Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction

• URL: http://arxiv.org/abs/2109.09317v1
• Date: Mon, 20 Sep 2021 06:38:50 GMT
• Title: Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction
• Authors: Xinyu Zhao, Hao Yan, Zhiyong Hu, Dongping Du
• Abstract summary: We propose a deep-temporal decomposition (DSTSD) approach to bypass the time-consuming cardiac partial differential equations. This approach is validated from the data set generated from the Courtemanche-amirez-Nattel neuron (CRN) model.
• Score: 11.076265159072229
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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