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

Seizures: Classification01:13

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

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

Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.

Diagnostics (Basel, Switzerland)·2026
Same author

Unraveling the degradation mechanisms of commercial cylindrical sodium-ion batteries under high cut-off voltages.

RSC advances·2026
Same author

Single-nucleus transcriptomic atlas of postnatal camel liver development identifies candidate adaptive features.

BMC genomics·2026
Same author

SHREC 2025: Protein surface shape retrieval including electrostatic potential.

Computers & graphics·2026
Same author

Treatment Patterns and Barriers to Care Among U.S. Adults With Co-Occurring Substance Use Disorder and Mental Illness.

The American journal of psychiatry·2026
Same author

Orbital-Engineered Sn/RuO<sub>2</sub> Nanocatalyst with Self-Regulating Electron Configuration for Durable Chlorine Evolution at Industrial Current Densities.

ACS applied materials & interfaces·2026

相关实验视频

Updated: May 10, 2026

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
05:54

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

Published on: June 13, 2016

17.9K

CNN-Autoformer:使用混合深度学习实现基于EEG的自动发作检测和定位.

Shuhao Ren1, Haotian Li1, Weisen Lu1

  • 1School of Integrated Circuits, Shandong University, Jinan 250100, P.R.China.

Biomedical signal processing and control
|November 5, 2025
PubMed
概括

一个新的CNN-Autoformer深度学习模型从EEG数据准确地检测发作. 这种框架还可以准确地定位发作发作,改善诊断和治疗潜力.

关键词:
自动成型器的自动成型器自动抓捕检测自动抓捕检测卷积神经网络 (CNN) 是一种神经网络.电脑电图 (EEG) 是一个电脑电图.发作局部化的地方

更多相关视频

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
06:58

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement

Published on: June 25, 2016

19.9K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

3.4K

相关实验视频

Last Updated: May 10, 2026

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
05:54

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

Published on: June 13, 2016

17.9K
Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
06:58

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement

Published on: June 25, 2016

19.9K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

3.4K

科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 的诊断依赖于手动EEG分析,这是耗时和主观的.
  • 目前的深度学习发作检测方法与复杂的EEG信号动态和发作局部化作斗争.
  • 准确和自动的发作检测和定位对于有效的管理至关重要.

研究的目的:

  • 开发一种新的混合深度学习框架,以改善发作的检测和定位.
  • 解决噪声EEG信号的时空动态建模方面的局限性.
  • 提高自动诊断的解释性和临床适用性.

主要方法:

  • 提出了一种混合CNN-Autoformer框架,将卷积神经网络 (CNN) 用于空间特征和Autoformer用于时间建模.
  • 利用CNN捕获多通道EEG中的通道间相关性.
  • 采用了Autoformer的自动相关机制,用于周期依赖和信号分解.

主要成果:

  • 实现了基于细分的高性能:98.34%的准确性,99.46%的灵敏性,97.12%的CHB-MIT数据集的特异性.
  • 证明了100%的基于事件的灵敏度,低的错误检测率 (0.21事件/小时).
  • 为定位生成了发作发作热图,与专家注释进行验证,在SH-SDU数据集上显示了可比性能.

结论:

  • 该CNN-Autoformer框架提供了强大的和可解释的发作检测和定位.
  • 该模型显示了在诊断中实现现实世界的临床整合的巨大潜力.
  • 这种方法提高了分析神经系统疾病的脑电图 (EEG) 数据的准确性和效率.