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.

本研究通过使用深度图神经网络提出了三种区分心电图信号的技术，通过从心电图信号中提取拓扑特征并使用图同构网络来对心电图进行分类，三种技术在 PTB 诊断数据集上分别取得了 99.38％、98.76％和 91.93％的心律失常分类准确率。

ECG 分类中的拓扑特征提取图神经网络

Graph Neural Networks for Topological Feature Extraction in ECG  Classification

Electrocardiogram (ECG), a technique for medical monitoring of cardiac
activity, is an important method for identifying cardiovascular disease.
However, analyzing the increasing quantity of ECG data consumes a lot of
medical resources. This paper explores an effective algorithm for automatic
classifications of multi-classes of heartbeat types based on ECG. Most neural
network based methods target the individual heartbeats, ignoring the secrets
embedded in the temporal sequence. And the ECG signal has temporal variation
and unique individual characteristics, which means that the same type of ECG
signal varies among patients under different physical conditions. A two-stream
architecture is used in this paper and presents an enhanced version of ECG
recognition based on this. The architecture achieves classification of holistic
ECG signal and individual heartbeat and incorporates identified and temporal
stream networks. Identified networks are used to extract features of individual
heartbeats, while temporal networks aim to extract temporal correlations
between heartbeats. Results on the MIT-BIH Arrhythmia Database demonstrate that
the proposed algorithm performs an accuracy of 99.38\%. In addition, the
proposed algorithm reaches an 88.07\% positive accuracy on massive data in real
life, showing that the proposed algorithm can efficiently categorize different
classes of heartbeat with high diagnostic performance.

本文探讨一种基于 ECG 并采用两个流体系结构的神经网络算法，旨在能够高效地自动分类不同类别的心跳信号类型，实验结果表明，该算法在 MIT-BIH 心律失常数据库上的准确率达到了 99.38％，正面诊断准确率达到了 88.07％。