Key result
This review systematically summarizes 22 commonly used public ECG datasets and analyzes advanced methods for automatic ECG analysis in applications like arrhythmia detection and mortality prediction.
Why the study?
ECG-related studies have used varied public datasets with substantial differences in preprocessing, targeted challenges, and modeling techniques, warranting a systematic overview.
This review provides a comprehensive overview of intelligent ECG analysis, including datasets, preprocessing, and applications, serving as a resource for future research in automated ECG interpretation.
Answers are grounded in this paper and cite their sources.
ECG applications span arrhythmia detection to biometrics; leaves open standardized validation across datasets before clinical adoption.
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.
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Guan et al. (2023) conducted a review in Electrocardiogram (ECG) analysis. Intelligent Electrocardiogram (ECG) Analysis was evaluated. This review systematically summarizes 22 commonly used public ECG datasets and analyzes advanced methods for automatic ECG analysis in applications like arrhythmia detection and mortality prediction.
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