TFDF: Self-Supervised Time-Frequency Dynamic Fusion with Dual Constraints for Atrial Fibrillation Detection

Atrial fibrillation (AF) is the most common paroxysmal cardiac arrhythmia, requiring continuous wearable electrocardiogram (ECG) monitoring for early detection. Supervised AF detection methods rely on extensive annotated ECG data, which is a costly barrier for real-world applications. Self-supervised learning (SSL) leverages unlabeled data for representation learning. However, existing SSL methods often fail to effectively model the cross-domain structural dependencies between temporal and spectral characteristics of AF under label-free conditions. To address this challenge, we propose a self-supervised Time-Frequency Dynamic Fusion (TFDF) with dual constraints for label-efficient AF detection. The TFDF takes temporal RR interval rhythm features as stable guidance and integrates multi-scale spectral representations of the RR and P-wave bands. A directional consistency constraint is introduced as the core objective to achieve adaptive cross-domain feature fusion, ensuring coherent latent representations between temporal and spectral modalities. Meanwhile, a cluster-guided constraint is designed to provide soft structural priors, stabilizing feature alignment during unsupervised pretraining. The TFDF was pretrained on the MIT-BIH AF Database and fine-tuned on the CPSC2018 dataset. When evaluated on the Chapman-Shaoxing 12-lead ECG dataset, TFDF achieved an average F1-score of approximately 0.920 and AUC of approximately 0.979, outperforming state-of-the-art SSL baselines. These results demonstrate that TFDF provides a generalizable and label-efficient solution for ECG-based AF detection.