A Heart Sound Classification Method Based on Residual Block and Attention Mechanism

Yujie Chen, Wenliang Zhu, Jinke Xu, Junwei Zhang, Zhanpeng Zhu, Lirong Wang
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Abstract

The automatic diagnosis of heart sounds is particularly important for cardiologists. However, the existing diagnostic methods still have a large space to be improved, In this paper, we proposed a novel method for heart sound classification. Our method consists of two stages. In the first stage, we preprocessed the heart sound signal, including two steps of denoising and downsampling, to reduce the noise and decrease the complexity of processing. In the second stage, we classify the processed signal, including framing and input network, and finally output three types of results. Our method was validated on the CirCor DigiScope Phonocardiogram Dataset. The result shows the F1 score reached 0.922 and is better compared to other networks’ results.
基于残块和注意机制的心音分类方法
心音的自动诊断对心脏病专家来说尤为重要。然而,现有的心音诊断方法仍有很大的改进空间,本文提出了一种新的心音分类方法。我们的方法包括两个阶段。在第一阶段,我们对心音信号进行预处理,包括去噪和降采样两个步骤,以减少噪声,降低处理的复杂度。在第二阶段,我们对处理后的信号进行分类,包括分帧和输入网络,最后输出三种结果。我们的方法在CirCor DigiScope心音图数据集上得到了验证。结果表明,F1得分达到0.922,优于其他网络的结果。
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