Key result
An electrocardiogram pattern recognition method based on wavelet transform and a standard BP neural network classifier demonstrated a good overall pattern recognition rate for different kinds of ECGs.
An ECG pattern recognition method using wavelet transform and a BP neural network classifier achieves good recognition rates.
Should not yet alter ECG interpretation practice; leaves open prospective validation of wavelet-BP neural network classifiers.
ECG is the recording of the electrical activity of the heart, and has become one of the most important tools in the diagnosis of heart diseases. ECG signal is shaped by P wave, QRS complex, and T wave. In the normal ECG beat, the main parameters including shape, duration, R-R interval and relationship between P wave, QRS complex, and T wave components are inspected. Any change in these parameters indicates an illness of the heart. This article introduces an electrocardiogram (ECG) pattern recognition method based on wavelet transform and standard BP neural network classifier. Experiment analyzes wavelet transform of ECG to extract the maximum wavelet coefficients of multi-scale firstly. This article then inputs them into BP to classify for different kinds of ECGs. The experimental result shows that the standard BP neural network classifier's overall pattern recognition rate is well.
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Gupta et al. (2019) studied Heart disease. ECG pattern recognition method based on wavelet transform and standard BP neural network classifier was evaluated on Pattern recognition rate. An electrocardiogram pattern recognition method based on wavelet transform and a standard BP neural network classifier demonstrated a good overall pattern recognition rate for different kinds of ECGs.
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