GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention
This addresses the challenge of interpreting complex ECG signals for cardiovascular disease diagnosis, representing a strong domain-specific advancement.
This paper tackles accurate ECG classification by introducing GAF-FusionNet, a multimodal framework that integrates time-series analysis with image-based representation using Gramian Angular Fields and a split attention module. The model achieved 94.5%, 96.9%, and 99.6% accuracy on three ECG datasets, showing significant improvements over state-of-the-art methods.
Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GAF-FusionNet) for ECG classification that integrates time-series analysis with image-based representation using Gramian Angular Fields (GAF). Our approach employs a dual-layer cross-channel split attention module to adaptively fuse temporal and spatial features, enabling nuanced integration of complementary information. We evaluate GAF-FusionNet on three diverse ECG datasets: ECG200, ECG5000, and the MIT-BIH Arrhythmia Database. Results demonstrate significant improvements over state-of-the-art methods, with our model achieving 94.5\%, 96.9\%, and 99.6\% accuracy on the respective datasets. Our code will soon be available at https://github.com/Cross-Innovation-Lab/GAF-FusionNet.git.