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

378
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:
378

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

Spatio-Temporal Attention with Spiking Neural Networks for Seizure Detection from Electroencephalogram Signals.

International journal of neural systems·2026
Same author

Enhancing Cross-Patient Seizure Detection with Test-Time Adaptation.

International journal of neural systems·2026
Same author

Epileptic Seizure Detection from EEG Signals with Long Short-Term Memory-Transformer and Self-Supervised Learning.

International journal of neural systems·2026
Same author

Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.

International journal of neural systems·2025
Same author

FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.

Journal of neural engineering·2025
Same author

Cross-Subject Seizure Detection via Unsupervised Domain-Adaptation.

International journal of neural systems·2024

相关实验视频

Updated: Jul 12, 2025

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
06:45

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue

Published on: January 19, 2019

9.0K

混合网络用于从EEG数据预测患者特定的发作.

Yongfeng Zhang1, Tiantian Xiao1, Ziwei Wang1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China.

International journal of neural systems
|October 30, 2023
PubMed
概括

这项研究引入了STCNN,一种混合深度学习模型,结合了Swin变压器和2D CNN,用于改善发作预测. 这种新的方法提高了使用电脑电图数据预测的准确性.

关键词:
在美国,CNN是CNN.这是一个EEGEEGEEGEEGEEGEEGEEG.抢劫预测预测的预测斯温变压器是什么意思

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
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

12.4K

相关实验视频

Last Updated: Jul 12, 2025

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
06:45

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue

Published on: January 19, 2019

9.0K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
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

12.4K

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:

  • 影响全球数以百万计的人,耐药性病例显著影响生活质量.
  • 准确的发作预测对于治疗至关重要,特别是在耐药形式.
  • 当前的深度学习模型,特别是单个卷积网络,在捕捉EEG数据中的长期依赖性方面存在困难.

研究的目的:

  • 开发一种先进的深度学习模型,用于准确预测发作.
  • 解决单一卷积模型在捕捉全球和长期EEG特征方面的局限性.
  • 提高对抗药性患者的预测系统的性能.

主要方法:

  • 开发了一种混合深度学习模型,STCNN,集成Swin变压器 (ST) 和2D卷积神经网络 (2DCNN).
  • 时间频率特征使用短期里叶变换 (STFT) 作为STCNN模型的输入来提取.
  • ST块捕获了全球信息和长期依赖,而2DCNN块则专注于本地和短期特征.

主要成果:

  • 该STCNN模型实现了92.94%的平均预测灵敏度.
  • 在ROC曲线下的面积 (AUC) 达到95.56%,表明高预测准确度.
  • 记录了0.073的低虚假阳性率 (FPR),证明了该模型的可靠性.

结论:

  • 拟议的STCNN模型有效地结合了全球和本地特征提取,以便更好地预测发作.
  • 这种混合方法显著改善了基于EEG的预测的传统单卷模型.
  • 该STCNN模型显示了在改善患者生活方面具有很强的临床应用潜力.