...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Automatic Differentiation of Normal and Continuous Adventitious Respiratory Sounds Using Ensemble Empirical Mode Decomposition and Instantaneous Frequency
【24h】

Automatic Differentiation of Normal and Continuous Adventitious Respiratory Sounds Using Ensemble Empirical Mode Decomposition and Instantaneous Frequency

机译：使用集合经验模态分解和瞬时频率自动区分正常和连续不定呼吸声

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

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multiscale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.
机译：在呼吸系统疾病的诊断中，区分正常呼吸音和不定呼吸音是一个重大挑战。特别地，连续不定声音（CAS）具有临床意义，因为它们反映了某些疾病的严重性。这项研究提出了一种新的分类器，可以自动将普通声音与CAS区别开。它基于对整体经验模式分解（EEMD）进行计算后的瞬时频率（IF）和包络（IE）的多尺度分析。这些技术比以前的技术有两个主要优点：通过计算IF-IE可获得高的时间分辨率，并且EEMD不需要了解信号特性。分类器基于以下事实：当CAS出现在呼吸周期中时，RS信号的IF色散显着降低。因此，通过使用移动窗口来计算IF序列的离散度来检测CAS。研究数据集包含从30个哮喘患者记录的870个吸气周期中提取的1494个RS段。先前，所有经验丰富的医师将所有周期及其RS段分类为包含正常声音或CAS，以获得黄金标准分类。支持向量机分类器使用迭代过程进行训练和测试，在该过程中，将数据集在循环内随机分为训练（65％）和测试（35％）集。 SVM分类器还对4592个模拟的CAS周期进行了测试。记录的（94.6％±0.3％）和模拟的（92.8％±3.6％）信号均具有很高的总精度。我们得出的结论是，所提出的方法对于RS分析和分类很有希望。

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