Abstract
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
Highlights
Cardiovascular disease (CVD) is the leading cause of death in the world, according to the statistics of the World Health Organization
Removal of muscle artifacts is quite challenging without distorting the clinical features, which is essential for recognizing various ECG arrhythmia [24]
The results showed that the grey spectral noise cancellation (GSNC) scheme was superior to Empirical mode decomposition (EMD) and Ensemble empirical mode decomposition (EEMD) methods when tested on the MIT-BIH database where different signal-noise-ratio levels for the power-line interference (PLI) and EMG noise were considered
Summary
Cardiovascular disease (CVD) is the leading cause of death in the world, according to the statistics of the World Health Organization. With the advancement of algorithm and physical hardware technology, automated diagnostic systems become to play an increasingly important role in the diagnosis of heart disease, transitioning from selecting potentially effective lesion features for doctors to independent decision making. Without using any hand-crafted techniques, ECG analysis based on end-to-end model has great advantages in accuracy and robustness. The procedure of ECG signals analysis based on machine learning is discussed from data preprocessing, feature extraction and selection, classification, and application. End-to-End models based on deep learning algorithms for ECG analysis have been summarized, which enable the analysis process no longer to require a feature extraction with hand-crafted techniques. We discuss the development trends and challenges of computational diagnostic techniques for ECG analysis, which demonstrates great potential of ECG-assisted analysis in health diagnosis
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