The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning
Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It remains unclear whether these choices sacrifice information needed for rhythm inference and longitudinal consistency in real-world ambulatory recordings. We present a controlled study on the Icentia11k single-lead dataset that varies (i) the input horizon (16 seconds, 1 minute, 5 minutes, and 10 minutes) and (ii) the front-end representation (continuous convolutional patch embeddings vs. fixed vector-quantized tokens), while holding the Transformer backbone and training protocol constant. Representations are assessed by downstream abnormal r...
| Authors | Ahmed Sameh, Ramzi Al-Sharawi, Yogatheesan Varatharajah |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 25 August 2026 |
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