Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3149-3152. doi: 10.1109/EMBC46164.2021.9630377.
Noninvasive electrophysiological imaging plays an important role in the clinical diagnosis and treatment of heart diseases over recent years. Transmembrane potential (TMP) is one of the most important cardiac physiological signals, which can be used to diagnose heart disease such as premature beat and myocardial infarction. Considering the nonlocal self-similarity of TMP distribution and integrating traditional optimization strategy into deep learning, we proposed a novel global features based Fast Iterative Shrinkage/Thresholding network, named as GFISTA-Net. The proposed method has two main advantages over traditional methods, namely, the l1-norm regularization helps to avoid overfitting the model on high-dimensional but small-training data, and facilitates embedded the spatio-temporal correlation of TMP. Experiments demonstrate the power of our method.
近年来，无创电生理成像在心脏疾病的临床诊断和治疗中发挥着重要作用。跨膜电位（TMP）是心脏最重要的生理信号之一，可用于诊断早搏和心肌梗死等心脏病。考虑到 TMP 分布的非局部自相似性，并将传统优化策略集成到深度学习中，我们提出了一种新颖的基于全局特征的快速迭代收缩/阈值网络，命名为 GFISTA-Net。与传统方法相比，该方法具有两个主要优势，即 l1 范数正则化有助于避免对高维但小训练数据的过度拟合，并且有利于嵌入 TMP 的时空相关性。实验证明了我们方法的有效性。
