实现基于架构可切换深度学习模型的实时睡眠阶段预测和在线校准
IEEE journal of biomedical and health informatics
|October 25, 2023
概括
一个新的实时睡眠阶段测定网络,SwSleepNet,准确地预测睡眠阶段. 这项技术有助于睡眠调节和睡眠障碍的干预.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 自动睡眠分期对于睡眠障碍干预至关重要.
- 尽管取得了进展,但实时睡眠分期仍未得到充分探索.
研究的目的:
- 提出SwSleepNet,一个新的网络,用于线下和实时的睡眠阶段.
- 为了实现精确的睡眠阶段预测和校准,用于睡眠调节和干预.
主要方法:
- SwSleepNet集成了序列扩展模块 (SBM),序列CNN (SCNN),挤压和激发 (SE) 块和序列巩固模块 (SCM) 来进行离线分析.
- 对于在线分析,SCNN和SE可以从短段预测睡眠阶段,并使用校准机制进行准确.
主要成果:
- 在多个数据集上,SwSleepNet实现了高离线准确度 (84.5%-86.7%),超过了现有的方法.
- 在线睡眠分期显示了使用短段和校准机制的强大的准确性 (>80%).
结论:
- SwSleepNet提供了一个强大而稳定的实时睡眠分期解决方案.
- 预计这种架构将促进睡眠调节和睡眠障碍干预.
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