Pulse2View: Same-Sensor Cross-View Pre-Training for Patient-Level Multi-Disease Classification from Wearable PPG

Photoplethysmography (PPG) is a widely available biosignal in consumer wearables, enabling scalable, low-burden cardiovascular screening. However, labeled PPG datasets are scarce, and single-view self-supervised pre-training can underexploit the complementary morphology, rhythm, and time-frequency structure of PPG. To address this, we propose Pulse2View (P2V), a same-sensor cross-view pre-training framework aligning two views of each 30-second recording without an additional sensing modality. For each of three consecutive 10-second segments, the continuous wavelet transform (CWT) branch constructs a three-channel Ricker-CWT image from the exported PPG and its first- and second-order differences, while a Conv1D-BiGRU encodes the full sequence as a temporal (time-domain) view. Symmetric InfoNCE aligns the two views without disease labels. Both encoders are transferred and fused end-to-end downstream, while inference remains PPG-only. Pre-trained on 99,701 unlabeled 30-second PPG windows, P2V was evaluated on the private hospital-derived Clinical Wearable PPG Cohort (CWPC; 14,811 patients and 100,657 windows). It achieved the highest three-seed mean on all four primary patient-level metrics among evaluated models, including a coronary heart disease (CHD) AUROC of 0.7859 ± 0.0044 and a Macro AUROC of 0.7150 ± 0.0012, exceeding a public image-pretrained ConvNeXtV2-Tiny initialization by 3.2 and 1.4 AUROC points and the strongest public baseline, MOMENT-small, by 2.6 and 0.8 AUROC points. At 25% and 50% label budgets, P2V improves over the image-pretrained initialization and a two-CWT-view control, while achieving the highest CHD and Macro AUPRC among these configurations. Paired bootstrap and ablations support the contributions of cross-view alignment and the pre-trained temporal branch. On the public Liu PPG arrhythmia six-class dataset, P2V also achieves a window-level Macro AUROC of 0.9697 ± 0.0011. Together, these results support label-efficient cross-view pre-training for PPG-only patient-level multi-label classification.