Research On ECG Signal Classification Based On Improved Convolutional Neural Network

At present,cardiovascular disease has become one of the main diseases faced by mankind.As a visual physiological signal reflecting the condition of the heart,the ECG signal is of great significance to the prevention,diagnosis and treatment of cardiovascular diseases.Convolutional neural network has the advantage of integrating automatic feature extraction and high classification accuracy.Therefore,this paper studies the ECG signal classification method based on improved convolutional neural network.The specific research content is as follows:1.An ECG signal denoising method based on improved wavelet threshold and adaptive complete set empirical mode decomposition(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)is proposed.In order to eliminate the noise in the electrocardiography(ECG)signal,this method first performs CEEMDAN decomposition of the ECG signal to obtain a set of Intrinsic Mode Function(IMF)from high frequency to low frequency distribution,and then according to the correlation coefficient method,Perform wavelet denoising with improved threshold for high-frequency IMF components.For the low-frequency IMF components,by setting a fixed threshold,the IMF components below the threshold are considered to be baseline drift signals,and they are removed,and then the denoised IMF components and the retained IMF are reconstructed to obtain a clean ECG signal.2.Designed an ECG signal classification model based on an improved residual network,and replaced the convolutional layer and pooling layer in the traditional residual network with the Inception module to extract information features of different scale levels,and increase the network’s generalization ability to scales.At the same time,by nesting the residual network method in the residual network,the information characteristics of the bottom and high-level are fully integrated to further alleviate the interference of the gradient problem on the model,and improve the accuracy of the model’s classification of ECG signals.The classification test was performed on the MIT-BIH data set,and finally an overall accuracy rate of 95.1% was obtained.3.The ECG signal classification model based on the improved VGGNet network is designed.First,in order to make the VGG model more suitable for the four-category output,the fully connected layer structure is redesigned.Second,in order to compress the number of parameters in the network and improve the training efficiency,the deep separable convolution is introduced into the network model.Finally,because VGG can only extract the spatial characteristics of the ECG signal and ignore the timing characteristics,a Long Short-Term Memory(LSTM)is introduced to capture the timing information between the spatial characteristics of the ECG signal.The experimental results show that the improved network improves the classification accuracy while reducing the parameters,reaching an accuracy rate of 93.6%.