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Electrical conduction among cardiac tissue is commonly modeled with partialdifferential equations, i.e., reaction-diffusion equation, where the reactionterm describes cellular stimulation and diffusion term describes electricalpropagation. Detecting and identifying of cardiac cells that produce abnormalelectrical impulses in such nonlinear dynamic systems are important forefficient treatment and planning. To model the nonlinear dynamics, simulationhas been widely used in both cardiac research and clinical study to investigatecardiac disease mechanisms and develop new treatment designs. However, existingcardiac models have a great level of complexity, and the simulation is oftentime-consuming. We propose a deep spatio-temporal sparse decomposition (DSTSD)approach to bypass the time-consuming cardiac partial differential equationswith the deep spatio-temporal model and detect the time and location of theanomaly (i.e., malfunctioning cardiac cells). This approach is validated fromthe data set generated from the Courtemanche-Ramirez-Nattel (CRN) model, whichis widely used to model the propagation of the transmembrane potential acrossthe cross neuron membrane. The proposed DSTSD achieved the best accuracy interms of spatio-temporal mean trend prediction and anomaly detection.

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