在缺血性中风患者的睡眠分析的多睡眠学数据集
Suvadeep Maiti1, Shivam Kumar Sharma1, S Mythirayee2
1IHub-Data, International Institute of Information Technology Hyderabad (IIIT-H), Hyderabad, 500032, India.
Scientific data
|February 11, 2026
概括
这项研究介绍了iSLEEPS数据集,这是亚洲首个用于缺血性中风患者的睡眠数据库. 它揭示了中风幸存者中睡眠呼吸障碍 (SDB) 的高患病率,有助于未来的研究.
科学领域:
- 神经学 神经学
- 睡眠医学 睡眠医学
- 数据科学数据科学数据科学
背景情况:
- 睡眠障碍在缺血性中风幸存者中很常见,影响神经恢复和认知功能.
- 睡眠呼吸障碍 (SDB) 是这一群体中经常出现的并发症.
- 缺乏特定于中风的睡眠数据阻碍了对这些相互作用的研究.
研究的目的:
- 引入用于对缺血性中风患者睡眠分析的多睡眠学数据集 (iSLEEPS).
- 为中风研究提供全面的,注释的睡眠数据库.
- 调查亚洲中风队列中SDB的流行情况.
主要方法:
- 在印度 (2018年9月至2021年12月) 收集了来自缺血性中风患者的100张夜间多睡眠图 (PSG) 记录.
- 根据AASM (2017) 准则,专家对睡眠阶段,呼吸事件,四肢运动和氧气脱度进行了注释.
- 应用深度学习模型 (LSTM,变压器,CNN) 用于自动化睡眠阶段分类.
主要成果:
- iSLEEPS数据集包括100名中风患者的详细的PSG数据和临床指标.
- 在研究队列中观察到SDB的高患病率.
- 长短期记忆 (LSTM) 模型实现了最高的自动睡眠阶段分类准确率,达到74.70%.
结论:
- iSLEEPS数据集解决了中风睡眠研究中的一个关键差距.
- 这个资源有助于研究中风睡眠病理生理学.
- 它支持对经过缺血性中风后睡眠障碍的综合分析.
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