The GANAD-DFCNTA algorithm for distant transfer learning improved electrocardiogram classification accuracy by an average of 0.303-5.19% compared to traditional transfer learning.
Does the GANAD-DFCNTA distant transfer learning algorithm improve electrocardiogram classification accuracy compared to traditional transfer learning?
A novel distant transfer learning algorithm (GANAD-DFCNTA) significantly improves ECG classification accuracy by effectively bridging knowledge transfer from distant, non-medical source domains.
Effect estimate: 0.303-5.19% improvement
Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation in the performance of the classification models, particularly for models based on deep-learning algorithms. Transfer learning has demonstrated effectiveness in transferring knowledge from a source model with a similar domain and can enhance the performance of the target model. Nevertheless, the consideration of datasets with similar domains restricts the selection of source domains. In this paper, electrocardiogram classification was enhanced by distant transfer learning where a generative-adversarial-network-based auxiliary domain with a domain-feature-classifier negative-transfer-avoidance (GANAD-DFCNTA) algorithm was proposed to bridge the knowledge transfer from distant sources to target domains. To evaluate the performance of the proposed algorithm, eight benchmark datasets were chosen, with four from electrocardiogram datasets and four from the following distant domains: ImageNet, COCO, WordNet, and Sentiment140. The results showed an average accuracy improvement of 3.67 to 4.89%. The proposed algorithm was also compared with existing works using traditional transfer learning, revealing an average accuracy improvement of 0.303-5.19%. Ablation studies confirmed the effectiveness of the components of GANAD-DFCNTA.
Chui et al. (Fri,) conducted a other in electrocardiogram classification. GANAD-DFCNTA algorithm vs. traditional transfer learning was evaluated on average accuracy improvement (0.303-5.19% improvement). The GANAD-DFCNTA algorithm for distant transfer learning improved electrocardiogram classification accuracy by an average of 0.303-5.19% compared to traditional transfer learning.