@article{Han2025, 
author = {Xinchao Han and Aojun Zhang and Runchuan Li and Shengya Shen and Di Zhang and Bo Jin and Longfei Mao and Linqi Yang and Shuqin Zhang},
title = {An Arrhythmia Intelligent Recognition Method Based on a Multimodal Information and Spatio-Temporal Hybrid Neural Network Model},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {82},
number = {2},
pages = {3443-3465},
keywords = {Multimodal learning, spatio-temporal hybrid, graph convolutional network, data imbalance, ECG classification},
url = {https://www.sciopen.com/article/10.32604/cmc.2024.059403},
doi = {10.32604/cmc.2024.059403},
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, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.}
}