Key result
CCA-DSC ResGNet algorithm outperforms existing approaches in ECG signal classification accuracy.
Why the study?
Does the CCA-DSC ResGNet algorithm improve classification accuracy and F1-score of abnormal heartbeats in ECG signals compared to existing approaches?
Does the CCA-DSC ResGNet algorithm improve classification accuracy and F1-score of abnormal heartbeats in ECG signals compared to existing approaches?
The CCA-DSC ResGNet algorithm improves the classification accuracy and F1-score of abnormal heartbeats in ECG signals compared to existing methods.
With the widespread application of deep learning in biomedical signal processing, electrocardiogram (ECG) signal classification methods have continued to evolve. However, existing approaches still face challenges such as high computational complexity and insufficient capability to capture long-range dependencies. This paper proposes an ECG classification algorithm based on criss-cross attention and depthwise separable convolution residual gated network (CCA-DSC ResGNet). The proposed method leverages criss-cross attention (CCA) to enhance global information modeling, employs depthwise separable convolution (DSC) to reduce computational complexity, and integrates residual network (ResNet) to mitigate gradient vanishing issues. Additionally, gated recurrent units (GRU) are incorporated to model temporal dependencies, thereby improving the classification performance of abnormal heartbeats. Experimental results on the MIT-BIH Arrhythmia Database demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of classification accuracy and F1-score, validating its effectiveness and feasibility.
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Wang et al. (2025) studied Arrhythmia. CCA-DSC ResGNet algorithm vs. Existing state-of-the-art approaches was evaluated on Classification accuracy and F1-score. The proposed CCA-DSC ResGNet algorithm outperformed existing state-of-the-art approaches in ECG signal classification accuracy and F1-score on the MIT-BIH Arrhythmia Database.
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