Chinese Medical Sciences Journal ›› 2023, Vol. 38 ›› Issue (1): 38-48.doi: 10.24920/004160

Special Issue: 心脏疾病与健康

• Review • Previous Articles     Next Articles

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

Yu-Xia Guan1, Ying An2, Feng-Yi Guo1, Wei-Bai Pan1, Jian-Xin Wang1, *()   

  1. 1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
    2Big Data Institute, Central South University, Changsha 410083, China
  • Received:2022-08-26 Accepted:2022-12-22 Published:2023-03-31 Online:2023-02-28
  • Contact: Jian-Xin Wang

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.

Key words: Electrocardiogram, database, preprocessing, machine learning, medical big data analysis

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