# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
###### Abstract  
Cardiovascular diseases are the leading cause of death globally. The ECG is the most commonly used tool for diagnosing cardiovascular diseases, and, recently, there are a number of attempts to use deep learning to analyze ECG. In this study, we propose a method for performing multi-label classification on standard ECG (12-lead with duration of 10 s) data. We used the ResNet model that can perform residual learning as a base model for classification in this work, and we tried to improve performance through SE-ResNet, which added squeeze and excitation blocks on the plain ResNet. As a result of the experiment, it was possible to induce overall performance improvement through squeeze and excitation blocks. In addition, the random _k_-labelsets (RAKEL) algorithm was applied to improve the performance in multi-label classification problems. As a result, the model that applied soft voting through the RAKEL algorithm to SE-ResNet-34 represented the best performance, and the average performances according to the number of label divisions \(k\) were achieved 0.99%, 88.49%, 92.43%, 90.54%, and 93.40% in exact match, accuracy, F1-score, precision, and recall, respectively. computer aided diagnosis; ECG classification; multi-label classification; squeeze and excitation network +
Footnote †: journal: Medical  
were used to perform feature extraction, such as signal preprocessing and waveform detection. This feature extraction step is generally designed by experience and requires specific expertise [6]. However, recent advances in deep learning can perform tasks more efficiently than traditional methods without the need to feature extraction. Deep learning can be used to classify patterns, extract features to identify meaningful hidden information from data. In analyzing ECG, deep learning methods demonstrated better classification performance than previous traditional methods when trained with sufficient data [6]. There are many studies to use deep learning to analyze ECG [15]. However, most attempts were designed using single lead ECG, which remains the possibility of using more information from raw ECG data [16]. Clearly, there are investigations that performed the desired tasks with 12-lead (or multi-lead) ECG as inputs [17; 18]. Their common purpose is to predict and classify a single label, yet the PTB-XL ECG dataset [19] used in this study has a total of 5 superclasses, and since it can have various classes simultaneously, multi-label prediction is required. We used the k-label set method [20] to solve this problem, and convolutional neural network (CNN)-based model was used as a deep learning architecture for classification. CNN is deep learning architecture designed to learn topological information and patterns appearing in adjacent spaces, and has demonstrated impressive performance in computer vision [21; 22; 23; 24], natural language processing [24; 25], medical fields [15; 26], and many other tasks. CNN has been continuously evolving since LeNet [27], and has become the most prioritized model in image-related tasks. Over the past decade, various CNN-based models, such as VGGNet [28] and GooLNet [29] have shown good classification performance since the advent of the first large-scale CNN, AlexNet [21]. However, due to the gradient vanishing problem, as the layer of the model deepened, it was revealed that the performance deteriorated, and a problem occurred that the training was not performed properly. ResNet [30] overcomes this problem through residual learning using skip connection. In summary, we propose a method to solve the multi-label classification problem of ECG signals based on SE-ResNet, which applies the squeeze and excitation method [31] to ResNet. The squeeze and excitation method consists of two steps. Through the squeeze step, the entire information corresponding to the channel can be expressed as a channel descriptor. Then, in excitation step, the relative importance of each channel is calculated using the information obtained in the previous step and used as weights.