arXiv cs.AI· Farouk Ganiyu Adewumi, Timothy Oladunni·· 13 小时前AI 评分22
基于光电容积脉搏波、结合上下文与会话校准的血压追踪评估框架
Context-Augmented, Session-Calibrated Blood Pressure Tracking from Photoplethysmography
AI 导读
一项基于 653 名 AuroraBP 参与者的研究提出显式控制上下文与 PPG 贡献的校准评估框架，原始可穿戴状态模型实现收缩压/舒张压 MAE 7.697/6.139 mmHg，加入上下文后 MAE 分别降低 0.436/0.126 mmHg。
正文 · 原文
Abstract:this http URL distinguish the contributions of acquisition context and photoplethysmography (PPG) to session-calibrated blood pressure (BP) this http URL. The original comparison used three repeats of five-fold participant-level cross-validation in 653 AuroraBP participants, excluding calibration-labelled prediction targets. Matched analyses separated context and PPG and removed elapsed-time terms. Later exploratory follow-ups retained PPG, accelerometer signals and three initial cuff readings. Separate studies evaluated pulse-rate fusion and an intraoperative VitalDB model. Main this http URL original wearable-state model achieved participant-level mean absolute errors (MAEs) of 7.697/6.139 mmHg for systolic/diastolic BP (SBP/DBP). Added context reduced MAE by 0.436/0.126 mmHg (97.5% intervals 0.305-0.573/0.053-0.200). In the matched factorial without the external PPG prior, PPG improved on calibration, time and protocol-provided posture/activity by 0.214/0.135 mmHg; removing time left the DBP interval including zero. Standard PPG descriptors contributed, but additional nonlinear/attractor benefit was not established. In a separate follow-up series, a sensor-history ensemble achieved 7.299/5.445 mmHg versus its static-rich reference's 7.363/5.470, small gains of 0.064/0.025 mmHg. Larger DBP errors did not improve uniformly. Quality-weighted fusion reduced pulse-rate MAE from 3.985 to 3.690 beats per minute. VitalDB MAEs remained 15.375/8.833 mmHg despite small PPG gains. Significance. The contribution is a calibrated evaluation framework with explicit controls, reproducible feature-cache-to-results analyses and retained negative findings. The sensor-history gain is exploratory after repeated dataset reuse. Independently estimated wearable states, complete raw-signal reproduction of the supplied richer features, external transfer and clinical adequacy remain unestablished.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.29091 [cs.AI] |
| (or arXiv:2610.29091v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29091 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Farouk Ganiyu Adewumi [view email]
[v1]
Wed, 21 Oct 2026 02:34:07 UTC (299 KB)
来源：arXiv cs.AI · arxiv.org