# Multimodal Personalisation of Cardiac Electrophysiology Models combining 12-lead ECG and Computed Tomography

Buntheng Ly, Nicolas Cedilnik, Mihaela Pop, Josselin Duchateau, Frédéric Sacher, Pierre Jaïs, Hubert Cochet, Maxime Sermesant

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Buntheng Ly, Nicolas Cedilnik, Mihaela Pop, Josselin Duchateau, Frédéric Sacher, et al. Multimodal Personalisation of Cardiac Electrophysiology Models combining 12-lead ECG and Computed Tomography. FIMH 2025 - Functional Imaging and Modeling of the Heart, Radomir Chabiniok; Maria Gusseva; Tarique Hussain; Hoang Nguyen; Vlad Zaha; Qing Zou, Jun 2025, Dallas (TX), United States. 10.1007/978-3-031-94559-5_1. hal-05122021

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Distributed under a Creative Commons Attribution 4.0 International License
Multimodal Personalisation of Cardiac
Electrophysiology Models combining 12-lead ECG
and Computed Tomography

Buntheng Ly<fnref n="1" />, Nicolas Cedilnik<fnref n="2" />, Mihaela Pop<fnref n="2" />, Josselin Duchateau<fnref n="1" />,<fnref n="3" />,
Frédéric Sacher<fnref n="1" />,<fnref n="3" />, Pierre Jaïs<fnref n="3" />, Hubert Cochet<fnref n="1" />,<fnref n="3" />, and Maxime Sermesant ☒<fnref n="1" />,<fnref n="2" />

**Abstract.** Electrophysiology (EP) study is an invasive and time-consuming procedure that maps the heart activity and evaluates the inducibility of ventricular tachycardia (VT). Unfortunately, this diagnostic procedure often fails to identify patients at risk of potentially lethal VT in scar-related patients. Advances in computer modelling have enabled virtual patient-specific simulations of VT using cardiac imaging to delineate heart geometry and its structural changes (e.g. fibrosis). In this study, we build upon an existing work where in-silico induction of VT was obtained through an EP model personalised from CT-defined wall thickness (WT). We propose an automated framework for multi-modal parameterisation of the EP model, by combining information derived from CT scans and 12-lead ECGs, aiming to fine-tune the model parameters based on electrical features extracted from recorded ECGs. Myocardial WT was used for baseline parameterisation, then the electrical wave propagation through the heart and the surface 12-lead ECG signals were simulated. We compared the simulated and measured ECGs recorded in sinus rhythm, and optimised the model configuration in two stages: i) early onset approximation, and ii) EP model parameter adjustment, using the Covariant Matrix Adaptation Evolution Strategy (CMA-ES). The optimisation framework was tested on a small patient dataset (n=6). Compared with the recorded ECG, the optimised model achieved up to 89.81% accuracy in QRS peaks detection, with an average QRS duration error of 5.93ms—an improvement of 31.49ms over the initial baseline parameters. We also tested the optimised parameters on two patients, where electro-anatomical maps of VT were available. The result shows that the updated parameters are able to correct the VT cycle length discrepancy reported in the reference study and successfully induced virtual VT with cycle length and pattern closer to those recorded, as compared to the baseline parameters.

**Keywords:** Computational Modelling · Myocardial Infarction · ECG · Computed Tomography · Model Personalisation.

<footnote n="1">¹ Institut Hospitalo-Universitaire Liryc, Bordeaux, France</footnote>

<footnote n="2">² Centre Inria Université Nice Côte d'Azur, Sophia Antipolis, France</footnote>

<footnote n="3">³ Bordeaux University Hospital, Bordeaux, France
✉ Contact: maxime.sermesant@inria.fr</footnote>
# 1 Introduction

Post-infarction ventricular tachycardia (VT) is a major cause of sudden cardiac death (SCD), and its current management (i.e., diagnosis and ablation therapy) represents a critical burden to clinicians. For instance, the typical diagnostic method consists of a catheter-based electrophysiology (EP) study, an invasive and time-consuming procedure that maps the heart electrical activity and evaluates the inducibility of VT. Unfortunately, this diagnostic procedure often fails to identify patients at risk of potentially lethal VT, and consequently affects the ablation success [1]. Thus, better and less invasive diagnosis tools for providing accurate predictions of VT risk may improve the overall VT management.

In [2], the authors proposed an automatic, fast, and personalisable framework for cardiac EP simulation to identify potential patterns of re-entrant VT waves. The method calibrates the EP simulation parameters based on the myocardial thickness of the left ventricle (LV) using a set of predefined parameters derived from intracardiac electrical mapping data. Myocardial wall thinning in the LV has been shown to correlate with the slow conductivity, which enables the estimation of the fibrosis regions [3]. It was also shown in [4] that the channels, identified through the heterogeneity in myocardial wall thinning, have a strong correlation with conduction channels of the VT isthmuses. Although the method personalised the model parameters based on patient anatomy, it does not account for potential electrical variations specific to individual patients.

In this paper, we address this limitation by proposing a framework that incorporates the ECG signal to further optimise the EP model based on patient-specific electrical data. The framework is fully automatic and employs a two-step optimisation approach, similar to those in [5, 6]. The first step estimates the onset activation sites of sinus rhythm, after which the model parameters can be optimised in the second step.

# 2 Material and Methods

The complete pipeline for the virtual VT induction and the proposed optimisation framework are shown in Fig. 1. Using the computational EP model and the baseline parameters reported in [2], our optimisation framework aims to update these baseline parameters using patient's ECG recordings. In addition to extracting the biventricular anatomical domain for simulations from the CT images, we propose an automatic method for estimating the positions of ECG leads.

The 12-lead ECG signals are simulated using the anatomical heart domain, and then compared with recorded ECGs in sinus rhythm to optimise the computational model. The optimisation is performed using a two-step approach. First, we estimate the onset activation points on the LV and RV to recreate the ECG signals with QRS peaks matching the ground truth. Regional onset activation points are used as surrogates for the Purkinje system to simplify the optimisation process. Finally, the parameter corresponding to the conduction velocity in
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Comprehensive VT induction pipeline with multimodal data personalisation. Starting with the simulation pipeline proposed in [2] as the baseline CT-defined model, we implemented a fully automatic method in order to optimise the model parameters using information extracted from the 12-lead ECG signal recorded in sinus rhythm.</figcaption></figure>

the biophysical model is optimised in order to match the QRS peak timings of
the simulated signal to the ground truth.

**2.1 Clinical Data**

In this work, we selected a small cohort of retrospective datasets that were anal-
ysed in a previous study [2]. Notably, here we only included n=6 post-infarction
patients who have both the cardiac CT imaging scan and the numerical record-
ings of the 12-lead ECG during sinus rhythm. We manually selected the QRS
window of the ECG signals from all six patients and used it as the optimisation
ground truth.

## 2.2 Simulation Domain and ECG leads placement

We extracted the 3D heart anatomy (i.e., geometry and structural information)
from the CT scans by following the image processing steps detailed in [2], includ-
ing an automatic segmentation of the right ventricle (RV) and left ventricle (LV)
using a trained deep learning model, and the computation of LV wall thickness
using the PDE method proposed by [7].
We propose a semi-automatic, rule-based method to place the nine main leads of the surface ECG in the simulation domain based on the skeletal masks of a trans-axial CT volumetric image. The method consists of selecting the following sites for chest leads:

- V1 & V2: The points to the left and right side of sternum, closest to the middle of the anterior extremity of the 4th & 5th rib (4th intercostal space).

- Pseudo-V4: The middle of the anterior extremity of the 5th intercostal space.

- V5 & V6: Manually placed at the same slice level (in Z-axis) as pseudo-V4.

- *Projection of V1, V2, V5 (at inference) and V6 (at inference) leads to the closest point on the torso.*

- V4 & V3: The midpoint between V2 & V6 and V2 & V4.

- *Projection of V3 and V4 leads to the closest point on the torso.*

- LA & RA: The centre of the head of left and right humerus, respectively.

- LL: The centre of the head of left femur, respectively.

The standard placement for the lead V4 is on the 5th intercostal space, at the midclavicular line. The term "pseudo-V4" is used because the distal ends of the 5th and 6th left ribs do not correspond to the midclavicular line. We estimate the midclavicular line as the midpoint between lead V2 located at similar coordinate in the transverse plane as the sternal extremity of the clavicle, and lead V6 located at the midaxillary line at the similar transverse plane coordinate as the acromial extremity of the clavicle. The estimation ensured a consistent distance between leads V2, V4 and V6, especially for inputs with a limited field of view.

We applied this method to a public dataset of 98 full-body CT scans with expert segmentation of the skeletal masks [8], and computed the reference positions of each leads. The positions were calculated in spherical coordinates, using the centre of the bounding box of the whole heart region as the origin. The reference coordinates are presented in Tab. 1. At inference, the placement of ECG leads was performed automatically using the skeletal segmentation from the pretrained TotalSegmentator model [8] and the reference coordinates, which were used as substitute for the placement of leads V5 & V6, as well as any lead outside of the input field of view.

## 2.3 12-lead ECG Simulation

**Simulation of cardiac electrophysiology** Here we used the cardiac simulation framework proposed in [2], which allows for fast, personalised simulation of electrical wave propagation through the heart.

This particular framework models cardiac EP by employing a modified Mitchell-Schaeffer model [9] using two state variables as per Eq. 1, where *v* and *h* represents the transmembrane potential and the recovery (gate) variables. The term *J*<sub>in</sub> and *J*<sub>out</sub> represent the current density in and out of the cardiomyocyte membrane, respectively. The term *J*<sub>stim</sub> refers to the external stimulation current density. The term *D* is the anisotropic diffusion tensor which depends on tissue electrical conductivity *d*, anisotropy ratio *ρ* and fiber directions *a*. The EP
<table><caption>**Table 1.** Reference spherical coordinates of precordial and limb leads.</caption><thead><tr><th>Lead</th><th>r (mm)</th><th>θ(degree)</th><th>φ(degree)</th></tr></thead><tbody><tr><td>V1</td><td>98.09 ± 18.73</td><td>80.16 ± 10.93</td><td>243.21 ± 8.35</td></tr><tr><td>V2</td><td>89.07 ± 17.67</td><td>79.17 ± 12.03</td><td>265.82 ± 6.65</td></tr><tr><td>V3</td><td>96.78 ± 16.49</td><td>90.72 ± 11.18</td><td>289.23 ± 18.40</td></tr><tr><td>V4</td><td>110.74 ± 20.23</td><td>100.21 ± 10.79</td><td>311.73 ± 21.39</td></tr><tr><td>V5</td><td>131.91 ± 20.29</td><td>98.64 ± 9.33</td><td>350.93 ± 7.39</td></tr><tr><td>V6</td><td>154.97 ± 28.12</td><td>97.33 ± 7.99</td><td>22.99 ± 9.76</td></tr><tr><td>LA</td><td>203.36 ± 20.52</td><td>41.58 ± 7.13</td><td>19.89 ± 10.64</td></tr><tr><td>RA</td><td>234.13 ± 21.41</td><td>49.8 ± 6.62</td><td>166.18 ± 8.57</td></tr><tr><td>LL</td><td>360.12 ± 28.79</td><td>164.24 ± 2.78</td><td>29.31 ± 13.33</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** The baseline parameters of the EP simulation model, as reported in [2, 11].</figcaption></figure>

model is solved using the Lattice Boltzmann (LBM) method [10], which enables GPU parallel computing and significantly reduces computational time.

$$ 
\left\{ 
\begin{aligned}
\frac{dv}{dt} &= J_{in}(v, h) + J_{out} + J_{stim} + \nabla \cdot D\nabla v & J_{in} &= \frac{h(v - \lambda)(1 - v)}{\tau_{in}} \\
\frac{dh}{dt} &= \begin{cases} \frac{1 - v}{\tau_{open}} & \text{if } v < v_{gate} \\ \frac{v}{\tau_{close}} & \text{if } v > v_{gate} \end{cases} & J_{out} &= -\frac{v}{\tau_{out}} \\
& D = d[\rho I_3 + (1 - \rho)aa^T]
\end{aligned}
\right. 
\quad (1) $$

Considering the works in [2, 11], the variables $\tau_{in}$, $\tau_{out}$, $\tau_{open}$, $\tau_{close}$, $\lambda$, $d$ were personalised based on the LV WT using multi-linear interpolation functions. These authors defined six parameters per variable, corresponding to the increments of thickness values from 0 to 5mm and above. In our work here, the reported values were used as the baseline parameters, as shown in Fig. 2. The anisotropy ratio $\rho$ was set to $2.5^{-2}$.

**Electrogram computation** Based on the output of the EP simulations, we computed the corresponding body surface potential $\Psi$ at the electrode position
$X_T$, using the dipole formulation in Eq. 2, as detailed in [5]. $H$ designates a voxel in the cardiac domain, and $V_H$, $\nabla v_H$ and $\sigma_H$ refer to the volume, the action potential gradient and the myocardial volume conductor at the voxel $H$, respectively. The term $\sigma_T$ refers to the volume conductor of the torso, which is considered as a homogeneous and infinite volume. We set the values $\sigma_T = 0.2S/m$ and $\sigma_H = 2.5S/m$, as reported in [5], following the reference values from [12].

$$ \Psi_H(X_T) = \frac{1}{4\pi\sigma_T} \frac{\sigma_H^i V_H(\nabla v_H \cdot \overrightarrow{HT})}{\|\overrightarrow{HT}\|^3} \quad (2) $$

## 2.4 Optimisation Framework

The Covariance Matrix Adaption Evolution Strategy (CMA-ES) [13, 14] was used as optimisation strategy. At each iteration, we sampled a population of test candidates with different parameters from a multivariate normal distribution. Using the parameters of each candidate, we ran the ECG signal simulation and computed the error value (compared to the ground truth signal). Based on the error values, the multivariate normal distribution was then updated. The update followed the maximum likelihood principle to increase the likelihood of sampling successful candidates in future iterations. The evolution of the distribution was tracked at each iteration, called the evolution paths. These paths were used in the covariance matrix adaptation method, which allowed for a faster variant increase in successful directions, and in the population sampling step-size control, ensuring a fast convergence while preventing premature convergence. For both optimisation steps, we set the maximum number of iterations to 100 and the early stopping tolerance to $10^{-3}$.

**Onset Optimisation** We next proposed a simplification of the Purkinje activation system during the sinus rhythm, by using 34 regional onset points on the LV and RV endocardium. On the LV, each onset candidates was defined as the regional centroid of a segment in the AHA 17-segment model [15] of the endocardial surface mesh. A similar segmentation rule was used to define 17 regions on the RV. Figure 3 shows the 34 potential onset positions on the simulation domain.

At each iteration, the different combinations of onset candidates were sampled. The ECG simulation was run using the sampled onsets as stimulation points at $t = 0ms$. The tuning was done by minimising the average error across the 12 ECG signals. The error function was defined in Eq. 3, which computes the mean absolute errors of QRS peaks, QRS, and peaks voltage, $V_{qrs}$. The symbol * refers to the features extracted from simulated signals. The unit of voltage $V$ was set to $mv$, and the voltage of undetectable peak, where any of the Q, R, or S peaks were no visible, was set to $0mv$, which further penalised any detection error of the QRS peaks.

$$ L_{onset} = \frac{1}{3} \left[ \sum \left( |QRS - QRS^*| + |V_{qrs} - V_{qrs^*}| \right) \right] \quad (3) $$
<figure><img src="image_4.png" /><figcaption>Fig. 3. Potential onset identification inspired by the LV AHA 17-segment model.</figcaption></figure>

**Model Parameters Optimisation** To narrow the scope of this study, we only included the current myocardial tissue conductivity (d) as the optimised parameter. Starting with baseline as the initial values, we sampled the six values of the multi-linear interpolation function from a range between [0, 10], which corresponded to [0 − 3]m/s. The conductivity values were optimised using the mean of the error function in Eq. 4, which computed the mean absolute errors of the QRS peak times. The unit for time t was set to ms, and the time of undetectable peaks was set to 0ms.

$$L_{params} = \frac{1}{3} \left[ \sum \left| t_{qrs} + t_{qrs*} \right| \right] \quad (4)$$

## 3 Result

As shown in Fig. 3, the CMA-ES method can be used to effectively search for the optimal onset regions and conductivity (d) to produce accurate QRS peaks and peaks’ time. Figure 3 shows the optimisation outputs, which achieved 86, 11% peak accuracy after the onset optimisation, and an improvement of 105ms and 14.26ms in QRS duration and peaks time error, respectively, after the parameters’ optimisation. For the examplary case presented, 204 and 209 simulations were run during the onset and parameter optimisation. Each optimisation step roughly takes 1 hour of run time. We ran the complete optimisation framework on a computing node with an Intel(R) Xeon(R) E5-2630 CPU @ 2.20GHz with 128 GB of RAM, and an Nvidia GeForce GTX 1080 Ti (12GB) GPU. The LBM method was implemented using the PyTorch library [16].

Table 2 summarises the results for the six patients. Overall, The onset optimisation achieved an average QRS peak accuracy of 89.81%. The average differences in QRS duration and peak times with the optimised parameters were 5.93ms and 7.99ms, representing an improvement by 31.49ms and 5.94ms, compared to the baseline parameters. The optimisation outputs are reported in Fig. 5.

Considerable improvements were observed in patients 001 and 006, where the baseline conductivity failed to reconstruct the recorded signals, as suggested by the high QRS duration and peak time errors. In patient 002, 003, 004 and 005, we observed that the simulated ECGs with baseline parameters were already
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Comparison between recorded and simulated QRS signals (Patient 001). First, the onset optimisation identified the best onset regions to replicate signals with the same QRS peaks and peak voltages being the ground truth (blue signals). The onset-optimised signals are shown in red. Finally, the parameters optimisation (green signals) focuses on finding the optimal conductivity parameters to correct the peak time differences between the onset optimised output (red signals) and the ground truth (blue signals). *Blue: ground truth, Red: onset-optimised signal (with the baseline parameters), Green: parameters-optimised signal*</figcaption></figure>

close to the recorded ECG, with the maximum QRS duration error at only 15.1ms. Although limited, an overall improvement could still be noted after the parameter optimisation step in these patients.

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Optimal onset regions (A) and current conductivity multi-linear interpolation functions (B). The onset results are shown in the bullseye representation of the LV and RV. Each patient is identified by the same colour labels as in the subfigure B.</figcaption></figure>
<table><caption>**Table 2.** Quantitative comparison between the simulated ECG traces and recorded 12-lead ECG waveforms. The peak accuracies were computed using the optimised onset regions with baseline parameters. Each value shown is the average computed from 12 signals.</caption><thead><tr><th rowspan="2">Patient</th><th rowspan="2">Peaks Acc.</th><th colspan="2">Δ QRS Duration(ms)</th><th colspan="2">Δ Peaks Time(ms)</th></tr><tr><th>Opt Param.</th><th>Baseline</th><th>Opt Param.</th><th>Baseline</th></tr></thead><tbody><tr><td>001</td><td>86.11%</td><td><strong>2.48</strong></td><td>107.48</td><td><strong>12.94</strong></td><td>27.20</td></tr><tr><td>002</td><td>94.44%</td><td>9.37</td><td><strong>5.62</strong></td><td><strong>5.14</strong></td><td>5.33</td></tr><tr><td>003</td><td>97.22%</td><td>15.10</td><td>15.10</td><td><strong>8.27</strong></td><td>9.10</td></tr><tr><td>004</td><td>77.77%</td><td><strong>0.17</strong></td><td>4.82</td><td>11.53</td><td><strong>10.94</strong></td></tr><tr><td>005</td><td>88.88%</td><td><strong>3.32</strong></td><td>6.67</td><td><strong>4.77</strong></td><td>5.62</td></tr><tr><td>006</td><td>94.44%</td><td><strong>5.17</strong></td><td>84.82</td><td><strong>5.24</strong></td><td>25.34</td></tr></tbody><tfoot><tr><td colspan="6">Average(± std) 89.81%(±7.17%) 5.93(±5.44) 37.42(±46.20) 7.99(±3.56) 13.93(±9.81)</td></tr></tfoot></table>

Peaks Acc.: peak accuracy; Opt Param.: optimised parameters.

Among the patients with improvement in QRS duration and peak time (patients 001, 005 and 006), only the patients 001 and 005 have the electrical mapping with adequate spatial resolution for the comparison with virtually induced VT characteristics [2]. For these patients, we ran the complete VT induction protocol [2] with the updated parameters. As shown in Fig. 6, we were able to virtually induce the VT patterns that matched the recorded patterns, using the new parameters. Notably, we observed an improvement in the predicted VT cycle length (VTCL) value. For instance, in patient 001, the recorded VTCL during the EP study was 507 ms, while the VTCL of the simulated VT was reported to be 560 ms in [2] (without the optimization) vs. 505 ms obtained in our current optimization study. Similarly, in patient 005, the simulated VT has a CL of 490 ms following the optimisation step, which more closely matched the recorded VTCL value (446 ms) compared to the simulation output using the baseline parameters (590 ms).

# 4 Discussion

In the proposed optimisation framework, we worked under the constraint that the ground truth lead placement sites during the recording of the ECG were unknown, since we believe that it more closely reflects clinical conditions, while the rule-based method emulating the standard electrodes placements was proposed as an estimation. Nonetheless, we acknowledge that we did not take into account neither various causes of lead misplacement [17, 18] nor the morphology differences between the normal and CT scan positions. Based on the result in Fig. 7, we suggest that the misalignment of V3 lead in the simulation domain could be the potential cause of the Q peak error (yellow arrow). Without modifying the onset regions (which could cause error in other leads) this error would be reduced if V3 placement was closer to lead V2. While this might be an error
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Exemplary Simulated vs. Recorded VT patterns (patient 001 and 005). The automatic induction protocol successfully induced VT patterns matching the recorded reentry patterns. The simulations with optimised parameters demonstrate closer VTCL values to the recorded values, compared to the outputs using baseline parameters. Note that the recorded VT patterns were obtained during a clinical EP procedure using the Rhythmia System (Boston Scientific).</figcaption></figure>

due to the automatic placement of V3, it could also be a placement error in the ground truth signal.

Figure 8 shows a sensitive analysis of lead position variations compared to the reference positions obtained with the proposed method in section 2.2. For each lead, we sampled 16 points on a sphere with the radii of [5, 10, 15, 20, 25, 30] mm. We compared the simulated 12-lead ECG signals with the signals obtained at the reference positions. The simulations were performed using the optimised onset regions and the baseline parameters. The average errors and standard deviation computed at each sampling radius are included in Fig. 8. Notably, while the errors remained relatively small at 5mm, as expected, they increased linearly as the distance from the reference points grows. Furthermore, we observed differences in the error slope between different patients. This discrepancy can be attributed to several factors, including the position of the onset points, the geometry of the heart, and the specific characteristics of thinning regions in the LV (i.e., in the scar and VT substrate zone). Thus, in future work, we plan to explore methods to adjust the position of each lead during optimisation to better adapt to the lead placements during the recording. Finally, it is important to note that Eq. 2
assumes a homogeneous tissue conductivity of the torso, which may also lead to
potential errors.

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Potential errors in ECG lead placement (Patient 006). Blue signal: ground truth; Red: parameter-optimised signal.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Sensitive Analysis of electrode lead positions. The average (circle) and standard deviation (vertical dash line) for different sampling distances and patients are reported. The errors increase linearly as the lead sampling distance increases.</figcaption></figure>

We also acknowledge that due to the nature of the optimisation process, the uniqueness of the solutions could not be guaranteed. While the optimisation out-
puts allowed us to obtain a closer reproduction of the recorded signal compared to the default parameters, a re-run of the optimisation process could possibly produce a different set of optimal parameters. Another potential improvement is related to recent developments in ECG imaging (ECGI), which have shown its high potential in identifying of VT substrates [19, 20]. Another study demonstrated that ECGI provides more detailed information regarding the electrical abnormalities, compared to the traditional 12-lead ECG [21]. Furthermore, while the clinical application of ECGI remains limited, the generation of synthetic body surface potentials from the 12-lead ECG signals (using ML methods) has shown to be feasible [22]. Thus, in future work, we will investigate the integration of ECGI/BSP approaches into the optimisation framework, as a more comprehensive alternative to the conventional 12-lead ECG method and a potential method to enhance the uniqueness of the optimised outputs.

The loss computations only consider the QRS peaks. Peak and peak voltage accuracy are used for onset optimisation, providing a direct way to assess changes linked to the onset regions, as the loss computed from entire QRS complex might be less sensitive. This reasoning is also applied to the current conductivity optimisation, which is performed using the timing of the QRS peaks.

In this work, we only studied the optimisation of the QRS segment, which allowed us to limit the number of free parameters, and was done to assess the feasibility of the pipeline. We limit the possible onset points to the 17-segment model proposed by the AHA, as it is a widely recognised method for LV partitioning. While, the 34 possible onset points may suffice for initial estimation, the fixed points lack the flexibility required to replicate in simulation all of the Purkinje network onsets and their precise location. Thus, future work should include a more flexible onset selection strategy to address this limitation.

Although the current optimisation framework only optimised the conductivity, thanks to the flexibility of the CMA-ES method, the framework can be easily extended to include the optimisation of other parameters of the EP model. Taking into account key ECG features such as ST interval and T-wave characteristics (both of which are obtainable through the current biventricular simulation model), would enable the process to update the remaining parameters of the computational EP model described in Eq. 1. Personalising variables related to both depolarization and repolarization phases of action potential duration, for example, would be critical for more accurate simulations of VT re-entrant waves.

Finally, it is important to note that the two-step optimisation process introduces additional computation time and costs to the VT induction framework presented in [2]. Our VT induction framework is mainly designed for diagnostic purposes (VT risk), where the need for real-time simulation is less critical. In the context of therapy planing, preprocedural images and 12-lead ECG data are typically acquired at least 1-3 days prior to ablation [23], which would presumably allow sufficient time for predicting VT foci and reentry patterns using the optimization framework. However, such steps are out of the scope of this paper.
# 5 Conclusion

We proposed a fully automatic method for personalising a cardiac EP computa-
tional model integrating CT images and ECG signals recorded in sinus rhythm.
We further demonstrated that the virtually induced VT patterns and VTCLs
match better with the recorded ones after the parameterisation step using on
the proposed optimisation framework based on the 12-lead ECG signal in si-
nus rhythm. The proposed framework also shows promising results in recreating
realistic sinus rhythm 12-lead ECG signals and can be used to adapt the con-
ductivity parameters of the EP model accordingly. Although baseline parameters
previously reported were adequate in recreating correct QRS durations, we ob-
served that in some patients the personalisation based on electrical data is still
necessary. Thus, in future work, we aim to further improve the current optimi-
sation framework, and to extend the proposed framework to include more model
parameters (e.g. corresponding to the repolarization phase) during the second
phase of the optimisation.

## 5.1 Acknowledgments.

This work has been supported by the French government through the National Research Agency (ANR) 3IA Côte d'Azur, IA Cluster project and IHU LIRYC (ANR-19-3IA-0002, ANR-23-IACL-0001 and ANR-10IAHU-04) and through the France 2030 and the European Union (Next Generation EU) MediTwin project. The authors are grateful to the OPAL infrastructure from Université Côte d'Azur for providing resources and support.

## 5.2 Disclosure of Interests.

The authors have no competing interests to declare that are relevant to the content of this article.

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