Xinchao Han1,2, Aojun Zhang1,2, Runchuan Li1,2,*, Shengya Shen3, Di Zhang1,2, Bo Jin1,2, Longfei Mao1,2, Linqi Yang1,2, Shuqin Zhang1,2
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3443-3465, 2025, DOI:10.32604/cmc.2024.059403
- 17 February 2025
Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >
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