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

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
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
RNA-seq03:21

RNA-seq

10.1K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.1K

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

Metabolomics reveal drought-stress responses in guayule, a semi-arid rubber crop.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Comparative Efficacy of Continuous Versus Patch ECG Monitoring in Detecting Atrial Fibrillation Recurrence Post Catheter Ablation: Insights From the REAL-AF Registry.

Journal of cardiovascular electrophysiology·2026
Same author

Brain activity as a candidate biomarker for personalised caffeine treatment in premature neonates.

Frontiers in pediatrics·2026
Same author

Prototype-based sleep micro-structure learning for explainable and robust multimodal recognition of sleep-related conditions.

Research square·2026
Same author

Medication, Vaccine, and Folic Acid Use Among Pregnant Women in Belgium: Insights from the BELpREG Cohort.

Pharmacoepidemiology and drug safety·2026
Same author

Reliability of Self-Reported Maternal Health and Mother-Infant Outcome Data in Web-Based Questionnaires.

Drug safety·2026
Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026
Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026
Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026
Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

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

L-SeqSleepNet:用于自动睡眠分期的全周期长序列建模.

Huy Phan, Kristian P Lorenzen, Elisabeth Heremans

    IEEE journal of biomedical and health informatics
    |August 8, 2023
    PubMed
    概括
    此摘要是机器生成的。

    人类睡眠周期包含关键的长期依赖睡眠阶段. 一个新的深度学习模型,L-SeqSleepNet,有效地模拟这些全周期模式,以改善睡眠阶段的分类.

    更多相关视频

    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
    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
    10:56

    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

    Published on: August 2, 2017

    10.1K

    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
    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
    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
    10:56

    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

    Published on: August 2, 2017

    10.1K

    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.
    • 睡眠科学 睡眠科学

    背景情况:

    • 人类的睡眠表现出大约90分钟的周期性模式,这表明睡眠数据的长期时间依赖性很大.
    • 现有的睡眠阶段化深度学习模型往往无法有效地捕捉整个周期的动态,从而限制了性能.
    • 数据集中N2睡眠的占主导地位可能会扭曲模型性能,需要平衡所有阶段分类的方法.

    研究的目的:

    • 通过结合长期时间依赖来解决睡眠阶段化当前顺序建模方法的局限性.
    • 引入一种高效的长序列建模方法和一种新的深度学习架构,L-SeqSleepNet,用于睡眠分阶段.
    • 提高睡眠阶段的稳定性和准确性,特别是对于具有挑战性的病例和代表性不足的睡眠阶段.

    主要方法:

    • 开发了一种针对睡眠数据量身定制的高效长序列建模的新方法.
    • 提出了L-SeqSleepNet,这是一个深度学习模型,旨在整合整个睡眠周期的信息.
    • 评估了L-SeqSleepNet在四个不同的睡眠数据库中,使用三个电脑电图 (EEG) 设置 (PSG,入耳,cEEGrid),包括单通道配置.

    主要成果:

    • 在不同的EEG设置和数据库大小中,L-SeqSleepNet实现了最先进的性能.
    • 该模型证明了对所有睡眠阶段的分类准确度有所提高,减轻了对N2睡眠的偏差.
    • 对于以前使用基线方法表现不佳的受试者来说,观察到显著的性能改善.
    • 计算时间随着序列长度的增加而分线缩放,表明效率.

    结论:

    • L-SeqSleepNet有效地捕获关键的整个睡眠周期的信息,优于现有的睡眠分阶段的方法.
    • 该模型提供了增强的稳定性和准确性,即使数据有限 (单个EEG通道) 和具有挑战性的数据集.
    • 高效的长序列建模方法使得L-SeqSleepNet成为高级睡眠分析和临床应用的有希望的工具.