Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning

Abstract

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.

A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System

“…The transformer networks have efficiency advantages, capturing long-range dependencies, extracting global information, flexibility in adjustment, and good generalization ability. Therefore, transformer networks have been widely used in various tasks, such as natural language processing, machine translation, sentiment analysis, and image processing [ 11 , 19 , 20 , 21 , 22 ]. Natarajan et al [ 11 ], Li et al [ 19 ], and Qiu et al [ 20 ] have designed transformer network architectures for ECG-based applications, and the experimental results showed that the transformer network has high performance and is valid for practical applications.…”