Training a 152-layer SE-ResNet by fusing single-lead ECGs improved classification performance over single-lead training alone, achieving F1 scores of 98.7% for normal rhythm and 98.2% for AF.
Does a 152-layer SE-ResNet trained by fusion of various single-lead ECG data improve classification performance compared to training on only single-lead ECG data in rhythm-type ECG classification?
Training a deep learning model using a fusion of single-lead ECGs improves rhythm classification performance compared to single-lead training alone.
BACKGROUND AND OBJECTIVES: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-learning architecture to improve classification performance. However, in this study, we proposed a methodology for fusion of various single-lead ECG data as training data in the single-lead ECG classification problem. METHODS: We used a squeeze-and-excitation residual network (SE-ResNet) with 152 layers as the baseline model. We compared the performance of a 152-layer SE-ResNet trained on ECG signals from various leads of a standard 12-lead ECG system to that of a 152-layer SE-ResNet trained on only single-lead ECG data with the same lead information as the test set. The experiments were performed using five different types of rhythm-type single-lead ECG data obtained from Konkuk University Hospital in South Korea. RESULTS: Experiment results based on the combination from the relationship experiments of the leads showed that lead -aVR or II revealed the best classification performance. In case of -aVR, this model achieved a high F1 score for normal (98.7%), AF (98.2%), APC (95.1%), and VPC (97.4%), indicating its potential for practical use in the medical field. CONCLUSION: We concluded that the 152-layer SE-ResNet trained by fusion of single-lead ECGs had better classification performance than the 152-layer SE-ResNet trained on only single-lead ECG data, regardless of the single-lead ECG signal type. We also found that the best performance directions for single-lead ECG classification are Lead -aVR and II.
Park et al. (Wed,) conducted a other in Arrhythmia (ECG classification). 152-layer SE-ResNet trained by fusion of single-lead ECGs vs. 152-layer SE-ResNet trained on only single-lead ECG data was evaluated on Classification performance (F1 score). Training a 152-layer SE-ResNet by fusing single-lead ECGs improved classification performance over single-lead training alone, achieving F1 scores of 98.7% for normal rhythm and 98.2% for AF.