School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Physiol Meas. 2023 Sep 21;44(9). doi: 10.1088/1361-6579/acf3cf.
. To enhance the accuracy of heart sound classification, this study aims to overcome the limitations of common models which rely on handcrafted feature extraction. These traditional methods may distort or discard crucial pathological information within heart sounds due to their requirement of tedious parameter settings.We propose a learnable front-end based Efficient Channel Attention Network (ECA-Net) for heart sound classification. This novel approach optimizes the transformation of waveform-to-spectrogram, enabling adaptive feature extraction from heart sound signals without domain knowledge. The features are subsequently fed into an ECA-Net based convolutional recurrent neural network, which emphasizes informative features and suppresses irrelevant information. To address data imbalance, Focal loss is employed in our model.Using the well-known public PhysioNet challenge 2016 dataset, our method achieved a classification accuracy of 97.77%, outperforming the majority of previous studies and closely rivaling the best model with a difference of just 0.57%.The learnable front-end facilitates end-to-end training by replacing the conventional heart sound feature extraction module. This provides a novel and efficient approach for heart sound classification research and applications, enhancing the practical utility of end-to-end models in this field.
. 为了提高心音分类的准确性，本研究旨在克服常见模型的局限性，这些常见模型依赖于手工特征提取。由于需要繁琐的参数设置，这些传统方法可能会扭曲或丢弃心音中的关键病理信息。
我们提出了一种基于可学习前端的高效通道注意网络（ECA-Net）用于心音分类。这种新方法优化了从波形到频谱的转换，能够自适应地从心音信号中提取特征，而无需领域知识。然后将特征输入到基于 ECA-Net 的卷积递归神经网络中，该网络强调信息丰富的特征并抑制不相关的信息。为了解决数据不平衡问题，我们的模型采用了焦点损失。
使用著名的公共 PhysioNet 挑战 2016 数据集，我们的方法实现了 97.77%的分类准确率，优于大多数先前的研究，并与最好的模型仅相差 0.57%。
可学习前端通过替换传统的心音特征提取模块来实现端到端训练。这为心音分类研究和应用提供了一种新颖而有效的方法，增强了该领域端到端模型的实际效用。
