PM2ECGCN: Parallelized spatial-temporal structures of multi-lead ECG with graph convolution network for multi-center cardiac disease diagnosis

Abstract

Cardiovascular diseases (CVDs) are the leading global cause of death. As first-line diagnostic tool for CVDs, electrocardiograms (ECG) non-invasively assess cardiac condition across all clinical levels. Despite the strides made in automatic ECG analysis through artificial intelligence, current methods fail to utilize spatial–temporal interactions of multi-lead ECG, address multi-center data heterogeneity, and adapt to data security and computational constraints. This study proposed a graph neural network-based framework named PM2ECGCN responding to these challenges of multi-lead ECG from multi-center sources. PM2ECGCN independently extracts spatial–temporal dependencies with learning-based featurization on each isolated subset, and trains collaboratively under feature-sharing strategy that bridging isolated outcomes without exposing raw data. Benefiting from information fusion capacities of graph convolutional network with attention mechanism, PM2ECGCN enhances explicit topological representations of multi-lead ECG via parallelized cardiac graph structures, and fast spectral pooling provides linear inference complexity. PM2ECGCN promotes condition-invariant knowledge learning with domain adaptation for tackling the data heterogeneity of distrubution. Extensive experiments, conducted with various configurations under diverse scenarios, involve two public datasets, PTBXL and ICBEB, along with an external dataset from day-to-day clinical practice we collected. Empirical evaluations showcase outperformance over cutting-edge strategies for multi-center CVD diagnosis with consistent improvements, demonstrating potential for real-world clinical deployment.