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相关概念视频

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

53
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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相关实验视频

Updated: May 22, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
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自学EEG歧视模型与弱监督的样本构造:基于年龄的视角对ASD评估.

Tengfei Gao1, Dan Chen2, Meiqi Zhou2

  • 1National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China; Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China.

Neural networks : the official journal of the International Neural Network Society
|March 15, 2025
PubMed
概括

本研究介绍了自训练EEG模型 (STEM) 框架,以改进使用脑电图 (EEG) 数据检测自闭症谱系障碍 (ASD) 的深度学习,克服有限样本和受试者个性的挑战.

关键词:
自闭症谱系障碍 自闭症谱系障碍电脑脑电图 (EEG) 是一种电脑电图.模型自我训练的模型.多任务学习是多任务学习.伪标签是一种伪标签.一个样本的建筑结构样本.

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科学领域:

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

背景情况:

  • 对于脑电图 (EEG) 的深度学习模型在大脑障碍歧视方面表现出色,但在有限的标记数据和个体学科变异方面扎,特别是在自闭症谱系障碍 (ASD) 中.
  • 由于数据稀缺以及个体受试者的独特特征,现有方法在优化EEG模型方面面临挑战.

研究的目的:

  • 开发一个有效的框架,STEM (自我训练EEG模型),以优化EEG歧视模型在有限的标记样本和受试者的个性存在时.
  • 通过使用EEG数据,提高深度学习模型用于自闭症谱系障碍 (ASD) 检测的性能.

主要方法:

  • 开发了STEM框架,利用多任务学习进行模型初始化,将AutoEncoder与分类器结合起来,以学习EEG表示和预测概率.
  • 实施了伪标记样本构建 (PLASC) 方法,将可信的伪标签分配给未标记的样本,帮助自我训练和模型优化.
  • 在AutoEncoder中使用深度可分离的卷积和BiGRU,通过重建任务进行全面的EEG表示学习.

主要成果:

  • 在使用175名儿童的静止状态EEG数据,STEM框架在ASD歧视方面取得了卓越的表现,准确率为88.33%,F1得分为87.24%.
  • 当标记数据稀缺时,STEM的多任务学习优于传统的监督方法.
  • 与现有方法相比,PLASC方法显著提高了不同年龄组的ASD歧视准确性 (3%-8%) 和F1得分 (4%-10%).

结论:

  • 在基于EEG的深度学习中,STEM框架有效地解决了数据稀缺性和主体个性的局限性,用于大脑障碍歧视.
  • 在复杂的诊断场景中提高模型的准确性和适应性,例如ASD检测.
  • 拟议的多任务学习和伪标签策略为改善深度学习模型性能提供了强大的解决方案,使用有限的标签EEG数据.