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

Frequency Band Personalization for Seizure Network Analysis in Multifocal Patients.

International journal of neural systems·2026
Same author

AI-Based Performance Analysis for Track and Field Athletes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.

Genome medicine·2025
Same author

Education Research: Enhancing Medical Student Interest in Careers in the Clinical Neurosciences Through a Hands-on Procedure Workshop.

Neurology. Education·2025
Same author

Brivaracetam effectiveness and patient-reported outcomes in clinical practice: Data from a 12-month prospective, observational study in the United States.

Epilepsy & behavior : E&B·2025

相关实验视频

Updated: Jul 8, 2025

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.4K

使用多头深卷积神经网络进行间接性发性放电检测.

Munawara Saiyara Munia, Mehrdad Nourani, Jay Harvey

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    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,用于在EEG数据中自动检测间接性发性放电 (IED),提高精度并减少诊断的手动审查.

    更多相关视频

    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
    10:23

    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

    Published on: June 23, 2023

    2.0K
    Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
    07:43

    Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

    Published on: June 17, 2019

    7.8K

    相关实验视频

    Last Updated: Jul 8, 2025

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
    10:22

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

    Published on: December 6, 2016

    20.4K
    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
    10:23

    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

    Published on: June 23, 2023

    2.0K
    Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
    07:43

    Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

    Published on: June 17, 2019

    7.8K

    科学领域:

    • 神经科学是一个神经科学.
    • 生物医学工程 生物医学工程
    • 人工智能的人工智能

    背景情况:

    • 间接性发性排泄 (IED) 是的关键生物标志物,表明之间的皮质刺激.
    • 用于IED检测的脑电图 (EEG) 的手动解释是耗时的,容易引起观察者之间的变化.
    • 需要自动检测系统来帮助临床医生识别IED并预测发作复发.

    研究的目的:

    • 开发和评估一种新的深度学习方法,用于准确有效地自动检测IED.
    • 为了提高IED识别,利用EEG子频段的独特形态模式.
    • 减少医生对手动EEG解释的依赖.

    主要方法:

    • 开发了一种新的深度学习模型,将1D本地二进制模式符号化与规范化的多头1D卷积神经网络相结合.
    • 该模型被训练来学习来自不同EEG子频段的形态模式.
    • 该方法使用普大学事件的头皮脑电图数据验证了该方法.

    主要成果:

    • 拟议的深度学习方法在IED检测方面取得了87.18%的F1得分.
    • 该系统证明了从EEG子频段有效地学习独特的形态模式.
    • 结果表明,自动化IED检测能力取得了重大进展.

    结论:

    • 新的深度学习方法为的自动化IED检测提供了一个有希望的解决方案.
    • 这种方法有可能增强临床决策和减少手动EEG分析的负担.
    • 对不同的EEG数据集进行进一步验证是有必要的,以确认可通用性.