• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals
【24h】

J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals

机译：利用分析时频柔性小波变换对ECG信号进行J波自动检测

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

As a new important index of the electrocardiogram (ECG) of ventricular bipolar play, J wave plays an increasingly significant role in the clinical diagnosis. The existence of J wave hints at potential crisis of fatal disease and even death. Nowadays, however, it can hardly meet the clinical needs where the diagnosis of J wave variation only depends on experience of clinicians. Therefore, a new technique which is capable of detecting J wave using analytic time-frequency flexible wavelet transformation (ATFFWT) is proposed in this paper. We have used ATFFWT to decompose the processed ECG signals into the desired subbands. Further, Fuzzy Entropy (FE) is computed from each subband to capture more hidden and meaningful information. Feature scoring method is applied to select optimal feature set. Finally, the extracted features are fed to Least Squares-Support Vector Machine (LS-SVM) classifier. The 10-fold cross validation is used to obtain reliable and stable performance and to avoid the overfitting of the model. Our proposed algorithm has achieved accuracy of 97.61% for Morlet Wavelet (MW) kernel in comparison to 97.56% for Radial Basis Function (RBF) kernel. The developed effective algorithm can be used to design an expert system to aid clinicians in their regular diagnosis.
机译：J波作为心室双极性心电图的新的重要指标，在临床诊断中起着越来越重要的作用。 J波的存在暗示着致命疾病甚至死亡的潜在危机。然而，如今，仅凭临床医生的经验来诊断J波变异就已无法满足临床需求。因此，提出了一种能够利用时频弹性小波分析（ATFFWT）检测J波的新技术。我们已经使用ATFFWT将经过处理的ECG信号分解为所需的子带。此外，从每个子带计算模糊熵（FE）以捕获更多隐藏的有意义的信息。应用特征评分方法选择最佳特征集。最后，将提取的特征馈送到最小二乘支持向量机（LS-SVM）分类器。 10倍交叉验证用于获得可靠和稳定的性能，并避免模型的过拟合。相对于径向基函数（RBF）内核的97.56％，我们提出的算法对Morlet小波（MW）内核的准确性达到97.61％。所开发的有效算法可用于设计专家系统，以帮助临床医生进行常规诊断。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|6791405.1-6791405.11|共11页
  • 作者

    Li Deng-ao; Zhou Jie; Zhao Jumin; Liu Xinyan;

  • 作者单位

    Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan, Shanxi, Peoples R China;

    Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan, Shanxi, Peoples R China;

    Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan, Shanxi, Peoples R China;

    Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan, Shanxi, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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