...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical signal processing and control >Classification of seizure and seizure-free EEG signals using local binary patterns
【24h】

Classification of seizure and seizure-free EEG signals using local binary patterns

机译：使用局部二进制模式对癫痫发作和无癫痫发作的脑电信号进行分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Local binary pattern (LBP) is a texture descriptor that has been proven to be quite effective for various image analysis tasks in image processing. In this paper one-dimensional local binary pattern (1D-LBP) based features are used for classification of seizure and seizure-free electroencephalogram (EEG) signals. The proposed method employs a bank of Gabor filters for processing the EEG signals. The processed EEG signal is divided into smaller segments and histograffis of 1D-LBPs of these segments are computed. Nearest neighbor classifier utilizes the histogram matching scores to determine whether the acquired EEG signal belongs to seizure or seizure-free category. Experimental results on publicly available database suggest that the proposed features effectively characterize local variations and are useful for classification of seizure and seizure-free EEG signals with a classification accuracy of 98.33%. This result demonstrates the superiority of our approach for classification of seizure and seizure-free EEG signals over recently proposed approaches in the literature.
机译：本地二进制模式（LBP）是一种纹理描述符，已被证明对于图像处理中的各种图像分析任务非常有效。在本文中，基于一维局部二进制模式（1D-LBP）的特征用于癫痫发作和无癫痫性脑电图（EEG）信号的分类。所提出的方法采用了一组Gabor滤波器来处理EEG信号。将处理后的脑电信号分成较小的片段，并计算这些片段的1D-LBP的组织移植物。最近邻分类器利用直方图匹配分数来确定获取的EEG信号属于癫痫发作类别还是无癫痫发作类别。公开数据库上的实验结果表明，所提出的特征有效地表征了局部变异，并且对于癫痫发作和无癫痫性脑电信号的分类非常有用，分类精度为98.33％。该结果证明了我们的癫痫发作和无癫痫性脑电信号分类方法优于文献中最近提出的方法。

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