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Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems
- Source :
- Computer methods and programs in biomedicine. 214
- Publication Year :
- 2021
-
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.
- Subjects :
- business.industry
Computer science
Deep learning
12 lead ecg
Health Informatics
Pattern recognition
Convolutional neural network
Computer Science Applications
Electrocardiography
Rhythm
Test set
Republic of Korea
Humans
Artificial intelligence
Neural Networks, Computer
Ecg signal
Lead (electronics)
F1 score
business
Software
Algorithms
Subjects
Details
- ISSN :
- 18727565
- Volume :
- 214
- Database :
- OpenAIRE
- Journal :
- Computer methods and programs in biomedicine
- Accession number :
- edsair.doi.dedup.....e5093b3d74db8b8a83cbd54e248421ee