# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 2 Materials and Methods
### Classifier Model Architecture and Experiment  
In this study, we proposed an ECG-signal multi-classification model using the SE-ResNet. The SE-ResNet focuses on the interdependencies between the channels of its convolutional features, instead of investigating the spatial information. The SE block consists of a squeeze operation, which summarizes the overall information about each feature map, and an excitation operation, which scales the importance of each feature map. In this way, the squeeze operation extracts only the important information from each channel, using global average pooling, and the excitation operation computes the inter-channel dependencies, using a fully connected layer and a nonlinear function. The SE-ResNet is considered to be one of the most popular of the many CNN architectures because of its high performance on ImageNet for image classification. In addition, an SE network is easy to apply because it simply adds an SE block without changing the shape of the existing model.  
The main difference between the proposed network and the original SE-ResNet on ImageNet is that the proposed network uses 1D convolutions instead of 2D convolutions. We modified the original SE-ResNet by changing the input and output from \(224\times 224\times 3\) and \(1000\) to \(1\times 2000\times 1\) and seven (classes). The architectures of the SE-ResNet and ResNet for all the layers (18/34/50/101/152) used in our experiment are shown in Figure 1.  
To verify the performance of our model using the SE-ResNet, we chose the the ResNet used in [5; 6] as the baseline model. The ResNet is known as one of the best models for ECG-signal multi-classification. Specifically, we used a modified ResNet [18], which uses pre-activated weight layers, instead of the  
Figure 1: The architecture of the SE-ResNet (**a**) and the ResNet (**b**). Detailed parameters and a detailed structure description (**c**). Conv(m) means m \({}^{*}\) m convolution.  
original ResNet [19], which uses post-activated weight layers, because the modified ResNet has better performance than the original network.  
We evaluated our model on the lead-II ECG signal dataset measured in the Korea University Anam Hospital; it consists of seven classes. We considered 28,308 10 s ECG signals. We first split our lead-II ECG dataset into two, a training dataset and a test dataset, in the ratio of 8:2. After this, we set aside the test dataset and chose 80% of the training dataset to be the actual training dataset and the remaining 20% to be the validation dataset. In order to divide the dataset, we randomly selected each dataset; however, we fixed the random seed to compare the results. In this way, 64% of our total dataset was used to train the network, 16% was used to validate the model, and the remaining 20% was used to test the model. The number of training samples was 18,116. For the validation and test of the ECG signals, 4530 and 5662 samples were used, respectively.  
In order to optimize the model, we selected the Adam optimizer presented by [PERSON] et al. [20], with an initial learning rate of 0.0001. The training process continued until the validation loss did not decrease for a certain step. Similar to other deep-learning classification models, we used categorical cross entropy as a loss function.  
This retrospective study was approved by the Institutional Review Board of Korea University Anam Hospital on 12 February 2018 (2018 AN0037). Informed consent was waived by the IRB, given that data were de-identified.