发表机构
KU Leuven; University Psychiatric Center KU Leuven; Leuven Brain Institute; Maastricht University Medical Centre+ (MUMC+)（鲁汶大学; 鲁汶大学大学精神病中心; 鲁汶脑研究所; 马斯特里赫特大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以住院痴呆患者为对象，利用床垫下感知系统的夜间信号，通过含四种范式的患者分组基准，发现分钟级时序建模较传统方法能更好地预测次日激越风险，为相关研究提供了新方向。
AI 中文摘要
痴呆患者的激越症状会在短时间内波动，但用于预测次日激越风险的连续生理信息有限。本研究评估前一晚的非接触式床垫下信号是否可用于预测次日激越风险，以及保留分钟级时序结构是否比传统夜间汇总特征更能提升性能。研究分析了某专科医院痴呆单元65名受试者的423个患者-夜晚数据，采用两套床垫下感知系统，通过包含四种范式的统一基准方法，比较了夜间手工汇总特征、三时段手工特征、全夜序列建模、滑动窗口多实例学习这四类方法。研究采用源特定预处理和五折患者分组交叉验证，通过合并折外预测估计性能，评估指标包括判别度、校准度、固定阈值指标，还比较了两种时序模型的时段信号归因模式。结果显示，全夜序列建模的判别度最高（AUROC为0.692，AUPRC为0.849），平衡准确率为0.658；两种分钟级处理流程的AUROC均高于夜间汇总特征，但与三时段手工特征的差异不确定；跨模型归因将核心夜间时段的活动、心率、呼吸率作为优先特征；校准度仍有限。研究表明前一晚的信号可支持适度的次日风险判别，分钟级时序建模优于夜间汇总特征，在用于个体护理决策前需进行前瞻性校准和外部验证，该患者分组基准确定了非接触式夜间感知是住院痴呆队列激越风险研究中极具潜力的生物医学工程方向。
英文摘要
Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk is limited. We assessed whether contactless under-mattress signals from the preceding night inform next-day agitation risk and whether preserving minute-level temporal structure improves performance over conventional nightly summaries. We analyzed 423 patient-nights from 65 subjects in a specialized hospital dementia unit using two under-mattress sensing systems. A unified four-paradigm benchmark compared nightly handcrafted summaries, three-period handcrafted features, full-night sequence modeling, and sliding-window multiple-instance learning. Source-specific preprocessing and five-fold patient-grouped cross-validation were used, with performance estimated from pooled out-of-fold predictions. Evaluation included discrimination, calibration, fixed-threshold metrics, and a comparison of period-signal attribution patterns across two temporal models. Full-night sequence modeling achieved the highest discrimination (AUROC, 0.692; AUPRC, 0.849) and balanced accuracy (0.658). Both minute-level pipelines had higher AUROC than nightly summaries, but differences from three-period handcrafted features were uncertain. Cross-model attribution prioritized activity, heart rate, and respiratory rate during the core overnight period. Calibration remained limited. The preceding night's signals supported modest next-day risk discrimination, with minute-level temporal modeling outperforming nightly summaries. Prospective calibration and external validation are needed before use in individual care decisions. This patient-grouped benchmark identifies contactless overnight sensing as a promising biomedical engineering direction for agitation-risk research in hospitalized dementia cohorts.
