Qi Luo
ABSTRACT
Atrial fibrillation is one of the inducing factors of many cardiac abnormalities. Currently, most atrial fibrillation classification methods only use single-lead electrocardiogram data or validate the effectiveness of the algorithm on small-scale datasets. The classification method based on single-lead atrial fibrillation ignores the complementary characteristics of different lead disease information in multi-lead electrocardiograms. If only small-scale datasets are used, the generalization ability of the algorithm cannot be guaranteed. Therefore, a multi-lead classification model for atrial fibrillation is proposed in this paper, which combines ConvNeXt, a convolutional block attention mechanism and a bidirectional long short-term memory network. Based on the characteristics and positional representation of atrial fibrillation on the electrocardiogram, a convolutional block attention mechanism is embedded in the model, enabling the model to effectively extract temporal and spatial features of the electrocardiogram during training. The specificity, sensitivity, accuracy and F1 scores obtained by this method were 98.90%, 98.04%, 98.88% and 74.32%, respectively, through testing more than 150,000 ECG recordings on the Chinese cardiovascular disease database. The experimental results indicate that this method achieves good classification performance and can provide effective technical means for the prevention and auxiliary diagnosis of atrial fibrillation.
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Index Terms
- Multi-lead atrial fibrillation classification method using ConvNeXt and BiLSTM
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