Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction
Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction
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DOI：
10.1080/24725579.2021.1982081
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发表时间：
2021-09
影响因子：
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通讯作者：
Xinyu Zhao;Hao Yan;Zhiyong Hu;D. Du
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文献类型：
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作者：
Xinyu Zhao;Hao Yan;Zhiyong Hu;D. Du
摘要心脏组织间的电传导通常用偏微分方程来建模，即，反应-扩散方程，其中反应项描述细胞刺激，扩散项描述电传播。在这种非线性动态系统中检测和识别产生异常电脉冲的心脏细胞对于有效的治疗和规划是重要的。为了建立非线性动力学模型，仿真已被广泛用于心脏研究和临床研究，以研究心脏疾病的机制和开发新的治疗设计。然而，现有的心脏模型具有很大的复杂性，并且仿真通常是耗时的。我们提出了一种深度时空稀疏分解（DSTSD）方法，以利用深度时空模型绕过耗时的心脏偏微分方程，并检测异常的时间和位置（即，功能障碍的心脏细胞）。这种方法是从Courtemanche-Ramirez-Nattel（CRN）模型生成的数据集进行验证的，CRN模型广泛用于模拟跨神经元膜跨膜电位的传播。建议的DSTSD达到最好的时空平均趋势预测和异常检测的准确性。
Abstract Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i.e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation. Detecting and identifying of cardiac cells that produce abnormal electrical impulses in such nonlinear dynamic systems are important for efficient treatment and planning. To model the nonlinear dynamics, simulation has been widely used in both cardiac research and clinical study to investigate cardiac disease mechanisms and develop new treatment designs. However, existing cardiac models have a great level of complexity, and the simulation is often time-consuming. We propose a deep spatio-temporal sparse decomposition (DSTSD) approach to bypass the time-consuming cardiac partial differential equations with the deep spatio-temporal model and detect the time and location of the anomaly (i.e., malfunctioning cardiac cells). This approach is validated from the data set generated from the Courtemanche-Ramirez-Nattel (CRN) model, which is widely used to model the propagation of the transmembrane potential across the cross neuron membrane. The proposed DSTSD achieved the best accuracy in terms of spatio-temporal mean trend prediction and anomaly detection.