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基于动态EEG通道查的检测方法的优化.

Yuebin Song1, Chunling Fan1, Xiaoqian Mao1

  • 1College of Automation and Electronic Engineering, Qingdao University of Science and Technology Qingdao, 266061, China.

Neural networks : the official journal of the International Neural Network Society
|January 17, 2024
PubMed
概括

这项研究引入了一种使用动态脑电图 (EEG) 通道查来提高精度的新检测方法. 这种方法增强了特征提取,并实现了不同主体的高检测率.

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
  • 信号处理 信号处理

背景情况:

  • 由于焦点的干扰和有限的通道能力,发病检测面临挑战.
  • 有效的特征提取对于准确的诊断和跨主体概括至关重要.

研究的目的:

  • 提出一种使用动态电脑电图 (EEG) 通道查的新检测方法.
  • 为了增强特征提取,并使强大的跨主体检测.
  • 为临床检测提供有效的方法和理论支持.

主要方法:

  • 使用精细复合多尺度分散 (RCMDE) 来测量信号复杂性的动态EEG频道选.
  • 从查的道中识别患者中常见的特征 (3-15 Hz).
  • 构建一个残余卷积长短期记忆 (ResCon-LSTM) 神经网络用于检测.

主要成果:

  • 单个患者的检测准确率达到98.523%,比非道查有5.298%的改善.
  • 跨主体的检测达到96.596%的平均准确率.
  • 该方法在动态选最佳通道以获得良好的检测性能方面表现出有效性.

结论:

关键词:
动态道选 动态道选的检测的检测这是RCMDEDE的RCMDE.这是一个ResCon-LSTM神经网络.在STFT,STFT和STFT中使用.

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  • 动态EEG频道查显著提高了的检测能力和稳定性.
  • 拟议的方法成功地确定了常见的特征,用于跨主体检测.
  • 这种方法为跨多个学科的临床检测提供了一个有希望的解决方案.