@article{Mou2026, 
author = {Qiuxiao Mou and Haoyu Gui and Xianghong Tang and Jianguang Lu},
title = {MCCGAA: Multimodal Channel Compression Graph Attention Alignment Network for ECG Zero-Shot Classification},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {3},
keywords = {ECG zero-shot classification, contrastive learning, cross-modal alignment, graph attention network, channel attention mechanism},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.076251},
doi = {10.32604/cmc.2026.076251},
abstract = {Electrocardiogram (ECG) is a widely used non-invasive tool for diagnosing cardiovascular diseases. ECG zero-shot classification involves pre-training a model on a large dataset to classify unknown disease categories. However, existing ECG feature extraction networks often neglect key lead signals and spatial topology dependencies during cross-modal alignment. To address these issues, we propose a multimodal channel compression graph attention alignment network (MCCGAA). MCCGAA incorporates a channel attention module (CAM) to effectively integrate key lead features and a graph attention-based alignment network to capture spatial dependencies, enhancing cross-modal alignment. Additionally, MCCGAA employs a log-sum-exp loss function, improving classification performance and convergence over the original clip-style method. Experimental results show that MCCGAA outperforms current methods, achieving the highest classification accuracy across six publicly available datasets. MCCGAA holds promise for advancing ECG zero-shot classification and offering better decision support for researchers.}
}