A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysis
Journal:
arXiv
Published Date:
Apr 13, 2025
Abstract
Cardiovascular diseases remain the leading cause of global mortality,
emphasizing the critical need for efficient diagnostic tools such as
electrocardiograms (ECGs). Recent advancements in deep learning, particularly
transformers, have revolutionized ECG analysis by capturing detailed waveform
features as well as global rhythm patterns. However, traditional transformers
struggle to effectively capture local morphological features that are critical
for accurate ECG interpretation. We propose a novel Local-Global Attention ECG
model (LGA-ECG) to address this limitation, integrating convolutional inductive
biases with global self-attention mechanisms. Our approach extracts queries by
averaging embeddings obtained from overlapping convolutional windows, enabling
fine-grained morphological analysis, while simultaneously modeling global
context through attention to keys and values derived from the entire sequence.
Experiments conducted on the CODE-15 dataset demonstrate that LGA-ECG
outperforms state-of-the-art models and ablation studies validate the
effectiveness of the local-global attention strategy. By capturing the
hierarchical temporal dependencies and morphological patterns in ECG signals,
this new design showcases its potential for clinical deployment with robust
automated ECG classification.
