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arXiv AI By Chenyang Xu, Donglin Xie, Xi Xiang, Xiaoyu Li, Yufan Lu, Jiqiun Gao, Yi Zhao, Xin-Yi Li, Guangpu Zhu, Zijian Wang, Xiwen Yang, Dezhen Wang, Lin Chen, Shenda Hong, Leilei Li

PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary

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PulseBound is a photoplethysmography (PPG) representation learner that enforces an explicit stored‑window information boundary, ensuring that encoder inputs depend only on a visible prefix and a content‑independent cutoff. By combining physiologically structured future‑beat prediction with prefix‑only normalization, suffix replacement, and aligned masking, PulseBound achieves stored‑suffix invariance, preventing changes in the stored suffix from affecting forecast context or predictions. Evaluated on MIMIC and VitalDB datasets, PulseBound reduces mean absolute error by over 22% compared to last‑visible‑beat persistence and outperforms seven other models on nine frozen linear‑probe and seven full‑fine‑tuning downstream tasks.

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