Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Stages of Sleep01:22

Stages of Sleep

198
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...
198

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Efficacy of gel immersion endoscopic submucosal dissection for colorectal lesions.

Endoscopy international open·2026
Same author

MANTIS closure device-based rotate-suturing technique by both the operator and assistant for colorectal endoscopic submucosal dissection defects.

Clinical endoscopy·2026
Same author

Trends, Demographic Characteristics, and Seasonal Patterns of Nonoperative and Operative Reduction for Intussusception: A Preliminary Nationwide Claims-Based Analysis from 2014 to 2023 in Japan.

Internal medicine (Tokyo, Japan)·2026
Same author

Endoscopic Outcomes of Risankizumab in Crohn's Disease: A Real-World Analysis of Terminal Ileal Lesions.

Inflammatory intestinal diseases·2026
Same author

Developmental trajectory of individuals with Pelizaeus-Merzbacher Disease (PMD).

Molecular genetics and metabolism·2026
Same author

A Comprehensive Analysis of Prevalence, Risk Stratification, and Early Post-Procedure Predictive Scoring for Post-endoscopic Submucosal Dissection Coagulation Syndrome.

Digestion·2026
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
查看所有相关文章

相关实验视频

Updated: Jul 8, 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

539

痴呆症尺度分类与从睡眠活动数据的序列模型.

Shinichi Sugiura, Shinichiro Yokoyama, Ken Inoue

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括
    此摘要是机器生成的。

    监测睡眠活动可以帮助检测痴呆症. 这项研究使用机器学习对124名老年患者的睡眠数据进行认知状态分类,达到0.67F1分,表明睡眠模式预测痴呆症.

    更多相关视频

    Multi-Modal Home Sleep Monitoring in Older Adults
    07:40

    Multi-Modal Home Sleep Monitoring in Older Adults

    Published on: January 26, 2019

    7.7K
    Polygraphic Recording Procedure for Measuring Sleep in Mice
    08:45

    Polygraphic Recording Procedure for Measuring Sleep in Mice

    Published on: January 25, 2016

    23.8K

    相关实验视频

    Last Updated: Jul 8, 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

    539
    Multi-Modal Home Sleep Monitoring in Older Adults
    07:40

    Multi-Modal Home Sleep Monitoring in Older Adults

    Published on: January 26, 2019

    7.7K
    Polygraphic Recording Procedure for Measuring Sleep in Mice
    08:45

    Polygraphic Recording Procedure for Measuring Sleep in Mice

    Published on: January 25, 2016

    23.8K

    科学领域:

    • 神经学 神经学
    • 生物医学工程 生物医学工程
    • 数据科学数据科学数据科学

    背景情况:

    • 痴呆症是一种影响认知功能和睡眠模式的大脑疾病.
    • 监测睡眠活动为评估认知状态变化提供了一种潜在的非侵入性方法.
    • 早期发现认知衰退对于及时干预和管理至关重要.

    研究的目的:

    • 利用睡眠活动数据开发机器学习模型来对痴呆症进行分类.
    • 探索使用低负荷睡眠监测用于认知评估的可行性.
    • 为了确定与较低的认知分数相关的特定睡眠特征.

    主要方法:

    • 采集了124名老年参与者的睡眠活动数据 (心率,呼吸,睡眠深度),使用单个传感器.
    • 利用迷你精神状态估计 (MMSE) 测试来确定认知状态.
    • 应用统计分析和序列建模 (LSTM) 用于时间序列分析和二进制分类.

    主要成果:

    • 在具有高认知状态和低认知状态的参与者之间发现了睡眠模式的显著差异.
    • 在使用LSTM模型对痴呆症进行分类时,达到0.67的最大宏F1得分.
    • 证明了睡眠活动数据在预测痴呆症分类方面的潜力.

    结论:

    • 睡眠活动数据显示出与老年人的认知状态相关的明显模式.
    • 机器学习模型,特别是LSTM,可以有效地利用睡眠数据进行痴呆症分类.
    • 睡眠监测为痴呆症查和预测提供了一个有希望的,低负担的方法.