and the simulated database described
above was used for the synthetically generated signals.

#### Development of Online Platform for Clinical Turing Test

In order to conduct clinical Turing tests, an online solution provided by the
Know-Centeriiihttps://www.know-center.at, a research center for data science
and artificial intelligence located in Graz, was used. The Know-Center
extended its TimeFuseiiiiiihttps://ecgviewer.timefuse.io/public/login/turing
online signal data platform to include a survey feature and a plotter to
visualize 12 lead ECG signals. The ECG plotter was designed specifically to
present 12 lead ECGs in a typical visualization as seen by cardiologists in
the clinic on chart paper. Namely, horizontal lines on the pink background
correspond to $0.4\text{\,}\mathrm{s}$ and vertical lines correspond to
$0.1\text{\,}\mathrm{mV}$. The platform was also designed for hosting of
multiple clinical Turing tests. Clinical Turing tests of either healthy
signals or pathological signals could then be organized and conducted
separately.

#### Conducting Tests

In a first iteration, Turing tests were performed with normal healthy control
ECGs to better understand the ability of signals to pass as clinical signals
under normal healthy. For this purpose, five groups with 20 signals each were
created, resulting in a total of 100 signals. For the measured ECGs, 50
signals were randomly selected from a subset of the PTB-XL database, which
contained only signals annotated as 100% healthy. For the generated ECGs, 50
signals under healthy sinus rhythm were randomly taken from the synthetic
database described above. After pre-processing and filtering the 100 signals,
the five groups were uploaded to the online platform and assigned to the
survey participants. Within the test, expert cardiologists were required to
evaluate whether each ECG test case from the total 100 was measured or
generated. Clinicians were also allowed to refrain from answering, but a lack
of a statement was taken as a false classification. All clinicians were also
asked to provide reasoning behind the classification. A total of 6 clinicians
performed the test.

A similar test was also performed with pathological conditions to demonstrate
that the synthetic ECGs of the various modeled pathological cases would be
classified by expert clinicans at the same accuracy as real clinical signals
and could not be distinguished from clinically measured ECGs taken from the
PTB-XL database. The cases included myocardial infraction (MI), left bundle
branch block (LBBB), right bundle branch block (RBBB), first degree AV block
(1AVB), and left atrial overload/enlargement (LAO/LAE). Conditions of fibrotic
atrial cardiomyopathy (FAM) and complete interatrial conduction block (IAB)
were neglected as such diseases were not present within PTB-XL. Examples of
the disease are provided in Figures 4 (A) and 4 (B).

Similar to the healthy Turing test, 50 generated ECG signals were taken from
the synthetic database such that each of the five pathological classes is
represented by 10 ECGs. The 50 measured ECGs were randomly selected from five
subsets of the PTB-XL database, 10 cases per subset, where each subset only
contained signals labeled as 100% pathological according to the 5 classes.
Clinicians could choose from a list of 11 labels. Clinicians were asked to
make at least one annotation for each of the 100 pathological 12 lead ECG
signals from a list of 11 pathologies as listed below:

  * •

1AVB

  * •

atrial fibrillation (AFIB)

  * •

FAM

  * •

IAB

  * •

LAO

  * •

LBBB

  * •

MI

  * •

normal healthy control (NORM)

  * •

right atrial overload/enlargement (RAO/RAE)

  * •

RBBB

  * •

Wolf-Parkinson-White syndrome (WPW)

  *

A total of two cardiologists responded.

#### Results

##### Normal Healthy Control Clinical Turing Test

The six clinicians correctly classified 464 of the 600 cases, which
corresponds to an accuracy of $77.33\%$. On the other side, 136 signals
($22.67\%$) could not be correctly classified, including 62 ($10.34\%$)
synthetic and 74 ($12.33\%$) measured ECGs, see Figure 7 (B). A detailed
summary is given in Figure 7 (A),(C). Primary ECG features leading to
classification as simulated included fractionation or improper R wave
propagation in the QRS complex, a spiking or biphasic T wave, and a lack of
physiological noise in the signals.

##### Turing Test of Pathological ECGs

The two clinicians correctly classified the signals as either measured or
clinical in 166 of the 200 cases, which corresponds to an overall accuracy of
$83\%$. On the other side, the type of 34 signals ($17\%$) could not be
correctly classified, including 10 ($5\%$) synthetic and 24 ($12\%$) measured
ECGs, see Figure 7 (E). A detailed summary is given in Figure 7 (D),(F).
Regarding the correct classification of pathological cases, only 101 of the
200 ($50.5\%$) overall cases including both simulated and clinical signals
were classified correctly by both clinicians. Namely, 38 measured ECGs were
classified as the wrong pathology by experts resulting in an accuracy of
$62\%$. Inversely, simulated pathologies were correctly classified at only
$39\%$, with 61 signals being classified incorrectly. A detailed summary is
given in Figure 8 (A),(B).

The actual pathology and the diagnoses given by each clinician within the
pathological clinical Turing test is provided in Figure 8 (C). Some
pathologies were more commonly misdiagnosed by the clinicians and mistaken as
either normal healthy control or an alternative clinical pathology.
Differences in performance were also observed between simulated and clinical
ECG sets. This is highlighted by the confusion matrices constructed for all
pathological cases from the results for both measured and simulated signals
(Figure 8 (D)).

Within clinical signals, the pathological cases of LAO, 1AVB, and MI were
commonly mistaken as a 12 lead ECG in normal sinus rhythm by both clinicians.
Largest differences in diagnostic outcomes between simulated and clinical data
sets is observed for LBBB and RBBB.Within simulated ECGs, LBBB and RBBB were
commonly mistaken for MI.

### Limitations and Summary

The feature analysis showed that the synthetic signals exhibit interval and
amplitude features that are mostly in line with feature ranges reported in
PTB-XL for the healthy and the pathological cohorts. However, they neither
cover the full range of feature values that occur in clinical practice nor are
they characterized by accurately coinciding distributions. This could be
attributed to the fact that the atrial model population was parameterized
using ECG biomarker ranges for P wave amplitudes and durations reported for
extensive clinical cohorts partially comprising $>$200,000 subjects [49, 50]
which might lead to slightly different feature distributions compared to those
extractable from PTB-XL. The QRST complexes were also parameterized according
to experimental data or clinical data conducted on smaller model cohorts that
may not be representative of the entire population. Some parameters were also
estimated as no direct clinical or experimental data is available for these
entities. One such example is the heightened T wave amplitudes, which stem
from repolarization gradients in the ventricles that generate large cardiac
source. While the occurrence of repolarization gradients are known [32, 33],
the exact nature of such gradients are not well understood and thus hard to
parameterize for a patient population. Therefore, the synthetic signals are
not fully representative for an entire population, such as the one in PTB-XL.

The feature distributions in the synthetic cohort are however consistent in
themselves, i.e., unrealistic combinations of different features are unlikely
to occur. For example, the upper limit of RR intervals in the simulated
healthy cohort does not exceed 1000 $\mathrm{ms}$, while simultaneously, the
QT interval also only covers lower ranges of the clinical QT interval values
(compare Figure 5). This is due to the fact that multi-variate normal
distributions were used during the synthesization procedure ensuring that
clinically reported correlations between ECG biomarkers (such as P wave
duration and PR interval or QT duration and RR intervals) are taken into
account. Furthermore, detailed mechanistic electrophysiological models of the
heart were employed and simulation parameters in reasonable ranges reported in
literature were chosen leading to realistic single beat P waves and QRST
complexes in most cases.

It must be noted that PTB-XL lacks clinical data for fibrotic atrial
cardiomyopathy and for interatrial conduction block. Thus, fidelity assessment
of ECG features within these two classes by means of a comparison to clinical
data was not possible using the same clinical ECG resources. However, we
already showed in previous work that the simulated P waves reproduce
characteristic changes in key diagnostic ECG markers [22, 51]. These include a
prolongation of the P wave duration compared to the control simulations due to
delayed depolarization in fibrotic patches as well as a retrograde activation
of the left atrium through interatrial conduction pathways on the posterior
wall. Moreover, as shown in Figure 6, in interatrial conduction block
patients, the morphology and therefore the P wave amplitude is markedly
changed in lead aVL compared to the healthy cohort. In patients with fibrotic
atrial cardiomyopathy, the most pronounced decrease in P wave amplitude due to
scar tissue not contributing to the overall source distribution in the atria
occurs in the lateral leads (compare Figure 6).

The clinical Turing tests aimed to investigate the ability of the 12 lead ECG
signal to exhibit morphological features in accordance with clinical
diagnostic criteria as routinely assessed by clinicians under both normal
healthy control and pathological conditions. Within the clinical Turing test
performed for normal healthy control, it can be observed that accuracy in
identifying whether a signal was simulated or clinical was 77$\%$ accurate.
Primary ECG features leading to identification as a synthetic signal included
fractionation and R wave progression of the QRS complex. Spiked T waves with
high amplitudes or biphasic T waves could also be observed. Real ECG signals
tended to also exhibit a certain noise types not accounted for, including
electrical disturbances and large baseline wander, that must either be
modulated within simulated data or removed during the clinical Turing test.
Within the clinical Turing test to diagnose pathological ECGs, the accuracy of
type classification increased to 83$\%$, indicating type classification was
easier with synthetic pathological data. Misdiagnosis was common across both
signal types as pathologies were only diagnosed correctly by the two expert
cardiologists in $51\%$ of cases. More clinicians should perform the clinical
Turing test on pathology classification to give a better indication of the
true accuracy of ECG diagnosis on both simulated and clinical signals.
Furthermore, the clinical Turing test must be conducted on a larger number
signals beyond the 100 analyzed, ideally, for the entire ECG synthetic
database.

Regardless, it can be observed that clinicians had varying performance on
clinical-based 12 lead ECG signals in comparison to those taken from the
synthetic ECG database. Clinical signals were classified with the correct
pathology at an accuracy of $62\%$. Simulated signals, on the other side, were
classified correctly for the underlying disease pathology at only $39\%$. LAO
within both clinical and simulated data experienced the highest level of
misdiagnosis and resulted in similar performance. This could be contributed to
the fact that LAO manifests only within the P wave, where morphological
deviations are harder to detect due to a substantially lower amplitude than
the QRS complex. Misdiagnosis was also high among the diseases of LBBB and
RBBB within the simulated data set. Differences in outcome between the
clinical and synthetic signals may stem from the inability of the synthetic
ECG database to manifest the full complexity of the underlying diseases. For
example, remodeling within the ventricles under such conditions may lead to
slower conduction properties and alternative wave morphology. Furthermore,
only complete LBBB or RBBB was modeled. In clinical practice, however, there
are varying degrees of conduction block. A lower reported diagnostic accuracy
for MI and 1AVB is seen for the clinical signals in comparison to the
simulated ECGs, which could also stem from a lack of complexity within the
simulated setup easing diagnosis.

To lower the mismatch in performance between clinical and synthetic signals,
further parameter tuning is needed. Iterative clinical Turing tests would be
beneficial to update parameters ranges to mitigate the prevalence of
undesirable ECG features within the entire database. Refinement could also be
guided by sensitivity analysis that provides more information on the
relationship of model parameters and the morphological traits of simulated
signals as determined by clinicians. However, this requires a large investment
due to the variety in clinical pathological classes, and the lack of known
electrophysiology in such conditions. Certain important ECG features may also
be detected by machine learning analysis [52] to provide insight into the
refined sub-classification of pathological cases beyond current routine
diagnoses.

Some results from the Turing test of pathological cases indicate that standard
protocols for ECG classification by clinicians are not sufficient. Machine
learning algorithms may offer a means to aide in ECG diagnosis to improve
reliability of clinical decisions. Therefore it is important to provide
reference data to test such algorithms. An earlier benchmark study
demonstrated this with the large data set of clinical ECGs in PTB-XL [52]. In
this work, deep learning algorithms were e. g. found to exhibit diagnosis
success rates in the range of 80 – 95 percent depending on the used metric.
The clinical PTB-XL data set was also instrumental in demonstrating the clear
improvement of algorithms based on self-supervised learning [53].
Nevertheless, clinical data bases strongly depend on the quality and the
terminology used to label the ECG data. In addition large sets of publicly
available clinical data sets are still rare and limited in number. Here is
where benchmarking ML algorithm with validated simulated data sets can become
an important tool in the development and benchmarking of new algorithm for ECG
classification. Machine learning algorithms could then also be trained and
tested on real and synthetic data in different combinations. Data bases of
simulated ECGs like the MedalCare-XL set presented in this paper provide also
an important link of the growing knowledge developed in the cardiac modelling
community and practical development of algorithm for data analysis.

## Usage Notes

When using the synthetic ECGs as an input data source for machine learning
applications, samples that were generated based on the same anatomical model
should explicitly belong to only one of the training, testing or validation
sets. As the main variation in morphology of the P waves and QRST complexes
stem predominantly from anatomical differences in the model cohort [54],
splitting the data in the described fashion thus helps to prevent overfitting
to similar or almost identical samples that were already seen during training
[55].

When applying the simulated data for extending or replacing small or
imbalanced clinical datasets, the user is advised to refer to the signals with
superimposed realistic ECG noise instead of the raw signal traces. In this
way, the simulated signals exhibit characteristics due to noise interference
that are also observable in clinical ECGs. Thus, possible domain gaps can be
reduced eventually leading to an improved classification outcome on actual
clinical data.

## Code availability

The anatomical model cohort of the atria is publicly available under the
Creative Commons Licence CC-BY 4.0 [18]. Code for solving the Eikonal equation
and the forward problem of electrocardiography using the boundary element
method as used for the atrial simulations is openly available (Stenroos et al.
[56], Schuler et al. [57]). Python code for synthesizing single beat P waves
and QRST complexes to a 10$\text{\,}\mathrm{s}$ time series using multi-
variate normal distributions for amplitude scaling and interval selection is
publicly available [58].

The cohort of ventricular-torso models are not publicly available due to
constraints of the IRB approval and subject consent. The framework to simulate
electrophysiology of the ventricular-torso model is also not available for
public use in its entirety. However, all simulations can be carried out within
the publicly available openCARP simulation framework [41, 42], albeit less
efficiently with higher compute costs.

## References

  * [1] Wagner, P. _et al._ PTB-XL, a large publicly available electrocardiography dataset.  _Scientific Data_ 7, 1–15 (2020).
  * [2] Roberts, M. _et al._ Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.  _Nature Machine Intelligence_ 3, 199–217, 10.1038/s42256-021-00307-0 (2021).
  * [3] Puyol-Antón, E. _et al._ Fairness in cardiac mr image analysis: An investigation of bias due to data imbalance in deep learning based segmentation.  In de Bruijne, M. _et al._ (eds.) _Medical Image Computing and Computer Assisted Intervention – MICCAI 2021_ , 413–423 (Springer International Publishing, Cham, 2021).
  * [4] Pilia, N. _et al._ Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?  _APL Bioeng_ 4, 041501, 10.1063/5.0018504 (2020).
  * [5] Luongo, G. _et al._ Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram.  _EP Europace_ 10.1093/europace/euab322 (2022).
  * [6] Nagel, C., Schaufelberger, M., Dössel, O. & Loewe, A.  A bi-atrial statistical shape model as a basis to classify left atrial enlargement from simulated and clinical 12-lead ECGs.  In Puyol Antón, E. _et al._ (eds.) _Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge_ , 38–47, 10.1007/978-3-030-93722-5_5 (2022).
  * [7] Luongo, G. _et al._ Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG.  _Cardiovascular Digital Health Journal_ 2, 126–136, 10.1016/j.cvdhj.2021.03.002 (2021).
  * [8] Strocchi, M. _et al._ A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations.  _PloS one_ 15, e0235145 (2020).
  * [9] American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging _et al._ Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association.  _Circulation_ 105, 539–542 (2002).
  * [10] Gillette, K. _et al._ MedalCare-XL, 10.5281/zenodo.7293655 (2022).
  * [11] CIBC (2016).  Seg3D: Volumetric image segmentation and visualization. Scientific Computing and Imaging http://www.seg3d.org.
  * [12] Payer, C., Štern, D., Bischof, H. & Urschler, M.  Multi-label whole heart segmentation using cnns and anatomical label configurations.  In _International Workshop on Statistical Atlases and Computational Models of the Heart_ , 190–198 (Springer, 2017).
  * [13] Chetverikov, D., Svirko, D., Stepanov, D. & Krsek, P.  The trimmed iterative closest point algorithm.  In _Pattern Recognition, 2002. Proceedings. 16th International Conference on_ , vol. 3, 545–548 (IEEE, 2002).
  * [14] Prassl, A. J. _et al._ Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems.  _IEEE Transactions on Biomedical Engineering_ 56, 1318–1330 (2009).
  * [15] Gillette, K. _et al._ A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs.  _Medical Image Analysis_ 71, 102080 (2021).
  * [16] Bayer, J. _et al._ Universal ventricular coordinates: A generic framework for describing position within the heart and transferring data.  _Medical Image Analysis_ 45, 83–93 (2018).
  * [17] Nagel, C., Schuler, S., Dössel, O. & Loewe, A.  A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations.  _Medical Image Analysis_ 74, 102210, 10.1016/j.media.2021.102210 (2021).
  * [18] Nagel, C., Schuler, S., Dössel, O. & Loewe, A.  A bi-atrial statistical shape model and 100 volumetric anatomical models of the atria.  _Zenodo_ 10.5281/zenodo.4309958 (2020).
  * [19] Azzolin, L. _et al._ AugmentA: Patient-specific augmented atrial model generation tool.  _medRxiv_ 10.1101/2022.02.13.22270835 (2022).
  * [20] Zheng, T., Azzolin, L., Sánchez, J., Dössel, O. & Loewe, A.  An automate pipeline for generating fiber orientation and region annotation in patient specific atrial models.  _Current Directions in Biomedical Engineering_ 7, 136–139, 10.1515/cdbme-2021-2035 (2021).
  * [21] Lang, R. M. _et al._ Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.  _Eur Heart J Cardiovasc Imaging_ 16, 233–70, 10.1093/ehjci/jev014 (2015).
  * [22] Nagel, C. _et al._ Non-invasive and quantitative estimation of left atrial fibrosis based on P waves of the 12-lead ECG - a large-scale computational study covering anatomical variability.  _J Clin Med_ 10, 10.3390/jcm10081797 (2021).
  * [23] Pishchulin, L., Wuhrer, S., Helten, T., Theobalt, C. & Schiele, B.  Building statistical shape spaces for 3D human modeling.  _Pattern Recognition_ 67, 276–286, 10.1016/j.patcog.2017.02.018 (2017).
  * [24] Durrer, D. _et al._ Total excitation of the isolated human heart.  _Circulation_ 41, 899–912 (1970).
  * [25] Kassebaum, D. G. & Van Dyke, A. R.  Electrophysiological effects of isoproterenol on purkinje fibers of the heart.  _Circulation Research_ 19, 940–946 (1966).
  * [26] Bayer, J. D., Blake, R. C., Plank, G. & Trayanova, N. A.  A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models.  _Annals of Biomedical Engineering_ 40, 2243–2254 (2012).
  * [27] Streeter Jr, D. D., Spotnitz, H. M., Patel, D. P., Ross Jr, J. & Sonnenblick, E. H.  Fiber orientation in the canine left ventricle during diastole and systole.  _Circulation Research_ 24, 339–347 (1969).
  * [28] Taggart, P. _et al._ Inhomogeneous transmural conduction during early ischaemia in patients with coronary artery disease.  _Journal of Molecular and Cellular Cardiology_ 32, 621–630 (2000).
  * [29] Roberts, D. E. & Scher, A. M.  Effect of tissue anisotropy on extracellular potential fields in canine myocardium in situ.  _Circulation Research_ 50, 342–351 (1982).
  * [30] Keller, D. U., Weber, F. M., Seemann, G. & Dössel, O.  Ranking the influence of tissue conductivities on forward-calculated ECGs.  _IEEE Transactions on Biomedical Engineering_ 57, 1568–1576 (2010).
  * [31] Mitchell, C. C. & Schaeffer, D. G.  A two-current model for the dynamics of cardiac membrane.  _Bulletin of Mathematical Biology_ 65, 767–793 (2003).
  * [32] Opthof, T. _et al._ Cardiac activation–repolarization patterns and ion channel expression mapping in intact isolated normal human hearts.  _Heart Rhythm_ 14, 265–272 (2017).
  * [33] Opthof, T. _et al._ Dispersion in ventricular repolarization in the human, canine and porcine heart.  _Progress in Biophysics and Molecular Biology_ 120, 222–235 (2016).
  * [34] Keller, D. U., Weiss, D. L., Dossel, O. & Seemann, G.  Influence of $I_{Ks}$ heterogeneities on the genesis of the t-wave: A computational evaluation.  _IEEE Transactions on Biomedical Engineering_ 59, 311–322 (2011).
  * [35] Neic, A., Gsell, M. A. F., Karabelas, E., Prassl, A. J. & Plank, G.  Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool.  _SoftwareX_ 11, 100454, 10.1016/j.softx.2020.100454 (2020).
  * [36] Mendonca Costa, C., Plank, G., Rinaldi, C. A., Niederer, S. A. & Bishop, M. J.  Modeling the electrophysiological properties of the infarct border zone.  _Frontiers in Physiology_ 9, 356 (2018).
  * [37] Loewe, A., Wülfers, E. M. & Seemann, G.  Cardiac ischemia-insights from computational models.  _Herzschrittmacher & Elektrophysiologie_ 29, 48–56, 10.1007/s00399-017-0539-6 (2018).
  * [38] Neic, A. _et al._ Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model.  _Journal of Computational Physics_ 346, 191–211, 10.1016/j.jcp.2017.06.020 (2017).
  * [39] Potse, M.  Scalable and accurate ECG simulation for reaction-diffusion models of the human heart.  _Frontiers in Physiology_ 9, 370 (2018).
  * [40] Vigmond, E., Dos Santos, R. W., Prassl, A., Deo, M. & Plank, G.  Solvers for the cardiac bidomain equations.  _Progress in Biophysics and Molecular Biology_ 96, 3–18 (2008).
  * [41] Plank, G. _et al._ The openCARP simulation environment for cardiac electrophysiology.  _Computer Methods and Programs in Biomedicine_ 208, 106223, 10.1016/j.cmpb.2021.106223 (2021).
  * [42] openCARP Consortium _et al._ openCARP v11.0.  _RADAR4KIT_ 10.35097/703 (2022).
  * [43] Fu, Z., Kirby, R. M. & Whitaker, R. T.  A fast iterative method for solving the eikonal equation on tetrahedral domains.  _SIAM J Sci Comput_ 35, c473–c494, 10.1137/120881956 (2013).
  * [44] Loewe, A. _et al._ Patient-specific identification of atrial flutter vulnerability–a computational approach to reveal latent reentry pathways.  _Frontiers in Physiology_ 9, 10.3389/fphys.2018.01910 (2019).
  * [45] Pilia, N. _et al._ ECGdeli - An open source ECG delineation toolbox for MATLAB.  _SoftwareX_ 13, 100639, 10.1016/j.softx.2020.100639 (2021).
  * [46] Kantelhardt, J. W., Havlin, S. & Ivanov, P. C.  Modeling transient correlations in heartbeat dynamics during sleep.  _Europhysics Letters (EPL)_ 62, 147–153, 10.1209/epl/i2003-00332-7 (2003).
  * [47] Petrenas, A. _et al._ Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes.  _Physiol Meas_ 38, 2058–2080, 10.1088/1361-6579/aa9153 (2017).
  * [48] Strodthoff, N. _et al._ PTB-XL-Feat, a comprehensive electrocardiographic feature dataset.  _in preparation_ .
  * [49] Nielsen, J. B. _et al._ P-wave duration and the risk of atrial fibrillation: Results from the Copenhagen ECG study.  _Heart Rhythm_ 12, 1887–1895, 10.1016/j.hrthm.2015.04.026 (2015).
  * [50] Nagel, C., Pilia, N., Loewe, A. & Dössel, O.  Quantification of interpatient 12-lead ECG variabilities within a healthy cohort.  _Current Directions in Biomedical Engineering_ 6, 493–496, 10.1515/cdbme-2020-3127 (2020).
  * [51] Bender, J. _et al._ A Large-scale Virtual Patient Cohort to Study ECG Features of Interatrial Conduction Block.  _Current Directions in Biomedical Engineering_ 8, 97–100, 10.1515/cdbme-2022-1026 (2022).
  * [52] Strodthoff, N., Wagner, P., Schaeffter, T. & Samek, W.  Deep learning for ECG analysis: Benchmarks and insights from PTB-XL.  _IEEE Journal of Biomedical and Health Informatics_ 25, 1519–1528 (2020).
  * [53] Mehari, T. & Strodthoff, N.  Self-supervised representation learning from 12-lead ECG data.  _Computers in Biology and Medicine_ 141, 105114 (2022).
  * [54] Dössel, O., Luongo, G., Nagel, C. & Loewe, A.  Computer modeling of the heart for ECG interpretation—a review.  _Hearts_ 2, 350–368, 10.3390/hearts2030028 (2021).
  * [55] Luongo, G. _et al._ Automatic ECG-based discrimination of 20 atrial flutter mechanisms: Influence of atrial and torso geometries.  In _Computing in Cardiology_ , vol. 47, 1–4, 10.22489/CinC.2020.066 (IEEE, 2020).
  * [56] Stenroos, M., Mäntynen, V. & Nenonen, J.  A Matlab library for solving quasi-static volume conduction problems using the boundary element method.  _Computer Methods and Programs in Biomedicine_ 88, 256–263 (2007).
  * [57] Schuler, S. & Loewe, A.  FIM_Eikonal: v1.0.  _Zenodo_ 10.5281/zenodo.7217554 (2022).
  * [58] Nagel, C., Eichhorn, N. & Loewe, A.  ECG-Synthesization: v1.0.  _Zenodo_ 10.5281/zenodo.7293625 (2022).
  * [59] Gillette, K. _et al._ Automated framework for the inclusion of a his–purkinje system in cardiac digital twins of ventricular electrophysiology.  _Annals of Biomedical Engineering_ 49, 3143–3153 (2021).
  * [60] Odille, F., Liu, S., van Dam, P. & Felblinger, J.  Statistical variations of heart orientation in healthy adults.  In _Computing in Cardiology Conference (CinC)_ , vol. 44, 10.22489/CinC.2017.225-058 (2017).
  * [61] Loewe, A. _et al._ Left and right atrial contribution to the P-wave in realistic computational models.  In van Assen, H., Bovendeerd, P. & Delhaas, T. (eds.) _Lecture Notes in Computer Science_ , vol. 9126 of _Functional Imaging and Modeling of the Heart_ , 439–447, 10.1007/978-3-319-20309-6 (2015).

## Acknowledgements

This work was supported by the EMPIR programme co-financed by the
participating states and from the European Union’s Horizon 2020 research and
innovation programme under grant MedalCare 18HLT07. The authors also
acknowledge the support of the British Heart Foundation Centre for Research
Excellence Award III (RE/18/5/34216). SEW is supported by the British Heart
Foundation (FS/20/26/34952).

We thank the cardiologists Dr. Anna-Sophie Eberl, Dr. Ewald Kolesnik, Dr.
Martin Manninger-Wünscher, Dr. Stefan Kurath-Koller, Dr. Susanne Prassl, and
Dr. Ursula Rohrer for their involvement in the clinical Turing tests and for
their feedback regarding the online platform and the ECG signal morphology. We
also thank Thomas Ebner and his colleagues from the Know-Center for the great
collaboration and the rapid implementation of our requirements in their online
platform TimeFuse.

## Author contributions statement

All authors were involved in the writing and revision of the manuscript.
K.G. built the ventricular-torso model cohort, parameterized and performed the
simulations of the QRST complexes under both sinus and disease, conducted
analysis on the clinical Turing tests, and organized the revision of the
manuscript.
M.G. managed the development of the testing platform for the clinical Turing
test and developed tools to evaluate the test results. He also assisted in all
aspects of simulations and model building.
C.N. built the atrial model cohort, parameterized and performed the P wave
simulations under both sinus and disease conditions, designed and implemented
the synthesization model, led the technical validation of simulated and
clinical ECG biomarkers.
J.B. performed and validated P wave simulations for interatrial conduction
block.
B.W. ran simulations on the ventricular-torso model cohort, and extracted
features from the clinical ECGs for technical validation.
S.W. provided clinical insight and feedback on the clinical Turing tests. He
also provided assistance on parameterization for both models.
M.B. was involved in funding acquisition, provided guidance on relevant data
processing and metrology aspects, reviewed and edited the final manuscript.
T.S. provided motivation behind the study and gave clinical insight and
guidance on relevant disease pathologies.
O.D. was involved in funding acquisition and provided supervision of the
atrial model cohort simulations.
G.P was involved in funding acquisition and provided supervision of the
ventricular-torso model cohort simulations.
A.L. was involved in funding acquisition and provided supervision of the
atrial model cohort simulations.

## Competing Interests

The authors declare no competing interests.

## Figures & Tables

Figure 1: Pipeline for the generation and validation of the synthetic 12 lead
ECG database using individual multi-scale models of the atria and the
ventricles. Electrophysiological Parameters of QRS Complex Simulations
---
Entity |  Parameter |  Value |  Unit |  Reference
Geometry |  $\mathbf{\lambda}_{V,i},i\in[1,13]$ |  [1, 13] |  - |  Gillette et al. 2021[15]
1-5 Fascicular Sites |  $\vec{x}_{rv,mod}$ |  {$\rho=0.2$, $\phi=$[0, 1.0], $z=$[0.1, 0.6], $r=$[0.2, 0.8], $t=$[0, 10]} |  - |  Durrer et al. 1970[24], Gillette et al. 2021[15]
|  |  -
|  |  -
|  |  -
|  |  $\text{\,}\mathrm{ms}$
|  $\vec{x}_{rv,sept}$ |  {$\rho=0.8$, $\phi=$[-1.5, 1.5], $r=0.4$, $z=$[0.2, 0.4], $t=10$ } |  -
|  |  -
|  |  -
|  |  -
|  |  $\text{\,}\mathrm{ms}$
|  $\vec{x}_{lv,sept}$ |  {$\rho=0.2$, $\phi=$[-1.5, 1.5], $r=$[0.05, 0.4], $z=$[0.3, 0.7], $t=10$ } |  -
|  |  -
|  |  -
|  |  -
|  |  $\text{\,}\mathrm{ms}$
|  $\vec{x}_{lv,ant}$ |  {$\rho=0.2$, $\phi=$[1.0, $\pi$], $r=$[0.05, 0.4], $z=$[0.2, 0.8], $t=10$ } |  -
|  |  -
|  |  -
|  |  -
|  |  $\text{\,}\mathrm{ms}$
|  $\vec{x}_{lv,post}$ |  {$\rho=0.2$, $\phi=$[-$\pi$, -1.0], $r=$[0.05, 0.4], $z=$[0.2, 0.7], $t=10$} |  -
|  |  -
|  |  -
|  |  -
|  |  $\text{\,}\mathrm{ms}$
1-5 Conduction Velocity |  $cv_{endo}$ |  2.0 |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ |  Kassebaum et al. 1966[25]
|  $cv_{endo,r}$ |  1.0 |  - |  Gillette et al. 2021[59]
|  $cv_{myo}$ |  0.6 |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ |  Taggart et al. 2000[28]
|  $cv_{myo,r}$ |  4:2:1 |  - |  Taggart et al. 2000[28]
1-5 Myocardial Fiber |  $\alpha_{endo}$ |  60.0 |  ∘ |  Bayer et al. 2012[26], Streeter et al. 1969[27]
Orientations |  $\alpha_{epi}$ |  -60.0 |  ∘
|  $\beta_{endo}$ |  -65.0 |  ∘
|  $\beta_{epi}$ |  25.0 |  ∘
1-5 Heart Conductivity |  $\sigma_{il}$ |  0.34 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$ |  Roberts et al. 1982[29]
|  $\sigma_{in}$ |  0.06 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
|  $\sigma_{it}$ |  0.06 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
|  $\sigma_{el}$ |  0.12 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
|  $\sigma_{en}$ |  0.08 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
|  $\sigma_{et}$ |  0.08 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
1-5 Volume-Conductor |  $\sigma_{torso}$ |  0.22 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$ |  Keller et al. 2010[30]
Conductivities |  $\sigma_{atria}$ |  0.0537 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
|  $\sigma_{lungs}$ |  0.0389 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
|  $\sigma_{blood}$ |  0.7 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$
Table 1:  Model parameters for the electrophysiology within the ventricular
simulations generating QRS simulations. Positioning, sizing, and timing of the
5 sites of fascicular breakthrough representing the His-Purkinje System within
the ventricles provide variation in the QRS complex. Fixed parameters were
held constant at physiological values across all simulations as indicated.

Electrophysiological Parameters of T Wave Simulations
---
Entity |  Parameter |  Value |  Unit |  Reference
Ionic Model |  $\vec{i}_{sinus}$ |  {$V_{gate}=0.13$, $V_{min}=-86.2$, $V_{max}=40.0$, $\tau_{in}=0.3$, $\tau_{out}=5.4$, $\tau_{open}=80.0$} |  - |  Mitchell & Schaeffer 2003[31]
|  |  $\text{\,}\mathrm{mV}$
|  |  $\text{\,}\mathrm{mV}$
|  |  -
|  |  -
|  |  -
1-5 Repolarization Gradients |  $APD_{min}$ |  [150, 175] |  $\text{\,}\mathrm{ms}$ |  Opthof et al. 2017[32].Opthof et al. 2016[33],Keller et al. 2011[34]
|  $APD_{max}$ |  [225, 250] |  $\text{\,}\mathrm{ms}$
|  $\vec{q}_{w}$ |  {$\rho=$[-0.6, 0.0], $\nu=$[0.1, 0.15], $\phi=0$, $z=$[0.9, 1.0]} |  -
|  |  -
|  |  -
|  |  -

Table 2:  Model parameters for the electrophysiology within the ventricular
simulations generating T waves simulations. Base parameters of the action
potential were held constant, but variations in action potential duration are
prescribed using weighted gradients.

Electrophysiological Parameters of Myocardial Infarction
---
Entity |  Parameter |  Value |  Unit |  Reference
Sizing of Infarct |  $d_{co}$ |  [0, 1.0] |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$ |  Keller et al. 2011[30]
1-5 Infarct Center |  $\vec{x}_{LAD,mi}$ |  { $\phi=$[0.0,2.0], $z=$[0.1, 1.0]} |  - |  AHA et al. 2002[9]
|  |  -
|  |  -
|  $\vec{x}_{RAD,mi}$ |  {$\phi=$[-2.0, 0.0], $z=$[0.2,1.0] } |  -
|  |  -
|  |  -
|  $\vec{x}_{LCX,mi}$ |  { $\phi=$[2.0, 3.14] $\cup$ [-3.14,-2.0], $z=$[0.2, 1.0] } |  -
|  |  -
|  |  -
|  |  -
1-5 Infarct Transmurality |  $\rho_{\nu,mi}$ |  $\\{0.3,1.0\\}$ |  - |
1-5 Conduction Velocity |  $cv_{BZ}$ |  0.15 |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ |  Mendonca et al. 2018[36]
|  $cv_{BZ,r}$ |  1.0 |  -
1-5 Mitchell Schaeffer |  $\vec{i}_{BZ}$ |  {$V_{gate}=0.13$, $V_{min}=-73.1$, $V_{max}=12.5$, $\tau_{in}=0.45$, $\tau_{out}=3.6$, $\tau_{open}=44.0$} |  - |  Mitchell,Schaeffer 2003[31], Loewe et al. 2018[37]
Ionic Model |  |  $\text{\,}\mathrm{mV}$
|  |  $\text{\,}\mathrm{mV}$
|  |  -
|  |  -
|  |  -
Table 3:  Additional parameters were included to define infarct zones within
the ventricular-torso model. Variations in the locations of the occlusion of
the 3 primary arteries (LCA, LCX, and RCA) are based on clinical observations.
Two different transmuralities are modeled. Fixed parameters comprise
conductivity, conduction velocity, and the cellular settings.

Figure 2: Cohort of ventricular-torso models derived from clinical MRIs.
Tissues include lungs, blood pools, atrial tissue, ventricles, and general
torso. Parameters dictating ventricular electrophysiologyfor normal healthy
control were varied through physiological ranges. Disease conditions of BBB
and MI were then modeled by making adaptions to the model.

Figure 3: Anatomical model cohort for atrial simulations. 80 atrial geometries
with physiological left and right atrial volumes were derived from a bi-atrial
statistical shape model [17] and served as a basis for normal healthy control
simulations. 9 different volume fractions of these models were additionally
replaced by fibrosis for simulations of fibrotic atrial cardiomyopathy.
Interatrial conduction block signals were generated by blocking conduction in
Bachmann’s Bundle in the same 80 geometries. Furthermore, 45 geometries with
enlarged left atrial volumes were generated. As for the torso anatomy, 25
geometries were derived from a human body statistical shape model to account
for height, weight and gender differences in the virtual patient cohort.
Moreover, the rotation angle as well as the spatial position of the atria
inside the torso were varied in physiological ranges.

Electrophysiological Parameters of P wave simulations
---
Entity |  Parameter |  Value |  Unit |  Reference
Geometry |  $\mathbf{\lambda}_{A,i},i\in[1,24]$ |  [-3, 3] |  - |  Nagel et al. 2021[17]
|  $\mathbf{\lambda}_{T,i},i\in[1,2]$ |  [-2, 2] |  - |  Pishchulin et al. 2017[23]
1-5 Atrial rotation |  $\alpha_{x}$ |  [-20, 20] |  ∘ |  Odille et al. 2017[60]
|  $\alpha_{y}$ |  [-20, 20] |  ∘
|  $\alpha_{z}$ |  [-20, 20] |  ∘
1-5 Atrial translation |  $t_{x}$ |  [-10, 10] |  mm |  Odille et al. 2017[60]
|  $t_{y}$ |  [-10, 10] |  mm
|  $t_{z}$ |  [-10, 10] |  mm
1-5 Transversal Conduction |  $\mathrm{CV_{bulk\leavevmode\nobreak\ tissue}}$ |  [0.57, 0.85] |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ |  Loewe et al. 2015[61]
Velocities |  $\mathrm{CV_{interatrial\leavevmode\nobreak\ connections}}$ |  [0.46, 0.70] |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$
|  $\mathrm{CV_{crista\leavevmode\nobreak\ terminalis}}$ |  [0.57, 0.85] |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$
|  $\mathrm{CV_{pectinate\leavevmode\nobreak\ muscles}}$ |  [0.62, 0.92] |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$
|  $\mathrm{CV_{inferior\leavevmode\nobreak\ isthmus}}$ |  [0.57, 0.85] |  $\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$
1-5 Anisotropy ratios |  $\mathrm{AR_{bulk\leavevmode\nobreak\ tissue}}$ |  1.94 |  - |  Loewe et al. 2015[61]
|  $\mathrm{AR_{interatrial\leavevmode\nobreak\ connections}}$ |  3 |  -
|  $\mathrm{AR_{crista\leavevmode\nobreak\ terminalis}}$ |  2.56 |  -
|  $\mathrm{AR_{pectinate\leavevmode\nobreak\ muscles}}$ |  3.24 |  -
|  $\mathrm{AR_{inferior\leavevmode\nobreak\ isthmus}}$ |  1 |  -
1-5 Torso conductivity |  $\sigma_{torso}$ |  0.22 |  $\text{\,}\mathrm{S}\text{\,}{\mathrm{m}}^{-1}$ |  Keller et al. 2010[30]
Table 4:  Model parameters for atrial simulations. Values were varied randomly
following a uniform distribution in the specified intervals. Fixed parameters
comprise anisotropy ratios and torso conductivity, which were defined as
indicated in the respective column.

Figure 4: (A) Exemplary 10 $\mathrm{s}$ ECGs (lead II) of each pathology class
and a normal healthy control in the virtual cohort. (B) Exemplary 10
$\mathrm{s}$ ECGs (lead II) of each MI pathology class for different occlusion
sites and degrees of transmurality.

Figure 5: Comparison of features in the healthy clinical and virtual cohort.
Probability density functions are shown for timing features (left column, from
top to bottom: P wave duration, QRS duration, T wave duration, PR interval,
QTinterval, RR interval) and amplitude features (right column, from top to
bottom: P wave amplitude, Q / R / S peak amplitude, T wave amplitude). Blue
and red curves represent the distributions calculated based on the clinical
and the simulated data, respectively.

Figure 6: Comparison of features extracted from healthy (solid lines) and
pathological (dotted line) ECGs in the clinical (blue curves, bottom panel)
and virtual (red curve, top panel) cohorts. Probability density functions are
shown for selected timing or amplitude features that are clinically evaluated
for a diagnosis of the displayed disease (from left to right: RBBB, LBBB, MI,
1AVB, LAO, IAB and FAM).

Figure 7: (Type classification) Healthy cases: (A) Classification results for
each of the six expert clinicians for the five Turing tests and percentage of
correct assessments. In summary, 62 of 300 assessments of the synthetic ECGs
and 74 of 300 assessments of the measured ECGs could not be correctly
classified by the experts. (B) Type classification matrix across all 600
assessments. (C) Results of the clinical Turing tests performed by 6
clinicians. Each row corresponds to a clinical Turing test and each square
belongs to one of the 20 ECGs per test. Shown is the relative number of
clinicians who correctly classified the corresponding signal. Pathological
cases: (D) Type classification results for each of the two expert clinicians
for the five Turing tests and percentage of correct assessments. In summary,
10 of 100 assessments of the synthetic ECGs and 24 of 100 assessments of the
measured ECGs could not be correctly classified by the experts. (E) Type
classification matrix across all 100 assessments. (F) Results of the clinical
Turing tests performed by 2 clinicians. Each row corresponds to a clinical
Turing test and each square belongs to one of the 20 ECGs per test. Shown is
the relative number of clinicians who correctly classified the type of the
corresponding signal.  Figure 8: (Pathology classification) (A) Pathology
classification results for each of the two expert clinicians for the five
Turing tests and percentage of correct assessments. In summary, 61 of 100
assessments of the synthetic ECGs and 38 of 100 assessments of the measured
ECGs could not be correctly classified by the experts. (B) Pathology
classification matrix across all 100 assessments. (C) (Clinician-based). Shown
are the classifications for both clinicians of all ECG Signals. For each ECG
signal designated by a s quare, the top entries are the correct pathology and
the bottom entries are the pathology actually selected by the user. Each row
corresponds to a clinical Turing test and each square belongs to one of the 20
ECGs per test. (D) Confusion Matrices.