ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning

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ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
Journal Title:
IEEE Journal of Biomedical and Health Informatics
Publication Date:
15 September 2023
Gao, H., Wang, X., Chen, Z., Wu, M., Li, J., & Liu, C. (2023). ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning. IEEE Journal of Biomedical and Health Informatics, 1–12.
The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG2-RP-2021-027

This research was funded by the National Natural Science Foundation of China (62171123, 62211530112, 62201144 and 62071241), the National Key Research and Development Program of China (2022YFC2405600), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20 0088), the Fundamental Research Funds for the Central Universities (3201002106D).
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