# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 3 Related Theory
### GoogLeNet Model  
Based on the mechanism of convolutional neural networks, many researchers have designed convolutional neural network models for specific problems, such as GoogLeNet, AlexNet, VGGNet, ResNet, and so on. The GoogLeNet model proposed by the Google team is a relatively successful convolutional neural network model. The model has a total of 22 layers of network structure. In the model, in addition to the convolution layer, pooling layer, and full connection layer, there is also the inception structure proposed by the Google team. Through the convolutional kernels of different scales in the inception structure, different image features can be extracted. The GoogLeNet model has achieved excellent results in the field of image recognition with its multi-layer convolutional neural network structure and inception structure.  
The core architecture of the GoogLeNet network model has nine inception modules. The initial inception v1 network consists of three convolutional layers of size \(1\times 1\), \(3\times 3\), \(5\times 5\), and a \(3\times 3\) pooling layer (as shown in Figure 4). This structure processes the input images in parallel, and then splices each output result according to the channel, which is used to increase the width of the network and the adaptability of the network to the scale. If the input image has more channels, the convolutional kernel parameters will be more. The computation amount will increase accordingly, so it is necessary to reduce the dimension of the data. Since the \(1\times 1\) convolution does not change the height and width of the image, it can reduce the number of channels. Therefore, \(1\times 1\) convolutional kernels are added before \(3\times 3\), \(5\times 5\) convolutional layers and after the \(3\times 3\) max pooling layer, respectively, to reduce the feature map thickness, as shown in Figure 5.  
Figure 4: Inception network module diagram.  
Figure 5: Inception network module diagram.  
As can be seen from Figure 5, this structure is different from the convolutional neural network of the earlier sequential structure. The difference in the size of the convolutional kernel makes the expressions of the model features more diversified, which greatly improves the ability of the model feature extraction. The GoogLeNet structure is shown in Figure 6. Due to the complex network structure of GoogLeNet, its structure is approximately represented by modules of different colors representing different network functions. The red module represents the pooling layer, the blue module represents the convolution layer and ReLU layer, the green module represents the splicing operation, the yellow module represents the Softmax activation function and the white modules at both ends represent the input and classified output. The red box represents different Inception structures and the green box represents the auxiliary classification structure. From this figure, it can be seen that the GoogLeNet structure contains many similar inception modules. A complex GoogLeNet model is formed by connecting these modules end-to-end [14; 15; 16; 17; 18].