Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has once again been restricted, prompting the need for the development of advanced diagnostic algorithms. Classic rule-based algorithms are now completely outperformed by deep learning based methods. But the advancement of smart diagnostic algorithms is hampered by issues like small dataset, inconsistent data labeling, inefficient use of local and global ECG information, memory and inference time consuming deployment of multiple models, and lack of information transfer between tasks. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout, with the help of the development of low-resolution high-level semantic information, by capitalizing on both local morphological information and global rhythm information. From the perspective of effective data leverage and inter-task knowledge transfer, we develop a parameter isolation based ECG continual learning (ECG-CL) approach. We evaluated our model's performance on four open-access datasets by designing segmentation-to-classification for cross-domain incremental learning, minority-to-majority class for category incremental learning, and small-to-large sample for task incremental learning. Our approach is shown to successfully extract informative morphological and rhythmic features from ECG segmentation, leading to higher quality classification results. From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.

翻译：心电图监测是心血管疾病早期识别的最有力技术之一，而智能可穿戴心电图设备的引入使得日常监测成为可能。然而，由于心电图解读需要专业知识，普通公众的访问再次受到限制，从而推动了先进诊断算法开发的必要性。基于经典规则的算法目前已被基于深度学习的方法完全超越。但智能诊断算法的进展受到数据集小、数据标注不一致、局部与全局心电图信息利用效率低、多模型部署时内存与推理时间消耗大，以及任务间信息传递不足等问题的阻碍。我们提出了一种多分辨率模型，该模型能够在利用局部形态学信息和全局节律信息的同时，通过低分辨率高层语义信息的发展，全程维持高分辨率低层语义信息。从有效数据利用和任务间知识迁移的角度，我们开发了一种基于参数隔离的心电图持续学习方法。我们通过在四个开放获取数据集上设计分割到分类的跨领域增量学习、少数类到多数类的类别增量学习，以及小样本到大样本的任务增量学习，评估了模型性能。结果表明，我们的方法能够成功从心电图分割中提取信息丰富的形态学和节律特征，从而获得更高质量的分类结果。从智能可穿戴应用的角度，本文也证实了基于单导联心电图实现综合心电图解读算法的可能性。