Decoder Design Matters for ECG Delineation

2026-09-14

AI Summary

研究通过设计R-U-Net模型，结合ResNet-18编码器和U-Net解码器，改进了心电图边界识别的准确性，解决了标注数据稀缺问题。

Abstract

Electrocardiogram (ECG) delineation identifies the boundaries of P waves, QRS complexes, and T waves, providing structural annotations that can guide AI models in learning to interpret ECGs. However, training accurate delineation models requires manual annotations that are scarce and time-consuming to obtain. Recent work addresses this limitation through semi-supervised learning (SSL), but the design of the architecture, particularly the decoder, has received less attention. To this end, we propose R-U-Net, an ECG delineation model that pairs a ResNet-18 encoder with a U-Net decoder. On SemiSegECG, R-U-Net outperforms the strongest evaluated ResNet-18 + fully convolutional network (FCN) head baseline in each of the 16 in-domain settings by 3.3-13.0 mIoU and achieves 82.6 mIoU in the cross-domain setting, an improvement of 8.1 mIoU. Controlled ablations show that decoder design contributes more to performance gains than the evaluated SSL methods, motivating further exploration of architectures for ECG delineation. All code is open-source at github.com/ELM-Research/ECG-Delineation.

Problem

• Electrocardiogram
• Delineation
• Semi-supervised learning
• Decoder design

Innovation

• R-U-Net
• U-Net decoder
• Semi-supervised learning (SSL)
• Decoder design