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- W2899043459 abstract "the aim of this proof-of-concept work was to apply the spatiotemporal Kalman filter (STKF) algorithm to magnetocardiographic (MCG) recordings of the heart. Due to the lack of standardized software and pipelines for MCG source imaging, we needed to construct a pipeline for MCG forward modeling before we could apply the STKF method. In the forward module, the finite element method (FEM) solvers in SimBio software are used to solve the MCG forward problem. In the inverse module, STKF and Low Resolution Brain Electromagnetic Tomography (LORETA) algorithms are applied. The work was conducted using two simulated datasets contaminated with different levels of additive white Gaussian noise (AWGN). Then the inverse problem was solved using both LORETA and STKF. The results indicate that STKF outperformed LORETA for MCG datasets with low signal-to-noise ratio (SNR). In the future clinical MCG recordings and more sophisticated simulations will be used to evaluate the accuracy of MCG source imaging via STKF." @default.
- W2899043459 created "2018-11-09" @default.
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- W2899043459 date "2018-07-01" @default.
- W2899043459 modified "2023-09-26" @default.
- W2899043459 title "Pipeline for Forward Modeling and Source Imaging of Magnetocardiographic Recordings via Spatiotemporal Kalman Filtering" @default.
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- W2899043459 doi "https://doi.org/10.1109/embc.2018.8512188" @default.
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