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The electrocardiogram (ECG) is a dependable instrument for assessing thefunction of the cardiovascular system. There has recently been much emphasis onprecisely classifying ECGs. While ECG situations have numerous similarities,little attention has been paid to categorizing ECGs using graph neuralnetworks. In this study, we offer three distinct techniques for classifyingheartbeats using deep graph neural networks to classify the ECG signalsaccurately. We suggest using different methods to extract topological featuresfrom the ECG signal and then using a branch of the graph neural network namedgraph isomorphism network for classifying the ECGs. On the PTB Diagnostics dataset, we tested the three proposed techniques. According to the findings, thethree proposed techniques are capable of making arrhythmia classificationpredictions with the accuracy of 99.38, 98.76, and 91.93 percent, respectively.

Graph Neural Networks for Topological Feature Extraction in ECG Classification