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

TL;DR

This paper introduces a deep spatio-temporal sparse decomposition method to efficiently predict cardiac electrical activity and detect anomalies, bypassing complex PDE simulations with a data-driven deep learning approach.

Contribution

The paper presents a novel deep spatio-temporal model that accurately predicts cardiac electrical trends and detects anomalies without relying on time-consuming PDE simulations.

Findings

Achieved high accuracy in trend prediction and anomaly detection

Validated on CRN model data set

Outperformed existing methods in efficiency and accuracy

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.