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一个统一的灵活的大型多睡眠学模型用于睡眠分期和精神障碍诊断
Guifeng Deng1,2, Mengfan Niu1, Shuying Rao1,2
1Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China.
medRxiv : the preprint server for health sciences
|December 23, 2024
概括
一个新的大型多睡眠学 (PSG) 模型,LPSGM,提高了睡眠阶段的准确性,并在各中心进行了概括. 它还有助于诊断神经精神疾病,如麻醉症和严重抑郁症 (MDD).
科学领域:
- 睡眠医学 睡眠医学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 临床多睡眠学 (PSG) 分析是劳动密集型的,自动化工具缺乏通用性.
- 跨中心和协议的PSG数据的异质性限制了当前的自动睡眠分期.
- 诊断神经精神疾病往往需要了解睡眠架构,但缺乏综合工具.
研究的目的:
- 开发一个大规模的PSG模型 (LPSGM),以改善睡眠阶段的交叉中心概括.
- 通过使用PSG数据,使神经精神疾病的适应性诊断成为可能.
- 为整合睡眠和心理健康诊断创建一个可扩展,数据高效的框架.
主要方法:
- 在220500小时的多中心PSG数据上训练了LPSGM,来自16个公共数据集.
- 集成的领域适应性预培训,灵活的道配置和统一的架构.
- 验证了用于睡眠阶段的LPSGM,并为麻醉症和严重抑郁症 (MDD) 检测进行了微调.
主要成果:
- 在睡眠分阶段 (κ = 0.845 ± 0.066) 方面,LPSGM在专家层面达成了共识.
- 在独立的私人队伍中,性能与完全监督的模型相匹配.
- 精心调整的LPSGM在麻醉症检测方面达到88.01%的准确性,在MDD检测方面达到100%.
结论:
- LPSGM提供了一个可扩展和数据效率高的框架,用于自动化睡眠分期,具有跨中心通用性.
- 该模型显示了睡眠障碍和神经精神疾病综合诊断的潜力.
- 公共可用的代码和模型在睡眠医学和翻译研究中提升了可重复性.
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