GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention

• URL: http://arxiv.org/abs/2501.01960v1
• Date: Sat, 07 Dec 2024 07:02:16 GMT
• Title: GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention
• Authors: Jiahao Qin, Feng Liu,
• Abstract summary: This paper introduces a novel framework for ECG classification that integrates time-series analysis with image-based representation. We evaluate ECG-FusionNet on three diverse 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.
• Score: 4.673285689826945
• License:
• Abstract: 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.

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