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

Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of beats contained in each patch shifts as heart rate varies. This matters most for infants, whose heart rates are higher than adults and whose ECG differs from the adult, clinic-recorded 12-lead data on which these models are built. A model for infant ECG should therefore reason about heartbeats directly rather than recover them from arbitrary patches. We propose BeatGraph, which makes the heartbeat its unit of representation, modeling each 30-second window as a graph of beats. A shared beat encoder turns each heartbeat into a feature vector from its waveform and inter-beat intervals, a Transformer with positional encoding orders the beats in time, and residual graph attention layers relate every beat to every other before attention-based pooling produces a window-level representation. We pretrain BeatGraph on our new corpus of unlabeled infant recordings by predicting the embeddings of masked beats, then fine-tune it for each task. This single backbone supports sleep-wake detection, infant-state classification, activity-source identification, meaning whether movement is infant- or caregiver-initiated, and infant affect recognition, improving macro-F1 over the strongest baseline on each task by 0.076 to 0.158. It also transfers across age groups, reaching 0.892 AUROC on the ZZU-pECG pediatric benchmark among children 0 to 14 years of age, within 0.001 of the best published self-supervised ECG model, and matching that model under linear evaluation on the adult PTB-XL benchmark despite pretraining only on infant recordings. Finally, to our knowledge, we release the first public infant ECG corpus collected in homes, classrooms, and laboratory settings with state and affect labels. This corpus contains 3,408 hours of single-channel ECG from 143 infants aged 3 to 11 months, with unlabeled pretraining data, benchmark tasks, and subject-level splits.