Lung sounds convey valuable information relevant to human respiratory health. Therefore, it is important to classify lung sounds for early diagnoses of respiratory disorders. In recent years, computerized lung sound analysis with machine learning algorithms has attracted researchers, especially the state-of-the-art convolutional neural network (CNN). However, most of these algorithms require a large number of labeled respiratory sound samples, which is time- and cost-consuming. Based on a four-layers CNN, this study proposes graph semi-supervised CNNs (GS-CNNs), which can classify respiratory sounds into normal, crackle and wheeze ones with only a small labeled sample size and a large unlabeled sample size. The graph of respiratory sounds (Graph-RS) with labeled and unlabeled respiratory sound samples as vertexes is first constructed, which can indicate not only the reasonable metric information but also the relationship of all the samples. Then, GS-CNNs are developed by adding the information extracted from Graph-RS to the loss function of the original CNN. The added information enables the GS-CNNs to regulate the structure of the original CNN, thus enhancing classification accuracy. The GS-CNNs are evaluated by experiments with the samples collected by electronic stethoscope. Results demonstrate that the proposed GS-CNNs outperform the original CNN, and that the more information from Graph-RS is used, the better recognition effect will be achieved.

肺部声音传达了与人类呼吸系统健康相关的宝贵信息。因此，对肺音进行分类对于呼吸系统疾病的早期诊断非常重要。近年来，使用机器学习算法的计算机化肺音分析吸引了研究人员，尤其是最先进的卷积神经网络 (CNN)。然而，这些算法中的大多数都需要大量标记的呼吸音样本，这既费时又费钱。本研究基于四层 CNN，提出了图半监督 CNNs (GS-CNNs)，它可以将呼吸音分类为正常、噼啪声和喘息声，只有很小的标记样本量和很大的未标记样本量。首先构建以标记和未标记的呼吸音样本为顶点的呼吸音图（Graph-RS），它不仅可以指示合理的度量信息，还可以指示所有样本的关系。然后，通过将从 Graph-RS 提取的信息添加到原始 CNN 的损失函数中来开发 GS-CNN。添加的信息使 GS-CNN 能够调节原始 CNN 的结构，从而提高分类精度。通过对电子听诊器收集的样本进行实验来评估 GS-CNN。结果表明，所提出的 GS-CNN 优于原始 CNN，并且使用来自 Graph-RS 的信息越多，识别效果越好。添加的信息使 GS-CNN 能够调节原始 CNN 的结构，从而提高分类精度。通过对电子听诊器收集的样本进行实验来评估 GS-CNN。结果表明，所提出的 GS-CNN 优于原始 CNN，并且使用来自 Graph-RS 的信息越多，识别效果越好。添加的信息使 GS-CNN 能够调节原始 CNN 的结构，从而提高分类精度。通过对电子听诊器收集的样本进行实验来评估 GS-CNN。结果表明，所提出的 GS-CNN 优于原始 CNN，并且使用来自 Graph-RS 的信息越多，识别效果越好。