Fusion of edge detection and graph neural networks to classifying electrocardiogram signals

The analysis of electrocardiogram (ECG) signals are among the key factors in the diagnosis of cardiovascular diseases (CVDs). However, automatic processing of ECG in clinical practice is still restrained by the accuracy of existing algorithms. Deep learning methods have recently achieved striking success in a variety of task including predictive healthcare. Graph neural networks are a class of machine learning algorithms which can learn by directly extracting important information from graph-structured data, and perform prediction on unknown data. Such algorithms are suitable for mining complex graph data, deducing useful predictions. In this work, we present a Graph Neural Network (GNN) model trained in two datasets with more than 107,000 single -lead signal images extracted from laboratories of Boston's Beth Israel Hospital and of the Massachusetts Institute of Technology (MITBIH), and 1.5 million labeled exams analyzed by the Physikalisch-Technische Bundesanstalt (PTB). Our proposed GNN achieves promising performance, i.e., the results show that ECG classification based on GNNs using either single-lead or 12-lead setup is closer to the human-level in standard clinical practice. By several testing instances, the proposed approach obtains an accuracy of 1.0, thereby outperforming various state-of-the-art baselines by both databases with respect to effectiveness and timing efficiency. We anticipate that the approach can be deployed as a non-invasive pre-screening tool to assist doctors in real-time monitoring and performing their diagnosis activities.