Key result
Label-efficient deep learning improves ECG segmentation accuracy by ~17 percentage points over rule-based tools.
Why the study?
Deep learning for ECG delineation relies on costly expert annotations, and it remained unclear whether label-efficient strategies like self-supervised pretraining and semi-supervised learning yield reliable delineation models that outperform widely used delineation tools.
Does a label-efficiently trained deep learning model using self-supervised pretraining and semi-supervised fine-tuning improve ECG waveform delineation accuracy and clinical interval estimation compared to established open-source and commercial rule-based tools across diverse cardiac rhythms?
Does a label-efficiently trained deep learning model using self-supervised pretraining and semi-supervised fine-tuning improve ECG waveform delineation accuracy and clinical interval estimation compared to established open-source and commercial rule-based tools across diverse cardiac rhythms?
Self-supervised pretraining combined with semi-supervised fine-tuning enables deep learning ECG delineation models to significantly outperform widely used commercial and open-source rule-based tools across diverse datasets and arrhythmias without requiring massive expert annotations.
May aid ECG delineation in arrhythmias; leaves open prospective validation before clinical use.
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.
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Lim et al. (2026) studied Electrocardiogram (ECG) waveform delineation across sinus rhythm and arrhythmias (n=3,138). Label-efficient deep learning model (DL DELINEATOR) using self-supervised pretraining and semi-supervised/supervised fine-tuning vs. Widely used open-source (NeuroKit2, Prominence, ECGdeli) and commercial (CalECG) ECG delineation tools was evaluated on Mean Intersection over Union (mIoU) on the rhythm-diverse RDB dataset. Label-efficient deep learning outperformed the strongest rule-based tool (ECGdeli) on rhythm-diverse ECGs (mIoU 71.3% vs 54.8%; point-wise sensitivity 92.6% vs 76.4%).
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