ResNet deep network with stable step for multi-lead electrocardiogram recognition
When the classical ResNet deep neural network is used to recognize and classify the one-dimensional multi-lead ECG image,the high dimension of the original image leads to the high dimension of the deep feature extracted,which leads to the problems of slow convergence and over-fitting in the full connection layer training.In order to deal with this problem,a stable step momentum training algorithm is proposed in the full connection layer of ResNet,which enhances the optimization ability and accelerates the convergence speed of the momentum method by introducing approximate second-order gradient information.Firstly,the step size is adaptively adjusted by using the parameter variation and gradient information of two consecutive iterations,and then the boundary function is constructed to limit the step size to prevent the step size from being too large or too small to affect the convergence stability.Finally,the momentum term is used to modify the updated direction of the parameters.The experimental results on the CPSC2018 ECG dataset show that the F1 score,accuracy and accuracy of the ResNet trained by the proposed algorithm reach 0.859,97.4％and 87.9％,respectively,and the convergence speed and the overall classification index value are better than other comparative methods.
multi-lead electrocardiogramResNet deep networkmomentum optimization algorithmstable step sizesecond-order gradient information
万方数据
维普
