## 3 Depth Residual Network  
The CNN (Convolutional Neural Network) is essentially a multi-layer perceptron. The key reason for its success lies in how it locally connects and shares weights [19]. On the one hand, the reduced number of weights makes the network easy to optimize; on the other hand, it reduces the risk of over fitting. The CNN uses a convolution layer, and this family of networks have several classical models. Whenever the convolution layer is established, the values in the convolution kernel are randomly generated. In addition to the convolution layer, the convolutional network often uses the pooling layer to reduce the size of the model and improve the computing speed and the robustness of the extracted features. Common pooling layers include max pooling and average pooling. One of these operations takes the maximum value in the convolution kernel, while the other one takes the average value [20]. Figure 2 shows the recognition process of convolutional networks. As shown in Figure 2, the processing results are worked out by substituting the processed data into the network, passing through the first hidden layer and activation function (ReLU) [21], and cycling this operation. Convolution operation is a common mathematical calculation. The convolution of the 2D matrix represents the result of the inner product between one matrix and another after rotating 180\({}^{\circ}\) on the plane. Among them, \"volume\" is rotation and \"product\" is the inner product, that is, weighted summation. This process can be expressed by mathematical formula:  
Figure 1: Schematic diagram of ECG beat. Figure 2: Convolution network structure. \[\left[ \begin{array}{ccccc}x_{11}&x_{22}&\cdots&x_{1n}\\ x_{21}&x_{22}&\cdots&x_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m1}&x_{m2}&\cdots&x_{mn}\end{array} \right]\ \ast\ \left[ \begin{array}{ccccc}y_{11}&y_{22}&\cdots&y_{1n}\\ y_{21}&y_{22}&\cdots&y_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ y_{m1}&y_{m2}&\cdots&y_{mn}\end{array} \right]=\sum_{i=0}^{m-1}\sum_{j=0}^{n-1 }x_{(m-i)(n-j)}y_{(1+i)(1+j)} \tag{1}\]  
In Equation (1), \(x\) represents the value of one matrix, \(y\) represents the value of another matrix, \(m\) is the quantity of rows, and \(n\) is the quantity of columns of the two matrices. If the corresponding position elements are multiplied and summed, the final result is the matrix convolution [22]. In the process of convolution, we usually choose to use smaller convolution kernels to reduce the number of parameters. That is, the size of Equation (1) \(m\) and \(n\) is usually very small. In this way, important features can be extracted through multiple scans of data, and the ability can be effectively increased through stacking of convolution layers. However, with the increase in network layers, the effect of the network model does not rise but fall, which is the so-called network \"degradation\". The emergence of the residual network solves this problem, such that the number of layers of the network can continue to deepen [23]. Figure 3 is a comparison diagram of the residual block and ordinary network. As shown in the above figure, the output of a residual unit is divided into two parts: one part is the input, which is output without any operation, and the other part is the fitting result output through the convolution structure in the residual unit. The final output is the sum of the two partial outputs, that is:  
\[\rm{H(x)=x+F(x)} \tag{2}\]  
where F(x) is the fitting result of the middle layer. The residual element is a structure that presents the jump connection in parallel with the ordinary convolution layer and adds their results. Compared with the simple convolution layer, the residual unit almost achieves zero burden to increase the network depth, and greatly improves the training and operation speed. The recognition and classification of ECG signals is the focus of this study. Based on its special data structure, the residual unit of 1D convolution is applied.