Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems. (February 2022) - Park, Junsang; An, Junho; Kim, Jinkook; Jung, Sunghoon; Gil, Yeongjoon; Jang, Yoojin; Lee, Kwanglo; Oh, Il-young
Highlights: A twelve-lead ECG fusion approach for ECG classification is proposed. Among single-lead ECGs, the best classification performance are lead -aVR and II. In case of -aVR classification problem, the model's total F1 score was 97.5%. Our model also achieved a high F1 score on actual ECG data measured in hospitals. The most important parts of the proposed DL model are spatial angle similarity. Abstract: 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.