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

297
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:
297
State Space Representation01:27

State Space Representation

160
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
160
Arteries of the Lower Limbs01:24

Arteries of the Lower Limbs

176
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
176

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

Networks of respiratory-muscular coupling in exercise and fatigue in young adults.

Physiological reports·2026
Same author

ERG-Graph: Graph Signal Processing of the Electroretinogram for Classification of Neurodevelopmental Disorders.

Bioengineering (Basel, Switzerland)·2026
Same author

Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.

Applied sciences (Basel, Switzerland)·2026
Same author

Frontal Alpha Asymmetry and Electrodermal Activity: A Mutual Information Analysis Across Cognitive Load and Sleep Deprivation.

Biosensors·2026
Same author

Recent trends in electrodermal activity signal processing and deep learning methods for emotion recognition.

Neuroscience·2026
Same author

Exploring Deep Learning in Electrodermal Activity Recording for Continuous Detection of Pain Intensity Level.

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

相关实验视频

Updated: May 24, 2025

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala
09:49

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

2.4K

使用相位空间域和机器学习算法预测状态.

Boluwatife Faremi, Yedukondala Rao Veeranki, Hugo F Posada-Quintero

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究通过分析电脑电图 (EEG) 数据的相位变换引入了一种用于的新方法. 这种方法获得了91.5%的准确性,为预测状态提供了一个有前途的工具.

    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

    2.1K
    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

    12.1K

    相关实验视频

    Last Updated: May 24, 2025

    Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala
    09:49

    Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

    Published on: June 29, 2022

    2.4K
    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

    2.1K
    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

    12.1K

    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
    • 数据科学数据科学数据科学

    背景情况:

    • 影响全球数百万人,需要先进的诊断工具.
    • 目前的诊断依赖于神经成像和脑电图 (EEG) 分析,这可能是复杂的.
    • 需要预测系统来改进使用新功能检测状态.

    研究的目的:

    • 调查状态的预测和检测.
    • 探索将二维EEG时间序列数据转换为相位空间域的功效,以提取特征.
    • 开发一种用于发病检测的预测模型.

    主要方法:

    • 脑电图时间序列数据被转换为相位空间域.
    • 计算了角距离和概率密度函数以提取相位空间特征.
    • 提取了Renyi和Tsallis复杂特征,并用于训练各种机器学习模型.
    • 使用"离开一个受试者"的交叉验证来评估模型的性能.

    主要成果:

    • 在相域复杂特征上训练的概率模型达到91.5%的准确性.
    • 这种准确性超过了其他评估的学习算法.
    • 阶段空间域证明有效地提取歧视性特征.

    结论:

    • 阶段空间领域为的检测和预测提供了一个有前途的方法.
    • 开发的方法在识别状态方面表现出高度准确性.
    • 这种技术有可能改善管理中的诊断能力.