Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Spatial heterogeneity of the ventricular interface between the median eminence and arcuate nucleus revealed by an unfolded whole-mount view.

Fluids and barriers of the CNS·2026

Same author

Multivalent Bi-Specific Nanoerythrosomes: A Two-Birds-One-Stone Approach to Combine Homologous and Active Targeting for Effective Thrombolysis.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Bioinspired synthetic strategies for enhanced cell membrane interactions for targeted delivery of biomacromolecules.

Expert opinion on drug delivery·2026

Same author

Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.

BMC infectious diseases·2026

Same author

Oxytocin alleviates the cognitive and memory dysfunction caused by neuroinflammation through blocking the TLR4/NLRP3/NF-κB signaling pathway.

Brain research bulletin·2026

Same author

Single-Incision Plus One-Port Laparoscopic Choledocholithotomy with Primary Suture for Choledocholithiasis.

Journal of visualized experiments : JoVE·2026

Search research articles

相关实验视频

Updated: Jun 9, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

一种轻量级的多重精神障碍检测方法,使用单通道EEG信号的基于热的矩阵.

Jiawen Li^1,2, Guanyuan Feng¹, Jujian Lv¹

¹School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.

|October 25, 2024

概括

这项研究引入了一种新的脑电图 (EEG) 分析方法,用于早期检测多重精神障碍. 通过分析的特征,它以最小的数据实现了高精度,改善了对精神分裂症,和抑郁症等疾病的诊断.

关键词:

电脑电图 (EEG) 是一种电脑电图.进入的过程中,机器学习是机器学习.精神障碍的检测精神障碍的检测

更多相关视频

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

相关实验视频

Last Updated: Jun 9, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

科学领域:

神经科学是一个神经科学.
计算精神病学是一种计算精神病学.
生物医学工程生物医学工程

背景情况:

心理健康障碍是一个日益增长的全球性挑战,需要改进的诊断工具.
目前用于心理健康状况的诊断方法往往是主观的,耗时的.
对于多重精神障碍的客观,高效和早期检测方法有极大需求.

研究的目的:

开发一种轻量级,数据高效的方法,用于早期检测多种精神障碍.
加强诊断程序,并为受影响的个人提供及时干预.
探索脑电图 (EEG) 信号分析对心理健康评估的有用性.

主要方法:

使用脑电图 (EEG) 信号作为主要数据源.
应用离散波纹分解 (DWT) 来获取大脑节奏.
提取了各种度 (近似,模糊,变换,样本度) 来创建一个基于度的矩阵.
使用机器学习分类器 (SVM,kNN,NB,GAM,LDA,DT) 进行疾病检测.
使用公共数据集验证了用于精神分裂症,和抑郁症的方法.

主要成果:

确定了每个疾病的代表性单通道EEG信号 (精神分裂症的O1,的F3,抑郁症的O2).
获得了高分类准确率:精神分裂症为88.10%,为75.47%,抑郁症为89.92%.
通过最小的数据输入,证明了有效的多重精神障碍检测.

结论:

拟议的轻量级EEG分析方法为早期多精神障碍检测提供了一种可靠的方法.
该方法提高了EEG信号中特征的解释性.
这种方法有助于更好地理解精神疾病的潜在机制和病理状态,为改善患者的治疗结果铺平了道路.