Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

作者：Yue Zhang, J. Lai, Chenyu Zhao, Jinliang Wang, Yong Yan, Mingyang Chen, Lei Ji, Jun Guo, B. Han, Yajun Shi, Yundai Chen, Wei Yang, Q. Feng · 发表于：Artif. Intell. Medicine · 年份：2024 · DOI：10.1016/j.artmed.2024.102992 · 被引用次数：5 · 研究领域：Medicine、Computer Science

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected,...