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Related Concept Videos

Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations.

Jess D Tate1, Steffen Schuler2, Olaf Dössel2

  • 1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA.

Functional Imaging and Modeling of the Heart : ... International Workshop, FIMH ..., Proceedings. FIMH
|December 5, 2019
PubMed
Summary
This summary is machine-generated.

Spatial interpolation significantly improves electrocardiographic imaging (ECGI) forward simulations by reducing errors from cardiac source undersampling. This technique enhances accuracy and lowers computational costs for ECGI applications.

Keywords:
Activation timesBody surface potentialsBoundary element methodECG forward simulationEquivalent double layerSpatial interpolation

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Area of Science:

  • Computational electrocardiology
  • Biomedical modeling

Background:

  • Electrocardiographic Imaging (ECGI) relies on accurate ECG forward simulations for cardiac activity calculation.
  • Current ECG forward simulations lack standardized guidelines for cardiac source sampling resolution.

Purpose of the Study:

  • To evaluate equivalent double layer (EDL) forward simulations with varying cardiac source resolutions and spatial interpolation techniques.
  • To minimize errors caused by cardiac source undersampling and establish guidelines for ECG forward simulations.

Main Methods:

  • Utilized a simulated dataset sampled at five spatial resolutions.
  • Computed body surface potentials using an EDL forward simulation pipeline.
  • Tested triangle weighting and triangle splitting as spatial interpolation methods.

Main Results:

  • Low cardiac source resolution introduced high-frequency artifacts and shifted extrema locations in predicted ECG signals.
  • Spatial interpolation effectively mitigated errors in predicted potentials.
  • Spatial interpolation reduced the required nodes for accurate body surface potentials from 9,218 to 2,306.

Conclusions:

  • Spatial interpolation is crucial for accurate and computationally efficient ECG forward modeling.
  • Interpolation methods can overcome limitations of low-resolution cardiac source data.
  • Findings provide guidelines for optimizing source sampling in ECGI simulations.