The high mortality rate and prevalence of cardiovascular disease (CVD) make early detection of the disease essential. Due to its simplicity and low cost, the phonocardiogram (PCG) system is widely used in healthcare applications for the recognition of CVD in multiclass problems. On the basis of the PCG signal, this paper proposes a hybrid method for classifying cardiac sounds with deep extracted features through two-step learning. For fine-grained features in Graph Convolutional Networks (GCNs), sampling and prior layers are employed. A PCG signal is divided into equal parts with overlap using the windowing process. L-spectrograms extract frequency-domain information from signals to figure out their power spectrum. Furthermore, the deep GCN tries to determine the association between CVD and spectrogram images to recognize CVD signals better. Combining retrieved features with convolutional neural network (CNN) characteristics reveals an image's intrinsic associations. To generate relational feature representations, correlations between clusters and GCN are visualized using a graph structure. CNN's discriminative ability has been enhanced by incorporating GCN attributes. Using Michigan Heart Sound and Murmur Database and PhysioNet/CinC 2016 Challenge results, we are 99.44% and 96.16% accurate, respectively. Through a combination of GCN architecture, CNN design, and deep features, the hybrid model significantly improves CVD classification accuracy. Measuring metrics demonstrate that the proposed approach detects CVD more effectively than previous approaches.

中文翻译：

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基于图卷积网络的深度特征学习，用于从心音信号中识别心血管疾病

心血管疾病 (CVD) 的高死亡率和流行率使得早期发现该疾病变得至关重要。由于其简单性和低成本，心音图 (PCG) 系统广泛用于医疗保健应用，以识别多类问题中的 CVD。本文在PCG信号的基础上，提出了一种通过两步学习对具有深度提取特征的心音进行分类的混合方法。对于图卷积网络 (GCN) 中的细粒度特征，采用了采样层和先验层。PCG 信号使用开窗过程被分成相等的部分并有重叠。大号-频谱图从信号中提取频域信息以计算出它们的功率谱。此外，深度 GCN 试图确定 CVD 和频谱图图像之间的关联，以更好地识别 CVD 信号。将检索到的特征与卷积神经网络 (CNN) 特征相结合可以揭示图像的内在关联。为了生成关系特征表示，集群和 GCN 之间的相关性使用图形结构进行可视化。通过合并 GCN 属性，CNN 的判别能力得到了增强。使用密歇根心音和杂音数据库以及 PhysioNet/CinC 2016 挑战赛结果，我们的准确率分别为 99.44% 和 96.16%。通过结合 GCN 架构、CNN 设计和深度特征，混合模型显着提高了 CVD 分类精度。