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

Understanding Sleep01:11

Understanding Sleep

410
Sleep, an essential biological state, involves significant reductions in physical activity, sensory awareness, and interaction with the environment. This complex physiological process is primarily regulated by specific brain regions, notably the hypothalamus and pons, which govern the sleep-wake cycle or circadian rhythm.
The circadian rhythm, a nearly 24-hour cycle, is deeply influenced by environmental light cues. Light exposure directly affects the hypothalamus, which in turn regulates...
410
Stages of Sleep01:22

Stages of Sleep

372
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...
372
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:
1.4K

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

Generation and application of monoclonal antibodies against CD4-1 and CD8α for characterizing T cell subsets in grass carp (Ctenopharyngodon idella).

Fish & shellfish immunology·2026
Same author

CTSS regulates macrophage lipid metabolic reprogramming and white matter repair after intracerebral hemorrhage.

Journal of translational medicine·2026
Same author

MPLIF: Multi-parametric leaky integrate-and-fire neuron for spiking neural networks.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Why Empirical Risk Minimization Performs Well for Open Set Domain Adaptation: A Theoretical Analysis From Causal View.

IEEE transactions on neural networks and learning systems·2026
Same author

Atomic-scale ordering enables intrinsic bioactivity and rapid osseointegration in medium-entropy alloys.

Bioactive materials·2026
Same author

RMT-match: an unsupervised 3D medical image registration network based on RMT and wavelet convolution.

Biomedical physics & engineering express·2026

相关实验视频

Updated: Jul 19, 2025

Author Spotlight: IntelliSleepScorer — 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

565

微睡眠网:为移动终端实时睡眠分期提供高效的深度学习模型.

Guisong Liu1, Guoliang Wei1, Shuqing Sun1

  • 1Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China.

Frontiers in neuroscience
|August 14, 2023
PubMed
概括

微睡眠网 (Micro SleepNet) 是一种新的轻量级深度学习模型,用于移动设备上的实时睡眠分期. 这种高效的脑电图 (EEG) 算法实现了高精度,使闭环睡眠调制应用成为可能.

关键词:
深度学习是一种深度学习.轻量级设计 轻量级设计模型的部署部署.实时效率效率实时效率.睡眠阶段化是什么

更多相关视频

Noninvasive, High-throughput Determination of Sleep Duration in Rodents
07:33

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Published on: April 18, 2018

7.9K
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

相关实验视频

Last Updated: Jul 19, 2025

Author Spotlight: IntelliSleepScorer — 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

565
Noninvasive, High-throughput Determination of Sleep Duration in Rodents
07:33

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Published on: April 18, 2018

7.9K
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

科学领域:

  • 生物医学工程 生物医学工程
  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 目前的深度学习睡眠阶段模型缺乏实时效率,并且具有冗余的参数,阻碍了移动设备应用程序.
  • 在移动设备上实时睡眠分期对于闭环睡眠调制至关重要.
  • 现有的模型通常依赖于上下文信号,限制了它们的独立实用性.

研究的目的:

  • 为移动设备开发一种轻量级,高性能的睡眠分期模型.
  • 为了从脑电图 (EEG) 时代在没有上下文信号的情况下实时推断睡眠阶段.
  • 为准确的闭环睡眠调节提供基础.

主要方法:

  • 提出了Micro SleepNet,这是一个利用1D组卷积和高效通道和空间注意力 (ECSA) 模块的模型.
  • 实现了具有扩展卷积的特征融合,并用全球平均聚合 (GAP) 取代了完全连接的层.
  • 通过对三个公共数据集进行主体独立交叉验证进行评估,并使用类激活映射 (CAM) 进行可视化.

主要成果:

  • 微睡眠网实现了83.3%的准确率和0.77.7的科恩卡帕.
  • 该模型显著减少了参数 (48,226) 和计算 (48.95 MFLOPs).
  • 在Android智能手机上实现实时性能 (100KB内存,每时代2.8ms推断).

结论:

  • 微型睡眠网提供了一个轻量级,高性能解决方案,用于实时移动睡眠阶段.
  • 该模型通过CAM可视化展示了强大的可解释性,支持实际应用.
  • 开发的算法符合实时要求,并有可能用于闭环睡眠调制系统.