Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation

TL;DR

This paper introduces CardioMix, a bidirectional fusion framework guided by cardiac patterns, enhancing semi-supervised ECG segmentation by effectively utilizing unlabeled data and improving existing methods.

Contribution

The paper proposes a novel CardioMix framework that employs cardiac pattern-guided bidirectional CutMix for improved semi-supervised ECG segmentation, compatible with various algorithms.

Findings

CardioMix outperforms existing CutMix strategies across multiple datasets.

The framework maintains physiological validity of augmented samples.

It demonstrates high compatibility with different SemiSeg algorithms.

Abstract

Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However, the scarcity of annotated data poses a significant challenge for training deep learning models. Conventional semi-supervised semantic segmentation (SemiSeg) methods primarily focus on consistency from unlabeled data, underutilizing the information exchange possible between labeled and unlabeled sets. To address this, we introduce CardioMix, a framework built on a bidirectional CutMix strategy guided by cardiac patterns for ECG segmentation. This approach enriches the labeled set with realistic variations from unlabeled data while simultaneously applying stronger supervisory signals to the unlabeled set, as the cardiac pattern-guided mixing ensures all augmented samples remain physiologically meaningful. Our framework is designed as a plug-and-play module, demonstrating high compatibility with various SemiSeg algorithms. Extensive experiments on SemiSegECG, a public multi-dataset benchmark for ECG delineation, demonstrate that CardioMix consistently outperforms existing CutMix-based fusion strategies across diverse datasets and labeled ratios as a plug-and-play module compatible with various SemiSeg algorithms.