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The Contribution of the Body Organs on Modeling of the Body Surface Potential Map

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12 June 2025

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13 June 2025

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Abstract
This study evaluates the contribution of individual organs to the Body Surface Potential Map (BSPM) generated during ventricular activation. The human torso was modeled as an inhomogeneous volume conductor using CT data, and the heart was treated as an anisotropic volume source based on Diffusion Tensor Imaging (DTI). The ventricular conduction system was also extracted from DTI scans. Excitation propagation was simulated using the Monodomain Reaction-Diffusion Equation (MD-RDE), and the resulting BSPM was computed. By selectively removing specific organs from the torso model and comparing BSPMs using correlation coefficient and relative error metrics, the study identifies the relative influence of each organ. Results confirm that the blood volume within the ventricles has the greatest impact on BSPM accuracy, with the bones, lungs, and liver having lesser but measurable effects.
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1. Introduction

The conduction system defines the primary excitation sites within the heart’s myocardium, which subsequently initiate the propagation of electrical activity throughout the myocardium. The resulting potential differences between activated and non-activated regions inside the heart create volume current sources that give rise to both electric potentials on the body surface and magnetic fields detectable outside the body [1].
The body may be represented as either a homogeneous or an inhomogeneous volume conductor. In the homogeneous model, all organs—including the blood volume within the ventricles—are assumed to share identical physical properties. Conversely, the inhomogeneous model assigns distinct parameters to each organ. As demonstrated by Gulrajani and Mailloux [2], the body’s inhomogeneity significantly influences the resulting surface potentials, with the blood mass identified as the most impactful factor.
In reality, all organs exhibit anisotropic properties. Tissues like skeletal muscles display pronounced anisotropy, whereas others, such as the liver and lungs, are nearly isotropic [1]. Accurately modeling organ anisotropy demands extensive data detailing each organ’s structural composition. Nevertheless, treating organs as isotropic materials is a reasonable approximation that simplifies the modeling process [1]. Some models represent the volume conductor—the body—using simplified geometric shapes; however, the most commonly adopted approach is the realistic torso shape. Within this framework, the torso is modeled either as a homogeneous volume conductor [2,3,4,5,6,7,8] or, more frequently, as an inhomogeneous volume conductor [9,10,11,12,13,14,15,16,17,18,19,20].
The majority of models utilize the body surface potential [21,22,23,24,25,26,27,28,29,30], while a smaller number focus on the heart’s magnetic field map [31]. Some models, however, incorporate both electric and magnetic fields in their analysis [3,7,32,33]. This study focuses on measuring the contribution of each organ to the BSPM of ventricle activation.

2. Methods

2.1. The Human Torso and the Human Heart Modeling

Human torso (Figure 1) has been modeled as inhomogeneous volume conductor using CT-Scans [20] and human heart is modeled as anisotropic volume source [34] using DTI images. The ventricles conduction network is extracted as well based on DTI images [35,36].
Figure 1. Human Torso [20].
Figure 1. Human Torso [20].
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Table 1. Different values of tissues and organs resistivity and conductivities [20].
Table 1. Different values of tissues and organs resistivity and conductivities [20].
Organ/Tissue Resistivity
(Ωcm)
Conductivity (mS/mm)
Skeletal muscle 400 25
Fats 2000 5
Bone 2000 5
Liver 600 16.7
Left lung, right lung 1325 7.5
Blood masses 150 66.7
Other tissues and organs 460 21.7
Heart muscle 450 22.2

2.2. Heart Activation Isochrones

Excitation propagation of the heart is modeled in tissue scale based on Monodomain Reaction Diffusion Equation (MD-RDE) [37,38,39].
Figure 2. The isochrones for the excitation propagation of the heart when it is considered an Anisotropic material [38].
Figure 2. The isochrones for the excitation propagation of the heart when it is considered an Anisotropic material [38].
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2.3. Body Surface Potential Map

The BSPM is generated based on the above compositions (Figure 3) [30].

3. Results

It was reported the blood volume inside the ventricles has a significant effect on the BSPM, and this fact is verified in this section. The effects of other organs over the BSPM are also considered by removing each organ from the body and recalculating the BSPM. The case of homogeneous body is also taken into consideration. The difference is considered by comparing the reference configuration (the heart is considered anisotropic material and the body as an inhomogeneous volume conductor) to each of the following configurations:
  • Removing Bones only.
  • Removing Other organs only.
  • Removing Liver only.
  • Removing Lungs only.
  • Removing Blood Volume only.
  • Homogeneous body.
The results of Coefficient Correlation (CC) and Relative Error (RE) for these different configurations are presented in Table 2, and Table 3 respectively, presented graphically in Figure 4 and Figure 5.
It was found the largest difference occurs when removing the blood volume from the BSPM calculations, and then it almost produces an output similar to the homogeneous body case. Removing the Other organs only has slight effect on the results. According to the previous analysis, the effects of organs are arranged in a descending order from the highest effect to the lowest according to the mean value of RE as follows:
  • The Blood Volume.
  • The Bones.
  • The Lungs.
  • The Liver.
  • Any Other organs.

4. Conclusions

The simulation results clearly show that the blood volume inside the ventricles plays the most significant role in shaping the BSPM, nearly equating the error produced when assuming a homogeneous torso. The bones, lungs, and liver also contribute to BSPM accuracy, but to a lesser extent. Removing only non-major organs had minimal impact. These findings highlight the importance of accounting for torso inhomogeneity—especially the inclusion of blood volume—in accurate BSPM modeling and emphasize the limitations of oversimplified homogeneous assumptions.

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Figure 3. System Layout [30].
Figure 3. System Layout [30].
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Figure 4. CC chart of Table 1.
Figure 4. CC chart of Table 1.
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Figure 5. RE chart of Table 2.
Figure 5. RE chart of Table 2.
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Table 2. CC between the full inhomogeneous body and the remove of individual organ form the body.
Table 2. CC between the full inhomogeneous body and the remove of individual organ form the body.
No Bones No Others No Liver No Lungs No Blood Homo.
1 0.994 1.000 0.999 0.998 0.907 0.880
2 0.993 0.999 0.999 0.998 0.898 0.878
3 0.991 0.999 0.999 0.998 0.895 0.860
4 0.991 0.999 0.999 0.998 0.800 0.765
5 0.990 0.999 0.999 0.998 0.797 0.770
6 0.991 0.999 0.999 0.998 0.856 0.852
7 0.991 0.999 0.999 0.998 0.906 0.904
8 0.992 0.999 0.999 0.998 0.942 0.942
9 0.993 0.999 0.999 0.998 0.961 0.962
10 0.994 0.999 0.999 0.998 0.972 0.973
11 0.994 0.999 0.999 0.998 0.980 0.979
12 0.995 0.999 0.999 0.998 0.986 0.981
13 0.995 0.999 0.999 0.998 0.990 0.983
14 0.995 0.999 0.999 0.998 0.992 0.983
15 0.996 0.999 0.999 0.998 0.991 0.981
16 0.996 0.999 0.999 0.998 0.991 0.982
17 0.996 0.999 0.999 0.998 0.992 0.984
18 0.997 0.999 0.999 0.999 0.991 0.985
19 0.997 0.999 0.999 0.999 0.991 0.985
20 0.996 0.999 0.999 0.999 0.993 0.985
21 0.993 0.999 0.999 0.998 0.995 0.983
22 0.989 0.999 0.999 0.998 0.996 0.983
23 0.985 0.999 0.999 0.997 0.996 0.979
Mean 0.993 0.999 0.999 0.998 0.949 0.937
SD 0.002 0.000 0.000 0.000 0.061 0.068
Table 3. RE between the full inhomogeneous body and the remove of individual organ form the body.
Table 3. RE between the full inhomogeneous body and the remove of individual organ form the body.
No Bones No Others No Liver No Lungs No Blood Homo.
1 0.106 0.001 0.008 0.069 0.473 0.549
2 0.117 0.001 0.009 0.063 0.485 0.547
3 0.127 0.001 0.009 0.060 0.473 0.546
4 0.129 0.001 0.010 0.060 0.606 0.655
5 0.137 0.002 0.011 0.061 0.604 0.643
6 0.134 0.003 0.012 0.062 0.532 0.554
7 0.134 0.003 0.012 0.063 0.436 0.455
8 0.125 0.003 0.012 0.063 0.357 0.371
9 0.116 0.003 0.010 0.061 0.304 0.317
10 0.108 0.003 0.009 0.059 0.259 0.270
11 0.102 0.003 0.009 0.053 0.217 0.228
12 0.100 0.003 0.010 0.052 0.176 0.197
13 0.096 0.004 0.013 0.059 0.144 0.184
14 0.091 0.004 0.016 0.064 0.128 0.183
15 0.088 0.004 0.018 0.068 0.127 0.193
16 0.086 0.004 0.021 0.070 0.130 0.197
17 0.081 0.005 0.023 0.068 0.130 0.189
18 0.071 0.005 0.023 0.065 0.129 0.176
19 0.069 0.004 0.018 0.065 0.134 0.168
20 0.089 0.004 0.015 0.064 0.136 0.172
21 0.117 0.003 0.014 0.063 0.135 0.185
22 0.142 0.002 0.017 0.070 0.141 0.193
23 0.172 0.003 0.026 0.079 0.132 0.211
Mean 0.110 0.003 0.014 0.063 0.278 0.321
SD 0.024 0.001 0.005 0.005 0.171 0.170
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