Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease
Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease
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DOI：
10.1109/iembs.2004.1403234
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发表时间：
2004-12
期刊：
影响因子：
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通讯作者：
Hualou Liang;Qiuhua Lin;J.D.Z. Chen
中科院分区：
文献类型：
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作者：
Hualou Liang;Qiuhua Lin;J.D.Z. Chen
经验模态分解（EMD）是一种分析非线性、非平稳时间序列的通用信号处理方法。EMD的核心思想是将时间序列分解为有限的且通常是少量的固有模式函数（IMF）。IMF被定义为具有极值的数量和零交叉的数量相等（或最多相差一个）并且还具有分别由局部最小值和最大值定义的对称包络的任何函数。分解过程是自适应的，数据驱动的，因此，高效的经验模态分解，并通过仿真验证其性能。然后将EMD应用于分析胃食管反流病的食管测压时间序列。结果表明，经验模态分解可能被证明是一个重要的技术分析食管测压数据。
The empirical mode decomposition (EMD) is a general signal processing method for analyzing nonlinear and non-stationary time series. The central idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). An IMF is defined as any function having the number of extrema and the number of zero-crossings equal (or differing at most by one), and also having symmetric envelopes defined by the local minima, and maxima respectively. The decomposition procedure is adaptive, data-driven, therefore, highly efficient The EMD is first described, and its performance is validated by simulations. The EMD is then applied to the analysis of esophageal manometric time series in gastroesophageal reflux disease. The results show that the EMD may prove to be a vital technique for the analysis of esophageal manometric data.