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

1.3K
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:
1.3K

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

Modified Posterior Approach for Scapular Body Non-union: A Case Report.

Journal of orthopaedic case reports·2026
Same author

Protocol for characterization of spatiotemporal network dynamics in cortical and hippocampal assembloids.

STAR protocols·2026
Same author

Peach Palm (<i>Bactris gasipaes</i>) as a Sustainable Source of Plant Proteins, Dietary Fiber and Other Functional Ingredients: Recovery Techniques and Functional Food Applications.

Foods (Basel, Switzerland)·2026
Same author

Physicochemical Properties and Fatty Acid Profiling of Texturized Pea Protein Patties Partially Replaced with Chia Seed Powder During Refrigerated Storage.

Foods (Basel, Switzerland)·2026
Same author

Unlocking the Potential of Peach Palm (<i>Bactris gasipaes</i> Kunth) for Plant-Based Foods: A Review of Nutritional, Techno-Functional, and Bioactive Attributes.

Foods (Basel, Switzerland)·2025
Same author

Decoding the distribution, structure-function-redox potential relationship and recent advances in fungal laccases: a systematic approach.

Bioprocess and biosystems engineering·2025
Same journal

From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.

medRxiv : the preprint server for health sciences·2026
Same journal

Large distant deletion disrupts CDKN2A enhancer and predisposes to melanoma.

medRxiv : the preprint server for health sciences·2026
Same journal

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives.

medRxiv : the preprint server for health sciences·2026
Same journal

Longitudinal MAP-MRI-based Assessment of Tissue Microstructural Alterations in Acute mTBI.

medRxiv : the preprint server for health sciences·2026
Same journal

A class of deep intronic <i>IGHMBP2</i> variants activate a shared cryptic splice donor, enabling correction of select variants with a single antisense oligonucleotide.

medRxiv : the preprint server for health sciences·2026
Same journal

Global Socioeconomic Context and Brain Ageing in Epilepsy: an ENIGMA-Epilepsy study.

medRxiv : the preprint server for health sciences·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.7K

使用多模式大语言模型进行自动抓获分类.

Lina Zhang1, Richard Jiang1, Tonmoy Monsoor1

  • 1Electrical and Computer Engineering, University of California, Los Angeles, California, USA.

medRxiv : the preprint server for health sciences
|November 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多式大型语言模型 (MLLMs) 方法,用于自动抓获分析. 在使用视频数据区分发作 (ES) 和非发作 (NES) 方面,MLLM方法显示出前景.

关键词:
音频语言模型 语言模型是一种病.多模式大型语言模型精神性非发作 精神性非发作查获分类 查获分类 查获分类语义学是什么意思 语义学是什么意思视觉语言模型 视觉语言模型

更多相关视频

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
06:28

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

3.2K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

相关实验视频

Last Updated: Jan 10, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.7K
Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
06:28

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

3.2K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 医学成像分析 医学成像分析

背景情况:

  • 将发作 (ES) 与非发作 (NES) 区分开来,在临床上是具有挑战性的,通常需要广泛的住院视频电脑电图 (EEG) 监测.
  • 对视频的自动分析有可能简化诊断并降低医疗保健成本.

研究的目的:

  • 开发和评估一种基于多模态大语言模型 (MLLMs) 的方法,用于从抓获视频中自动提取语义特征.
  • 使用提取的特征将事件分类为ES或NES.

主要方法:

  • 整合视觉语言模型 (VLM) 和音频语言模型 (ALM) 的MLLM框架被用于分析ES和NES事件的90个视频.
  • 模型自动提取了24个临床相关的半理学特征,这些特征与专家注释进行了比较.
  • 提取的特征被用来训练分类器 (KNN,XGBoost,深度分解机) 用于ES/NES差异化,使用离开一个患者的交叉验证.

主要成果:

  • 通过KNN实现高性能 (精度为0.97,回忆为0.97,F1得分为0.97,AUC为0.99).
  • 对于特征提取,MLLM管道的平均回忆率为0.71,平均准确率为0.58,平均F1得分为0.51.
  • 使用MLLM提取特征的最佳KNN模型实现了0.77精度,回忆0.76,F1得分0.76和AUC0.76,正确识别了68/90事件.

结论:

  • MLLMs可以从视频中切实提取临床相关的半理学特征,用于自动分析.
  • 这种基于MLLM的方法提供了一种有希望的,临床上可解释的方法,以帮助使用视频录制来诊断.