Graph Neural Networks for Topological Feature Extraction in ECG
  Classification
Kamyar Zeinalipour Marco Gori
The electrocardiogram (ECG) is a dependable instrument for assessing the
function of the cardiovascular system. There has recently been much emphasis on
precisely classifying ECGs. While ECG situations have numerous similarities,
little attention has been paid to categorizing ECGs using graph neural
networks. In this study, we offer three distinct techniques for classifying
heartbeats using deep graph neural networks to classify the ECG signals
accurately. We suggest using different methods to extract topological features
from the ECG signal and then using a branch of the graph neural network named
graph isomorphism network for classifying the ECGs. On the PTB Diagnostics data
set, we tested the three proposed techniques. According to the findings, the
three proposed techniques are capable of making arrhythmia classification
predictions with the accuracy of 99.38, 98.76, and 91.93 percent, respectively.