• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2019 7th International Symposium on Digital Forensics and Security >Feature Extraction of ECG Signal by using Deep Feature
【24h】

Feature Extraction of ECG Signal by using Deep Feature

机译：利用深度特征提取心电信号特征

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

The analysis and classification of Electrocardiogram (ECG) signals have become very important tool to diagnose of heart disorders. Computer-aided techniques are generally used to classify biomedical application areas. In this paper, we aim to feature extraction and classification of ECG signals. Accordingly, an open access ECG database in Physionet was employed in order to separate normal and abnormal of ECG records. Deep feature approach which is based on Convolutional Neural Network (CNN) was applied to taking out important features of heart recordings. Afterward, Extreme Learning Machine (ELM) was applied to the ECG records. The average precision value metric was used to the performance of the classification performed. In this content, it was noticed classification success values were achieved to accuracy % 88.33, sensitivity %89.47 and specificity % 87.80 with ELM.
机译：心电图（ECG）信号的分析和分类已成为诊断心脏疾病的重要工具。通常使用计算机辅助技术对生物医学应用领域进行分类。在本文中，我们旨在对心电信号进行特征提取和分类。因此，在Physionet中使用了开放式ECG数据库，以区分正常和异常的ECG记录。基于卷积神经网络（CNN）的深度特征方法被用于提取心脏记录的重要特征。之后，将极限学习机（ELM）应用于ECG记录。平均精度值度量用于执行分类。在该含量中，注意到使用ELM的分类成功率达到了准确度％88.33，灵敏度％89.47和特异性％87.80。

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