Honest-GE: 2-step heuristic optimization and node-level embedding empower spatial-temporal graph model for ECG

Graph-based models for Electrocardiogram (ECG) incorporate physiological spatial information among ECG leads in elegant manners. It is unachievable for convolution-based models. Nevertheless, existing ECG graph models heavily rely on manually pre-designed structures. Such structures may not be optimal for all ECGs. Moreover, these models lag behind larger representative models, despite their superiority in lightweight cases. In this study, a novel graph structure learning method is proposed. It can learn optimal structures and enhance model representations simultaneously. Typically, ECGs belong to limb/chest lead systems. To explore inter-system and intra-system connections, heuristic optimization including genetic algorithm and particle swarm optimization is employed. Specifically, candidate nodes/edges are encoded as individuals. Thereby graph structure learning is transformed into population iterations. In this scenario, graph-level embedding becomes non-applicable because models require retraining once structures change. Instead, node-level embedding continuously enhances models during structure learning. Consequently, Honest-GE further exploits graph methods' superiority in lightweight cases. It outperforms state-of-the-art models 11.53% and 10.03% in F1 on two databases. Additionally, it demonstrates comparable performance with larger models. In general, graph learning results corroborate medical knowledge and offer insights for lead selection. Honest-GE provides a promising avenue for high-performing lightweight models and portable deployments with fewer leads. •Heuristic algorithms are applied for Electrocardiogram graph structure learning.•Node-embedding GraphSAGE is employed to enhance model continuously in searching.•Honest-GE further shows superiority over existing methods with similar parameters.•Honest-GE achieves competent performance against representative large models.•Result corroborate with medical knowledge and provide advice for lead selection.