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

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

Prophylactic Methylprednisolone in Neonatal On-Pump Cardiac Surgery.

Reviews in cardiovascular medicine·2026
Same author

Multicenter external validation of the FW-TRIC score for predicting red blood cell transfusion in on-pump cardiac surgery.

Perioperative medicine (London, England)·2026
Same author

Immediate postoperative urine dipstick occult blood positivity: a simple and early indicator of high-risk acute kidney injury after on-pump cardiac surgery.

BMC nephrology·2026
Same author

Association between nadir albumin concentration and mortality in pediatric patients undergoing postcardiotomy extracorporeal membrane oxygenation.

Translational pediatrics·2026
Same author

External validation of ECPR prognostic models derived from pre-ECMO indicators in patients undergoing extracorporeal cardiopulmonary resuscitation.

Resuscitation plus·2026
Same author

Trajectories of the systemic pulsatility index after CF-LVAD implantation and clinical impact.

BMC cardiovascular disorders·2026

相关实验视频

Updated: Jan 7, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K

一种基于微态分析和图形卷积网络的耐火诊断的创新方法.

Wenwen Chang1, Dandan Li2, Bingyang Ji1

  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.

Journal of medical systems
|December 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于耐火性的新型EEG微态分析框架,在发作分类中达到80.2%的准确性. 该方法有效地模拟动态微状态转换,以改善诊断.

关键词:
定向图形卷积网络 定向图形卷积网络微态分析 微态分析耐火性是一种耐药性.休息中的电脑脑电图.

更多相关视频

Performing Behavioral Tasks in Subjects with Intracranial Electrodes
12:10

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Published on: October 2, 2014

11.8K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.6K

相关实验视频

Last Updated: Jan 7, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K
Performing Behavioral Tasks in Subjects with Intracranial Electrodes
12:10

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Published on: October 2, 2014

11.8K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.6K

科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 医疗信息学 医疗信息学

背景情况:

  • 耐火性 (RE) 带来了诊断方面的挑战.
  • 传统的脑电图 (EEG) 分析在临床环境中存在局限性.
  • 脑电图微态提供了一种新的方法来理解大脑动态.

研究的目的:

  • 系统地调查电脑电图显微状态在耐性患者的变化,跨发作阶段.
  • 开发和验证一个新的EEG微态分析框架,用于查识别和分类.
  • 将拟议的框架与传统方法进行比较,以提高诊断准确度.

主要方法:

  • 利用两个独立的数据集,在四个抓获阶段中提取微状态特征.
  • 构建了一个定向微态图形结构.
  • 使用定向图形卷积网络 (DGCN)进行分类,称为 MsG-GCN.
  • 与支持矢量机 (SVM) 等传统方法相比,比较了 MsG-GCN 的性能.

主要成果:

  • 拟议的MSG-GCN框架实现了80.2%的分类准确度,超过了最佳传统方法 (SVM) 的74.3%.
  • 微态A和C在发作阶段之间显示出显著的差异.
  • 平均微观状态发生率显示出比持续时间或覆盖范围更高的歧视力.

结论:

  • 这项研究提出了一个新的,高度可解释的框架 (MsG-GCN) 用于自动性发作分类.
  • 图形神经网络有效地模拟动态性微状态过渡.
  • 该框架为像这样的神经系统疾病的智能辅助诊断提供了一个有前途的工具.