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

Jihye Yoo,Min-Soo Kim,Yeongbong Jin,Bonggyun Ko

doi:10.3390/app11167758

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.

Highlights

SE-ResNet-34 was used as a classifier to apply the k-labelsets methodology because this model showed the best performance while changing the depth of plain ResNet and SE-ResNet models
convolutional neural network (CNN) is suitable for image classification problems, and ECG data have a very small amount of data compared to images, so it does not seem to show good performance even if the depth of the model is deep
The results of comparing plain ResNet and SE-ResNet showed that the overall performance improved when the squeeze and excitation network was used, but the performance measures were improved by less than 1% except exact match

Summary

Introduction

Algorithm was applied to improve the performance in multi-label classification problems. Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide, and are a generic term for disorders of the heart or blood vessels. Because of the high mortality rates of CVDs, early detection and accurate identification of arrhythmias are essential for treatment of patient [4]. The electrocardiogram (ECG), which records the electrical activity of the heart, is the most commonly used tool to detect arrhythmias due to its low cost and non-invasive characteristics. There may be subtle changes in the ECG that have not been detected. To overcome these problems, computer-aided diagnosis (CAD) algorithms have been used to automate the diagnosis of arrhythmias.

Methods

Results

Conclusion