A hybrid graph convolutional network and convolutional neural network model achieved 99.44% and 96.16% accuracy in classifying cardiovascular disease from heart sound signals across two databases.
Does a hybrid GCN-CNN deep learning model improve the accuracy of cardiovascular disease recognition from heart sound signals compared to previous approaches?
A hybrid deep learning model combining GCN and CNN architectures achieves high accuracy in detecting cardiovascular disease from phonocardiogram signals.
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.
Rezaee et al. (Fri,) conducted a other in Cardiovascular disease. Hybrid GCN-CNN deep feature learning model vs. Previous approaches was evaluated on CVD classification accuracy. A hybrid graph convolutional network and convolutional neural network model achieved 99.44% and 96.16% accuracy in classifying cardiovascular disease from heart sound signals across two databases.