Abstract
Accurate reconstruction and imaging of cardiac transmembrane potential through body surface ECG signals can provide great help for the diagnosis of heart disease. In this paper, a cardiac transmembrane potential reconstruction method (GISTA-Net) based on graph convolutional neural network and iterative soft threshold algorithm is proposed. It fully combines the rigor of mathematical derivation of traditional iterative threshold shrinkage algorithm and the powerful expression ability of deep learning method, as well as the characterization ability of graph convolutional neural network for non-Euclidean space data. We used this algorithm to simulate ectopic pacing data and simulated myocardial infarction data. The experimental results show that this algorithm can not only accurately locate the ectopic pacing point, but also accurately reconstruct the edge details of myocardial infarction scar while graph convolution makes full use of the connection information between the nodes on the heart surface.
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Acknowledgement
This work is supported in part by the National Key Technology Research and Development Program of China (No: 2017YFE0104000, 2016YFC1300302), and by the National Natural Science Foundation of China (No: U1809204, 61525106, 61701436).
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Mu, L., Liu, H. (2021). Cardiac Transmembrane Potential Imaging with GCN Based Iterative Soft Threshold Network. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_53
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