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基于无处不在的非线性动力学和脑电图数据分析身体活动的影响.

Prashant Kumar Shukla1, Priti Maheshwary2, Shakti Kundu3

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India.

Technology and health care : official journal of the European Society for Engineering and Medicine
|August 7, 2023
PubMed
概括

这项研究使用非线性动力学理论来分析电脑电图 (EEG) 信号,开发一种算法来准确区分清醒和中毒状态,用于酒精诊断和监测.

关键词:
只有一个人,只有一个人.这是一个EEGEEGEEGEEGEEGEEGEEG.在 FE FE FE FE 里面.在 KSE KSE 上.这就是PE PE PE.在SVM中,SVM是SVM.一个样本的样本.这就是为什么TS TS TS TS.我们是什么?我们是什么?

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科学领域:

  • 神经科学是一个神经科学.
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 非线性动态系统理论为分析复杂的生理信号提供了工具.
  • 这种方法越来越多地用于理解生理数据的演变.

研究的目的:

  • 将非线性动力学应用于脑电图 (EEG) 信号,以了解酒精精神状态.
  • 开发一种算法,用于自动分类清醒与醉酒EEG信号.

主要方法:

  • 从EEG信号中提取出基于的特征 (ApEn,SampEn,Shannon,Renyi,PE,TS,FE,WE,KSE) 的数据.
  • 采用T-test,Wilcoxon和Bhattacharyya排名方法来选择相关的特征.
  • 训练有素支持向量机 (SVM) 分类器与选定的功能,优化与一个辐射基函数内核.

主要成果:

  • 巴塔查里亚排名方法与SVM相结合,实现了高分类性能.
  • 获得了95.89%的分类准确度,94.43%的灵敏度和96.67%的特异性.
  • 带有辐射基函数内核的SVM分类器显示了最佳的结果.

结论:

  • 开发的模型准确地区分了清醒和中毒的EEG信号.
  • 这为诊断酒症提供了一种具有成本效益的决策支持工具.
  • 它还可以监测康复中心的干预效果.