Contrastive Time-Invariant Learning for Transferable ECG Representations

Learning transferable electrocardiogram (ECG) representations requires models to preserve diagnostic information and remain stable under changes that do not affect the diagnosis. Contrastive learning can encourage this stability through positive-pair construction. However, we observe that small temporal shifts still alter model predictions and internal representations. To address this issue, we propose Contrastive Time-Invariant Learning (CTI), which forms positive pairs from two independently circularly shifted views of the same ECG. CTI jointly optimizes supervised classification and cross-view contrastive objectives to explicitly encourage representation consistency across temporal positions, without changing the encoder architecture or test-time inference. In alignment analyses under fixed shifts, CTI improves deep-layer representation consistency at corresponding waveform locations. In frozen-encoder cross-dataset evaluation on three external ECG datasets, CTI improves classification accuracy by 4.9–14.2 percentage points over a two-view cross-entropy baseline using the same shifted views. These results show that explicitly encoding temporal shifts as a contrastive positive-pair relation improves the transferability of ECG representations beyond temporal-shift augmentation alone.