k-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet

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.