The Electrocardiogram Classification Approach Based on Wavelet Transform and FOAGRNN

In order to improve the accuracy of ECG classification, an ECG classification method based on comp-osite characteristics and FOAGRNN is proposed. First of all, this method begins by using the Kernel Independen-t Component Analysis (KICA) to get the characteristic vector A from extracting the nonlinear feature of the EC-G signals, then, wavelet packet transform is used to analyze multi-scale decomposition of ECG signals, ex-tracting the normalized enery which is reconstructed from wavelet packet node coefficient to compose the charact-erristic vector B, acting the compound eigenvector C which is composed by A and B as the ECG characteristic-s. Meanwhile, the fruit fly algorithm (FOA) was used to optimize the generalized regression neural network para-meter to construct the FOAGRNN model. Finally, identifying and classifying the ECG characteristics by the opti-mized classification model. The simulation results show that the classification accuracy of FOAGRNN classificati-on method is so higher compared with other methods, the classification accuracy can reach 99.0%.