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基于CWT和深度学习的老年人自动睡眠分期模型

Qunfeng Niu^1,2, Ranran Gui¹, Hengfang Liu³

¹College of Electrical Engineering, Henan University of Technology, Zhengzhou, China.

Scientific reports

|July 1, 2025

概括

这项研究介绍了RICM-SleepNet,这是一个先进的深度学习模型,用于老年人的自动睡眠阶段. 该模型实现了高精度,改善了对这一脆弱人群的睡眠障碍诊断.

科学领域:

生物医学工程生物医学工程
医疗保健中的人工智能
睡眠医学睡眠医学

背景情况:

手动睡眠分阶段是耗时和主观的,影响睡眠障碍的诊断.
自动睡眠分期模型缺乏特定年龄的考虑,尽管与年龄相关的睡眠架构变化.
由于睡眠模式发生变化,老年人更容易患上睡眠障碍,因此需要专门的模型.

研究的目的:

为老年人开发一个准确的自动睡眠分期模型.
为应对与年龄相关的睡眠结构变化所带来的挑战.
提高老年人睡眠障碍诊断的效率和可靠性.

主要方法:

建立了一个3D时间频率特征融合地图数据集,使用连续波波变换.
开发了RICM-SleepNet,结合了Inception模块,CBAM注意力和多尺度特征连接.
从睡眠-EDF扩展数据集中确定了最佳通道信号.

主要成果:

在专用数据集上,RICM-SleepNet实现了87.66%的准确性和0.8492的 κ值.
该模型的表现优于既有基线模型,包括GoogLeNet,ResNet50和DenseNet121.
统计分析 (克鲁斯卡尔-威利斯测试) 证实了该模型的卓越性能 (p=0.0014).

结论:

在老年人中,RICM-SleepNet显示了准确的自动睡眠分期的巨大潜力.
该模型的性能超过现有方法,为临床应用提供了一个有前途的工具.
建议进一步验证以更广泛地实施管理与年龄相关的睡眠问题.

关键词:

自动睡眠分期自动睡眠分期连续波形变换连续波形变换.这是一个EEGEEGEEGEEGEEGEEGEEG.经济发展组织 (EOG) 是一个经济发展组织.这就是RICM-SleepNet.在ResNet50中使用ResNet50

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