睡眠中的多模态人工智能：整合多通道信号以进行综合评估
Multimodal Artificial Intelligence for Sleep: Integrating Multichannel Signals for Comprehensive Assessment.
文献信息
| PMID | 42977369 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Samaneh Nasiri |
| 作者单位 | Department of Biomedical Informatics, Emory School of Medicine, GA, USA. Electronic address: snasiri@dbmi.emory.edu. |
| 期刊 | Sleep medicine clinics |
| SCI 分区 | Q2 |
| IF | 5 |
| 研究类型 | 综述 Meta · 临床 |
| 所属专科 | 咽喉科 |
中文摘要
睡眠医学产生丰富的多模态生理数据，传统上通过费力的手动解读被简化为汇总指标。人工智能（AI），尤其是深度学习，正在通过实现多导睡眠图信号的可扩展、可重复和整合分析来改变这一领域。本综述探讨了睡眠医学中多模态AI的5个相互关联的前沿方向：在多样化生物信号数据集上预训练的基础模型，可能统一跨设备和人群的分析；与个体专家可靠性相匹配的自动睡眠分期系统；AI驱动的呼吸分析，超越呼吸暂停-低通气指数，转向对睡眠呼吸障碍的连续、内型水平表征；AI衍生的睡眠生物标志物，包括脑电图微结构、脑年龄。
英文摘要
Sleep medicine generates rich, multimodal physiologic data that has traditionally been reduced to summary metrics through labor-intensive manual interpretation. Artificial intelligence (AI), particularly deep learning, is transforming the field by enabling scalable, reproducible, and integrated analysis of polysomnographic signals. This review examines 5 interrelated frontiers of multimodal AI in sleep medicine: foundation models pre-trained on diverse biosignal datasets that may unify analysis across devices and populations; automated sleep staging systems that match individual expert reliability; AI-driven respiratory analysis that moves beyond the Apnea-Hypopnea Index toward continuous, endotype-level characterization of sleep-disordered breathing; AI-derived sleep biomarkers, including electroencephalography microstructure, brain age.