Fig. 3

The architecture of the convolutional neural network (CNN). a The 2-dimensional image data were processed by five network computer architectures, which included VGG16, ResNet50V2, InceptionV3, InceptionResNetV2, and Xception to get the best image recognition at the Image Net part of CNN and then flattened into Global Average Pooling (GAP). b The dense layer of the single input from the 2-dimensional image data was connected. The dropout was added to avoid overfitting (drop rate = 0.5) and another dense layer with size of two was added to get an output layer. VPC ventricular premature complex, NOR normal rhythm. c The signals of time-series data were extracted through the CNN layers and flattened by GAP. The output features of the single -input model was directly connected to dropout (dropout rate = 0.5) and the multiple-input model from the twelve channels’ features were merged (to get the output result (dense size = 2). GAP Global Average Pooling