• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Multimedia Information Retrieval >An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification
【24h】

An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification

机译：自动特征提取和融合模型：电灰度（EMG）信号分类

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

In this paper, we present a real-time feature extraction and fusion model for an automated staging of electromyogram signals-generalizing canonical correlation analysis (CCA). The proposed method is capable of capturing multiple view information (i.e., feature matrices) generated from signals. Our algorithm employs an optimization technique to derive sets of statistical features among the paired views based on which possible variations of signals have been demonstrated. Next, discrete wavelet transformation is performed on multiple views to create domain independent views which are then subjected to CCA optimization. The estimated two sets of statistically independent features from two independent analysis are concentrated through two recently proposed fusion models, and then, we evaluate global feature matrices. Further it is validated statistically for $$p<0.05$$ p < 0.05 . The proposed algorithm is then analyzed and compared with state-of-the-art methods. Results indicate that the proposed approach outperforms many other methods in terms of accuracy, specificity and sensitivity, which are 98.80, 99.0 and 98.0%, respectively. Thus, the proposed algorithm is suitable for large-scale applications and expedite diagnosis research.
机译：在本文中，我们提出了一种实时特征提取和融合模型，用于励磁标记的自动分期 - 概括规范相关分析（CCA）。所提出的方法能够捕获从信号生成的多视图信息（即，特征矩阵）。我们的算法采用优化技术来基于已经证明了信号的可能变化，从而在配对视图中导出统计特征集。接下来，对多个视图执行离散小波变换以创建域独立视图，然后进行CCA优化。估计来自两个独立分析的两组统计独立特征通过两个最近提出的融合模型集中，然后，我们评估全局特征矩阵。此外，它在统计上验证，以满足$$ p <0.05 $$ p <0.05。然后分析所提出的算法和与最先进的方法进行比较。结果表明，在准确性，特异性和敏感度方面，该方法分别优于许多其他方法，分别为98.80,99.0和98.0％。因此，所提出的算法适用于大规模应用和加快诊断研究。

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