通过时空卡尔曼滤波进行心磁图记录的正向建模和源成像的流程
Pipeline for Forward Modeling and Source Imaging of Magnetocardiographic Recordings via Spatiotemporal Kalman Filtering.
作者信息
Habboush Nawar, Hamid Laith, Siniatchkin Michael, Stephani Ulrich, Galka Andreas
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:199-202. doi: 10.1109/EMBC.2018.8512188.
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.
这项概念验证工作的目的是将时空卡尔曼滤波器（STKF）算法应用于心脏的磁心动图（MCG）记录。由于缺乏用于MCG源成像的标准化软件和流程，我们需要构建一个用于MCG正向建模的流程，然后才能应用STKF方法。在正向模块中，使用SimBio软件中的有限元方法（FEM）求解器来解决MCG正向问题。在反向模块中，应用了STKF和低分辨率脑电磁断层扫描（LORETA）算法。这项工作使用了两个被不同水平的加性高斯白噪声（AWGN）污染的模拟数据集进行。然后使用LORETA和STKF求解反问题。结果表明，对于低信噪比（SNR）的MCG数据集，STKF的性能优于LORETA。未来将使用临床MCG记录和更复杂的模拟来评估通过STKF进行MCG源成像的准确性。
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