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.

提出了一个多分辨率模型，利用局部形态信息和全局节奏信息，通过开发低分辨率高级语义信息，持续维持高分辨率低级语义信息，从而成功地从 ECG 细分中提取信息丰富的形态和节律特征，从智能可穿戴应用的角度，也确认基于单导联 ECG 的全面 ECG 解释算法的可能性。