Key result
The SE-ResNet-34 model with soft voting through the RAKEL algorithm achieved an accuracy of 88.49% and an F1-score of 92.43% for multi-label ECG signal classification.
Why the study?
ECG is the most commonly used diagnostic tool for cardiovascular diseases, motivating deep learning methods to perform multi-label classification on standard ECG data.
The application of the RAKEL algorithm with soft voting to an SE-ResNet model improves multi-label classification performance of standard 12-lead ECGs.
May advance automated multi-label ECG classification; leaves open prospective clinical validation before practice adoption.
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.
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Yoo et al. (2021) studied Cardiovascular diseases. SE-ResNet with random k-labelsets (RAKEL) algorithm vs. Plain ResNet was evaluated on Multi-label classification performance (exact match, accuracy, F1-score, precision, recall). The SE-ResNet-34 model with soft voting through the RAKEL algorithm achieved an accuracy of 88.49% and an F1-score of 92.43% for multi-label ECG signal classification.
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