Impact of temporal context length and encoding strategies on self-supervised ECG representation learning
Read the original at arxiv.org→arXiv:2608.12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It remains unclear...
Original headline: "The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning"
Coverage timeline
- Aug 14, 04:00 UTC arXiv cs.LG lead source The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning