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

Machine Learning-driven ADHD Classification: Exploring Medication Effects with VMD Sub-band Analysis.

Current computer-aided drug design·2026
Same author

Differential Functional Connectivity Between Silent Reading and Resting-State fMRI and Their Relationships With Reading Performance in Children With and Without Dyslexia.

International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience·2025
Same author

Assessment of alterations in regional homogeneity and amplitude of low-frequency fluctuations in children with dyslexia.

Turkish journal of medical sciences·2025
Same author

Data-driven exploratory method investigation on the effect of dyslexia education at brain connectivity in Turkish children: a preliminary study.

Brain structure & function·2024
Same author

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques.

Diagnostics (Basel, Switzerland)·2023

相关实验视频

Updated: Jul 7, 2025

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.1K

用EEG信号对第一发精神病的分类:cissa和机器学习方法.

Şerife Gengeç Benli1

  • 1Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey.

Biomedicines
|December 23, 2023
PubMed
概括

这项研究引入了一种使用循环光谱分析 (ciSSA) 的新型EEG分析方法,用于早期检测首发精神病 (FEP). 该方法实现了高准确度,证明了其改善心理健康诊断的潜力.

科学领域:

  • 神经科学是一个神经科学.
  • 精神病学是一个精神病学.
  • 信号处理 信号处理

背景情况:

  • 第一次发作精神病 (FEP) 表示严重精神疾病的出现,使得早期诊断对有效干预和更好的患者结果至关重要.
  • 准确及时诊断FEP在精神卫生保健中是一个重大挑战.
  • 脑电图 (EEG) 信号为精神病的客观诊断标记提供了一个潜在的途径.

研究的目的:

  • 开发和验证一种新的高性能分类方法,用于FEP的早期诊断.
  • 调查循环频谱分析 (ciSSA) 从EEG获得的子频段信号对FEP分类的有用性.
  • 识别和利用EEG信号的重要特征,用于基于机器学习的FEP检测.

主要方法:

  • 使用循环频谱分析 (ciSSA) 分析了EEG信号,以提取子频段特征.
  • 采用LASSO方法进行特征选择,重点关注cissa子频段及其组合中的,频率和统计特征.
  • 机器学习模型,包括组合方法,支持矢量机 (SVM) 和人工神经网络 (ANN),被用于分类.

主要成果:

  • 来自EEG ciSSA子频段的混合特性与SVM分类器相结合,产生了卓越的性能.
  • 关键性能指标包括0.9893的曲线下面面积 (AUC),96.23%的准确性,0.966的灵敏度,0.956的特异性,0.9667的精度和0.9666的F1得分.
关键词:
循环的频谱分析.电脑脑电图 (EEG) 是一种电脑电图.第一个情节精神病的精神病.机器学习是机器学习.

更多相关视频

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

905
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

相关实验视频

Last Updated: Jul 7, 2025

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.1K
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

905
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
  • 这些发现强调了基于cissa的方法在使用EEG数据对FEP进行分类的有效性.

    • 结论:

    • 基于cissa的方法显示出作为一种有效的工具,具有显著的潜力,用于从EEG信号中早期和准确地分类FEP.
    • 这种方法为首发精神病的客观诊断策略提供了有希望的进步.
    • 进一步的研究可以探索这种EEG分析技术的临床整合,以改善心理健康诊断.