ECG Signal Classification Based on Deep Learning

Abstract

Abstract: Electrocardiogram (ECG) can reflect the state of the heart in real time, and can be used for the accurate diagnosis of arrhythmias and other cardiovascular diseases. In view of the noise interference during ECG signal acquisition, we reconstruct the fourth-order components of Db6 wavelet, then use Butterworth low pass filter to realize double denoising. Then, from denoised ECG signals to extract the R-wave, and the P-QRS-T are intercepted and input into the one-dimensional improved GoogLeNet model for training. One-dimensional improved GoogLeNet is an improved structure of the original two-dimensional GoogLeNet, which reduces the network depth and adds the maximum pooled layer and dilated convolution in the sparse connection to increase the receptive field, so as to reduce the amount of calculation and improve the training performance. Experiments on the MIT-BIH data set show that the classification accuracy is 99.39%, which is 0.17 percentage points and 0.22 percentage points higher than the one-dimensional GoogLeNet and the original GoogLeNet respectively, and the training efficiency is improved. Signal classification has a marked improvement over other advanced techniques.

Key words: classification of arrhythmias, Db6 wavelet, low pass filtering, one-dimensional improved GoogLeNet, dilated convolution

YU Yan, QIU Lei, . ECG Signal Classification Based on Deep Learning[J]. Computer and Modernization, 2022, 0(05): 16-20.

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