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

Classifying Matter by Composition03:35

Classifying Matter by Composition

90.2K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
90.2K
Classifying Matter by State02:49

Classifying Matter by State

103.0K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
103.0K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.7K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.7K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.2K
Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

3.2K
Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
3.2K
Seed Structure and Early Development of the Sporophyte02:33

Seed Structure and Early Development of the Sporophyte

31.0K
Seed structures are composed of a protective seed coat surrounding a plant embryo, and a food store for the developing embryo. The embryo contains the precursor tissues for leaves, stem, and roots. The endosperm and cotyledons—seed leaves—act as the food reserves for the growing embryo.
31.0K

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

Correction: Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study.

JMIR AI·2026
Same author

Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study.

JMIR AI·2026
Same author

Automated <i>Mycobacterium tuberculosis</i> Detection in Multivariant Digitized Ziehl-Neelsen Staining Using Faster R-CNN Method.

International journal of biomedical imaging·2026
Same author

Development and evaluation of a convolutional neural network model for sex prediction using cephalometric radiographs and cranial photographs.

BMC medical imaging·2025
Same author

Markers of Vascular Function and Future Coronary Artery Disease Risk Among Malaysians with Individual Cardiovascular Risk Factors.

Biomedicines·2025
Same author

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2023
Same journal

Correction: Yalçın et al. Impact of SGLT2 Inhibitors on Cardiovascular Risk Scores, Metabolic Parameters, and Laboratory Profiles in Type 2 Diabetes. <i>Life</i> 2025, <i>15</i>, 722.

Life (Basel, Switzerland)·2026
Same journal

Correction: Schubert et al. Minimally Invasive Ablation Strategies for Renal Cell Carcinoma Patients Ineligible for Surgery. <i>Life</i> 2026, <i>16</i>, 73.

Life (Basel, Switzerland)·2026
Same journal

Blood Group Antigen Combinations and COVID-19: Complexity, Associations and Possible Clinical Relevance.

Life (Basel, Switzerland)·2026
Same journal

Beyond HPV in Eastern Europe: Genotype Distribution, Molecular Biomarkers, Vaginal Microbiome, and Implications for Cervical Cancer Prevention.

Life (Basel, Switzerland)·2026
Same journal

Therapeutic Effects of <i>Scutellaria baicalensis</i> Georgi Extract and Baicalein on Olfactory Dysfunction and Neurobehavioral Alterations in a Methimazole-Induced Injury Model.

Life (Basel, Switzerland)·2026
Same journal

The Effects of Unstable Strength Training on Lower Limb Stability in Adolescent Volleyball Players in China.

Life (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 29, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.8K

支持EMG频谱的CNN中风分类器模型开发

Katherine1, Riries Rulaningtyas1, Kalaivani Chellappan2

  • 1Biomedical Engineering, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia.

Life (Basel, Switzerland)
|January 28, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,使用电肌谱 (EMG) 谱图来准确地分类中风患者. 这种方法增强了客观的中风评估,并自动化了家庭康复 (HBR) 的康复监测.

关键词:
在美国,CNN是CNN.在EMGEMGEMGEMGEMGEMGEMGEMGEMGEM深度学习是一种深度学习.频谱图谱是指一个光谱图.脑中风的分类 脑中风的分类

更多相关视频

Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis
06:07

Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis

Published on: May 6, 2021

7.6K
Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.9K

相关实验视频

Last Updated: Jan 29, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.8K
Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis
06:07

Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis

Published on: May 6, 2021

7.6K
Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.9K

科学领域:

  • 生物医学工程 生物医学工程
  • 神经康复疗法 神经康复疗法
  • 医疗保健中的机器学习

背景情况:

  • 脑卒中是长期残疾的主要原因,导致运动功能障碍和生产力下降.
  • 对康复服务的获取有限,特别是在低收入和中等收入国家,阻碍了中风后的康复.
  • 目前的家庭康复 (HBR) 依赖于主观评估,强调需要客观评估方法,如电肌学 (EMG).

研究的目的:

  • 开发和验证使用EMG信号进行客观中风评估的新型深度学习 (DL) 方法.
  • 根据EMG数据,自动化对中风患者与健康患者的分类.
  • 探索这种方法在加强中风康复程序和在HBR环境中的监测方面的潜力.

主要方法:

  • 电磁波信号被转化为时间频率表示 (TFR) 谱图.
  • 一个新的卷积神经网络 (CNN) 模型,Tri-CCNN,是使用这些光谱图作为输入来开发的.
  • 将Tri-CCNN模型的性能与浅CNN和LeNet-5架构进行了比较.

主要成果:

  • 拟议的 Tri-CCNN 模型实现了 93.33% 的分类准确性,超过了现有模型.
  • 对谱图振幅分布的分析揭示了明确的模式,使中风患者与健康人区分开来.
  • 这些发现表明该方法对客观中风评估和分类的潜力.

结论:

  • 使用EMG光谱图开发的DL方法为客观的中风分类提供了有效的工具.
  • 这种方法在家庭康复 (HBR) 设置中实现自动化康复监控方面具有显著的前景.
  • 这项研究为改善中风康复策略和可访问性铺平了道路.