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

Yu-xia Guan; Ying AN; Feng-yi Guo; Wei-bai Pan; Jian-xin Wang

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

Author: Yu-Xia GUAN ¹ ; Ying AN ² ; Feng-Yi GUO ¹ ; Wei-Bai PAN ¹ ; Jian-Xin WANG ¹
Author Information

1. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
2. Big Data Institute, Central South University, Changsha 410083, China.
Publication Type:Journal Article
Keywords: Electrocardiogram; database; machine learning; medical big data analysis; preprocessing
MeSH: Humans; Arrhythmias, Cardiac/diagnosis*; Electrocardiography/methods*; Algorithms
From: Chinese Medical Sciences Journal 2023;38(1):38-48
CountryChina
Language:English
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.