Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction

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.

Keywords:

• Spatio-temporal analysis; sparse decoposition; anomaly detection; cardiac electrical simulation; deep learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by National Science Foundation under Grant DMS-1830363, CMMI-1646664, CMMI 1922739, and CMMI-1728338. Division of Civil, Mechanical and Manufacturing Innovation;Division of Mathematical Sciences.