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A reference dataset for verifying numerical electrophysiological heart models.

Koch H, Bousseljot RD, Kosch O, Jahnke C, Paetsch I, Fleck E, Schnackenburg B - Biomed Eng Online (2011)

Bottom Line: The latter were recorded simultaneously from the same individuals a few hours after the MRI sessions.A training dataset is made publicly available; datasets for blind testing will remain undisclosed.While the MRI data may provide a common input that can be applied to different numerical heart models, the verification and comparison of different models can be performed by comparing the measured biosignals with forward calculated signals from the models.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physikalisch-Technische Bundesanstalt, Abbestr, 2-12, 10587 Berlin, Germany. hans.koch@ptb.de

ABSTRACT

Background: The evaluation, verification and comparison of different numerical heart models are difficult without a commonly available database that could be utilized as a reference. Our aim was to compile an exemplary dataset.

Methods: The following methods were employed: Magnetic Resonance Imaging (MRI) of heart and torso, Body Surface Potential Maps (BSPM) and MagnetoCardioGraphy (MCG) maps. The latter were recorded simultaneously from the same individuals a few hours after the MRI sessions.

Results: A training dataset is made publicly available; datasets for blind testing will remain undisclosed.

Conclusions: While the MRI data may provide a common input that can be applied to different numerical heart models, the verification and comparison of different models can be performed by comparing the measured biosignals with forward calculated signals from the models.

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MCG signals. Butterfly plot of the lowest level SQUID sensor signals (averaged) with red cursors at time instances of MR images in Figure 6 and green cursors for the time instances of the following maps of the z-component of the MCG signals.
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Figure 9: MCG signals. Butterfly plot of the lowest level SQUID sensor signals (averaged) with red cursors at time instances of MR images in Figure 6 and green cursors for the time instances of the following maps of the z-component of the MCG signals.

Mentions: The butterfly plot of the MCG is shown in Figure 9 which displays the signals of only 49 channels of the 304 channel system. These stem from the lowest layer of SQUID sensors which measure the z component of the magnetic induction. Figure 9 demonstrates the excellent signal quality of MCG signals. Finally, in Figure 10, the respective MCG maps are shown that correspond to the simultaneously acquired BSPMs shown in Figure 8. The file provided with the attached file folder contains all signals of the system, thus allowing access to the full vector field information (Additional files 7 and 8).


A reference dataset for verifying numerical electrophysiological heart models.

Koch H, Bousseljot RD, Kosch O, Jahnke C, Paetsch I, Fleck E, Schnackenburg B - Biomed Eng Online (2011)

MCG signals. Butterfly plot of the lowest level SQUID sensor signals (averaged) with red cursors at time instances of MR images in Figure 6 and green cursors for the time instances of the following maps of the z-component of the MCG signals.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037925&req=5

Figure 9: MCG signals. Butterfly plot of the lowest level SQUID sensor signals (averaged) with red cursors at time instances of MR images in Figure 6 and green cursors for the time instances of the following maps of the z-component of the MCG signals.
Mentions: The butterfly plot of the MCG is shown in Figure 9 which displays the signals of only 49 channels of the 304 channel system. These stem from the lowest layer of SQUID sensors which measure the z component of the magnetic induction. Figure 9 demonstrates the excellent signal quality of MCG signals. Finally, in Figure 10, the respective MCG maps are shown that correspond to the simultaneously acquired BSPMs shown in Figure 8. The file provided with the attached file folder contains all signals of the system, thus allowing access to the full vector field information (Additional files 7 and 8).

Bottom Line: The latter were recorded simultaneously from the same individuals a few hours after the MRI sessions.A training dataset is made publicly available; datasets for blind testing will remain undisclosed.While the MRI data may provide a common input that can be applied to different numerical heart models, the verification and comparison of different models can be performed by comparing the measured biosignals with forward calculated signals from the models.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physikalisch-Technische Bundesanstalt, Abbestr, 2-12, 10587 Berlin, Germany. hans.koch@ptb.de

ABSTRACT

Background: The evaluation, verification and comparison of different numerical heart models are difficult without a commonly available database that could be utilized as a reference. Our aim was to compile an exemplary dataset.

Methods: The following methods were employed: Magnetic Resonance Imaging (MRI) of heart and torso, Body Surface Potential Maps (BSPM) and MagnetoCardioGraphy (MCG) maps. The latter were recorded simultaneously from the same individuals a few hours after the MRI sessions.

Results: A training dataset is made publicly available; datasets for blind testing will remain undisclosed.

Conclusions: While the MRI data may provide a common input that can be applied to different numerical heart models, the verification and comparison of different models can be performed by comparing the measured biosignals with forward calculated signals from the models.

Show MeSH