发表机构
VUNO Inc.; Ajou University; C&Thoth Co., Ltd.; Chonnam National University Graduate School; Chonnam National University Hospital; Chonnam National University Medical School（VUNO公司; 亚洲大学; C&Thoth有限公司; 全南大学研究生院; 全南大学医院; 全南大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过多数据集基准测试证明，自监督预训练能有效提升心电分割的标签效率，使深度学习模型在多个指标上超越现有开源和商业工具，支持临床实际应用。
AI 中文摘要
心电图（ECG）分割，即波形边界的识别，是将原始心电信号转化为临床可解释测量的基础步骤。深度学习推进了这一任务，但仍依赖于成本高昂的专家标注。自监督预训练和半监督学习等标签高效策略有望减轻这一负担，然而目前尚不清楚这些策略是否能产生可靠的分割结果，以及由此产生的深度模型是否优于实践中使用的分割工具。我们分两个阶段解决这一问题。首先，在一个内部数据集和四个外部数据集上比较自监督目标与监督或半监督微调，我们发现预训练有帮助，但目标函数的选择至关重要，且半监督微调的价值取决于预训练目标。其次，我们使用三个互补的指标，将选定的深度学习模型与广泛使用的开源工具（NeuroKit2、Prominence、ECGdeli）和商业工具（CalECG）进行基准测试。该模型在每个指标和数据集上均排名最佳，在节律多样集上明显优于最强工具（mIoU 71.3% 对 54.8%；平均逐点灵敏度 92.6% 对 76.4%），并且从窦性节律到心律失常的退化最小。节律分层和逐点分析进一步刻画了每种工具的独特行为，为工具选择提供了实用指导。这些结果提供了系统的、多数据集的证据，表明自监督预训练对心电分割有效，并能通过利用丰富的未标注数据，使标签高效训练的深度学习模型优于广泛使用的分割工具。这支持在多样化的真实临床环境中采用此类模型。
英文摘要
Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinically interpretable measurements. Deep learning has advanced this task but remains dependent on costly expert annotations. Label-efficient strategies such as self-supervised pretraining and semi-supervised learning are expected to ease this burden, yet it remains unclear whether they yield reliable delineation and whether the deep models they produce outperform the delineation tools used in practice. We address this in two stages. First, comparing self-supervised objectives with supervised or semi-supervised fine-tuning across one internal and four external datasets, we find that pretraining helps but the objective matters, and that the value of semi-supervised fine-tuning depends on the pretraining objective. Second, we benchmark the selected deep learning model against widely used open-source (NeuroKit2, Prominence, ECGdeli) and commercial (CalECG) tools using three complementary metrics. The model ranks best on every metric and dataset, outperforming the strongest tool by a clear margin on the rhythm-diverse set (mIoU 71.3 vs. 54.8%; averaged point-wise sensitivity 92.6 vs. 76.4%), and degrades the least from sinus to arrhythmia. A rhythm-stratified and point-wise analysis further characterizes the distinctive behavior of each tool, yielding practical guidance for tool selection. These results provide systematic, multi-dataset evidence that self-supervised pretraining is effective for ECG delineation and enables a label-efficiently trained deep learning model to outperform widely used delineation tools by leveraging abundant unlabeled data. This supports adopting such models in diverse, real-world clinical settings.
Comments20 pages, 5 figures. First two authors contributed equally
