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相关概念视频

Stages of Sleep01:22

Stages of Sleep

258
Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
258
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

1.4K
Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
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相关实验视频

Updated: Jul 16, 2025

Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

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基于图像的标准化多睡眠数据库和深度学习算法用于睡眠阶段分类.

Jaemin Jeong1, Wonhyuck Yoon2, Jeong-Gun Lee1

  • 1Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea.

Sleep
|September 13, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一套基于图像的标准化数据集,用于使用深度学习 (DL) 进行自动化睡眠评分. DL模型的准确度超过80%,证明了其有效性和强大的睡眠分析潜力.

关键词:
计算机神经网络 计算机神经网络数据集数据集数据集深度学习是一种深度学习.聚类人体图像 (polysomnography) 是一种多人体图像.睡眠的不同阶段.

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科学领域:

  • 睡眠的药物 睡眠的药物
  • 人工智能的人工智能是人工智能.
  • 生物医学数据科学是生物医学数据科学.

背景情况:

  • 由于劳动强度,主观性和模两可,多睡眠学 (PSG) 评分具有挑战性.
  • 现有的深度学习 (DL) 模型用于自动睡眠评分受到固定输入通道和分辨率要求的限制.
  • 来自各种PSG设备和实验室环境的数据异质性使自动分析复杂化.

研究的目的:

  • 开发一个标准化的基于图像的数据集,用于多睡眠学 (PSG) 数据.
  • 创建和验证基于图像的深度学习 (DL) 模型,用于自动化睡眠分期.
  • 在PSG分析中克服原始信号异质性的局限性.

主要方法:

  • 将包含原始PSG信号的欧洲数据格式文件转换为标准化图像.
  • 开发了一种基于图像的DL模型,用于自动测定睡眠阶段.
  • 将基于图像的DL模型与基于信号的模型进行比较,并在外部数据集上进行验证.

主要成果:

  • 构建了一个由10253张基于图像的PSG记录组成的数据集.
  • 基于图像的DL模型实现了超过80%的准确性,与基于信号的模型相比.
  • 在睡眠医学中使用Eigen类激活地图证明了可解释的AI (DL),并取得了良好的外部验证性能.

结论:

  • 成功创建了一个基于图像的标准化PSG数据集.
  • DL模型显示了对数据采样率或传感器数量变化的稳定性,性能变化很小.
  • 这种方法为自动睡眠评分提供了一种灵活且可能更具适应性的解决方案.