Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications

Yu-Xia Guan; Ying An; Feng-Yi Guo; Wei-Bai Pan; Jian-Xin Wang

doi:10.24920/004160

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Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications

Chinese Medical Sciences Journal Vol. 38, Issue 1, Pages: 38-48(2023)

Review | Updated：2024-03-30

- Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications
- Affiliations：
  
  1. 1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
  2. 2Big Data Institute, Central South University, Changsha 410083, China
- Author bio：
- Funds：
- DOI：10.24920/004160
  CLC：
- Received：26 August 2022，
  
  Accepted：2022-12-22，
  
  Published Online：24 February 2023，
  
  Published：31 March 2023
- Accepted：
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Yu-Xia Guan, Ying An, Feng-Yi Guo, et al. Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications[J]. Chinese medical sciences journal, 2023, 38(1): 38-48.
DOI：

Yu-Xia Guan, Ying An, Feng-Yi Guo, et al. Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications[J]. Chinese medical sciences journal, 2023, 38(1): 38-48. DOI： 10.24920/004160.

摘要

心电图（electrocardiogram，ECG）是一种低成本、简单、快速、无创的检查方法。它可以反映心脏的电活动，为整个身体的健康状况提供有价值的诊断线索。因此，ECG已广泛应用于各种生物医学领域，如心律失常检测、疾病特异性检测、死亡率预测、生物特征识别等。近年来，ECG相关的研究使用了各种公开可用的数据集，在使用的数据集、数据预处理方法、有针对性的挑战以及建模和分析技术方面存在许多差异。本文系统地总结和分析了基于心电图的自动分析方法和应用。具体而言，我们首先回顾了部分常用的ECG公共数据集，并概述了数据预处理过程。然后，我们介绍了心电信号的一些最广泛的应用，并分析了这些应用中所涉及的先进方法。最后，我们阐述了心电图分析的一些挑战，并提出了进一步研究的建议。

Abstract

Electrocardiogram (ECG) is a low-cost

simple

fast

and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body. Therefore

ECG has been widely used in various biomedical applications such as arrhythmia detection

disease-specific detection

mortality prediction

and biometric recognition. In recent years

ECG-related studies have been carried out using a variety of publicly available datasets

with many differences in the datasets used

data preprocessing methods

targeted challenges

and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically

we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally

we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

关键词

Keywords

references

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