@Article{cmc.2026.076251,
AUTHOR = {Qiuxiao Mou, Haoyu Gui, Xianghong Tang, Jianguang Lu},
TITLE = {MCCGAA: Multimodal Channel Compression Graph Attention Alignment Network for ECG Zero-Shot Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n3/66922},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2026.076251}
}