🤖 AI Summary
This study addresses the reliance of ECG segmentation on costly expert annotations and the uncertain performance of existing tools by proposing a label-efficient deep learning framework. Methodologically, it systematically evaluates combination strategies of self-supervised pre-training and semi-supervised fine-tuning, benchmarking them against established tools such as NeuroKit2 across multiple datasets. The primary contribution lies in validating the effectiveness of these strategies for identifying ECG waveform boundaries, thereby providing empirical guidance for clinical model selection. Experimental results demonstrate that the proposed model comprehensively outperforms existing baselines across multiple evaluation metrics and datasets, exhibiting particularly significant advantages in scenarios characterized by diverse cardiac rhythms.
📝 Abstract
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.