Key result
An ECG eigenvalue detection algorithm based on wavelet transform achieved an eigenvalue recognition accuracy of >99.27% and a heart disease detection accuracy of >86.7%.
A novel ECG eigenvalue detection algorithm based on wavelet transform demonstrated high accuracy in recognizing ECG features and detecting heart diseases such as myocardial ischemia and heart failure.
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Supports wavelet-based ECG algorithms; leaves open prospective validation before clinical adoption.
This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease. Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing. Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential. The experimental results show that the accuracy of ECG eigenvalue recognition is more than 99.27%, and the accuracy rate of detection of heart disease such as myocardial ischemia and heart failure is more than 86.7%.
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Peng et al. (2017) studied Heart disease (myocardial ischemia and heart failure). ECG eigenvalue detection algorithm based on wavelet transform was evaluated on Accuracy of ECG eigenvalue recognition and detection of heart disease. An ECG eigenvalue detection algorithm based on wavelet transform achieved an eigenvalue recognition accuracy of >99.27% and a heart disease detection accuracy of >86.7%.
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