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在多模式EEG上使用转移学习进行发焦点定位.

Yong Yang1,2,3, Feng Li4, Jing Luo4

  • 1Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China.

Frontiers in computational neuroscience
|December 11, 2023
PubMed
概括
此摘要是机器生成的。

使用多模电脑图 (EEG) 的新转移学习方法准确地定位了耐药性焦点. 这种方法提高了诊断的准确性,帮助医生在临床定位和治疗规划.

关键词:
进行对抗性培训.发作焦点定位局部化多式联网电脑电流 (EEG) 的多式联网电流.独立于患者,独立于患者.转移学习转移学习

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科学领域:

  • 神经学 神经学
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 影响着数百万人,其中很大一部分患有耐药形式.
  • 目前的治疗方法包括药物治疗和手术切除,但对于大约1/3的患者来说,定位焦点仍然具有挑战性.
  • 精确地定位焦点对于有效的手术干预至关重要.

研究的目的:

  • 开发和验证一种新的转移学习方法,用于定位耐药焦点.
  • 通过使用多模电脑图 (EEG) 来提高焦点识别的准确性和效率.
  • 提供一个工具,帮助医生在临床诊断和治疗治疗计划.

主要方法:

  • 提出了一种转移学习方法,利用内EEG (iEEG) 和表面EEG (sEEG) 数据.
  • 在伯尔尼-巴塞罗那和波恩数据集上使用10倍交叉验证验证预训练模型.
  • 将模型与重庆医科大学的病数据进行了微调,并使用leave-one-out交叉验证进行了测试.

主要成果:

  • 预训练模型实现了94.50% (伯尔尼-巴塞罗那) 和97.50% (波恩) 的高准确率.
  • 在准确性,灵敏性和负预测值方面表现优于最先进的基线.
  • 在重庆医科大学的数据集中达到90.15%的平均准确度,突出了和非通道之间的显著特征差异.

结论:

  • 拟议的转移学习方法通过使用神经网络准确地分类和非通道,有效地定位焦点.
  • 该模型的卓越性能表明其作为临床诊断的宝贵工具的潜力.
  • 这种技术可以显著帮助医生精确地定位焦点,改善患者的治疗结果.