首页> 外文会议>IEEE International Symposium on Technology and Society >Interictal EEG denoising using independent component analysis and empirical mode decomposition
【24h】

Interictal EEG denoising using independent component analysis and empirical mode decomposition

机译:使用独立成分分析和经验模态分解的间质性脑电图去噪

获取原文

摘要

Noise contamination is inevitable in biomedical recordings. In some cases biomedical recordings are highly contaminated with artifacts which make the effective recovering process hard to achieve. Many different methods have been proposed for artifact removal from biomedical signals but introducing an effective method which can present valuable data for medical analysis, is still an ongoing process. In this paper a new method for interictal EEG denoising is presented. Single-channel ICA denoising method based on EMD decomposition is used to improve the multi-channel ICA denoising results. This method is tested on simulated epileptic recordings which are contaminated with real muscle artifact and EEG background activity.
机译:在生物医学记录中不可避免地会产生噪声污染。在某些情况下,生物医学记录被伪影高度污染,从而难以实现有效的恢复过程。已经提出了许多不同的方法来从生物医学信号中去除伪影,但是引入一种能够为医学分析提供有价值的数据的有效方法仍然是一个持续的过程。在本文中,提出了一种新的脑电间质去噪方法。采用基于EMD分解的单通道ICA去噪方法,提高了多通道ICA去噪效果。该方法在模拟的癫痫记录上进行了测试,这些记录被真实的肌肉伪影和EEG背景活动所污染。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号