基于大型数据库开发可通用的使用心率和运动的自动睡眠分阶段的开发
Joonnyong Lee1, Hee Chan Kim2,3, Yu Jin Lee4,5
1Mellowing Factory Co. Ltd, Seoul, 06535 South Korea.
Biomedical engineering letters
|October 24, 2023
概括
这项研究开发了一种深度学习模型,使用心率和运动数据自动进行睡眠分阶段. 微调的模型实现了高精度,显示了非接触式睡眠监测系统的前景.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 深度神经网络具有先进的生物信号处理,显著改善了自动睡眠分阶段.
- 然而,使用非脑电图 (EEG) 功能进行睡眠分阶段仍然具有挑战性,特别是在当前美国睡眠医学协会 (AASM) 标准下.
研究的目的:
- 利用心率和运动特征开发一种可广泛通用的自动睡眠分阶段算法.
- 微调深度神经网络模型以提高基于非EEG数据的睡眠阶段精度.
主要方法:
- 在一个大数据集 (8731个夜晚) 上,使用 Rechtschaffen & Kales 评分系统优化了一个深度神经网络.
- 然后,该模型在较小的AASM标记数据集 (1641晚) 上进行了微调,并在两个外部AASM标记数据集 (1183条记录) 上进行了验证.
- 用准确度和科恩的卡帕系数来评估表现.
主要成果:
- 微调的模型在一个外部验证集上实现了76.6%的准确性和0.606的科恩 κ.
- 在另一组外部验证中,它达到81.0%的准确率和0.673的Cohen κ,超过了之前的结果.
结论:
- 拟议的模型证明了使用非接触式监测功能来预测睡眠阶段的概括性和有效性.
- 这种方法对开发先进的家庭睡眠评估系统有重大影响.
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