BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment

BeatGraph is a self‑supervised model that represents infant ECG recordings as graphs of individual heartbeats rather than fixed‑length patches, allowing it to capture the higher heart rates and distinct waveform patterns of infants. The model uses a shared beat encoder, a Transformer for temporal ordering, and graph attention layers to produce a window embedding, which is pretrained on a large unlabeled infant ECG corpus and fine‑tuned for tasks such as sleep‑wake detection, infant‑state classification, activity‑source identification, and affect recognition. BeatGraph achieves state‑of‑the‑art performance on multiple infant‑specific benchmarks and transfers well to pediatric and adult ECG datasets, while also releasing the first public infant ECG corpus collected in diverse home and classroom settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• rag
• fine-tuning
• benchmarks