Heart auscultations are a low-cost and effective way of detecting valvular heart diseases early, which can save lives. Nevertheless, it has been difficult to scale this screening method since the effectiveness of auscultations is dependent on the skill of doctors. As such, there has been increasing research interest in the automatic classification of heart sounds using deep learning algorithms. However, it is currently difficult to develop good heart sound classification models due to the limited data available for training. In this work, we propose a simple time domain approach, to the heart sound classification problem with a base classification error rate of 0.8 and show that augmentation of the data through codec simulation can improve the classification error rate to 0.2. With data augmentation, our approach outperforms the existing time-domain CNN-BiLSTM baseline model. Critically, our experiments show that codec data augmentation is effective in getting around the data limitation.

翻译：心脏听诊是一种低成本且有效的早期检测瓣膜性心脏病的方法，可以挽救生命。然而，由于听诊的有效性依赖于医生的技能，这种筛查方法难以大规模推广。因此，近年来利用深度学习算法自动分类心音的研究兴趣日益增长。然而，由于可用于训练的数据有限，目前开发良好的心音分类模型仍存在困难。在这项工作中，我们提出了一种简单的时域方法来解决心音分类问题，基础分类错误率为0.8，并表明通过编解码器模拟进行数据增强可以将分类错误率降低至0.2。采用数据增强后，我们的方法优于现有的时域CNN-BiLSTM基线模型。重要的是，我们的实验表明，编解码器数据增强能有效规避数据限制问题。