Liang Hualou, Lin Qiu-Hua, Chen J D Z
Sch. of Health Inf. Sci., Texas Univ., Houston, TX 77030, USA.
Conf Proc IEEE Eng Med Biol Soc. 2004;2006:620-3. doi: 10.1109/IEMBS.2004.1403234.
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.
经验模态分解(EMD)是一种用于分析非线性和非平稳时间序列的通用信号处理方法。EMD的核心思想是将一个时间序列分解为有限且通常数量较少的固有模态函数(IMF)。IMF被定义为具有相等数量的极值点和零交叉点(或最多相差一个),并且分别由局部最小值和最大值定义对称包络的任何函数。分解过程是自适应的、数据驱动的,因此效率很高。首先描述了EMD,并通过仿真验证了其性能。然后将EMD应用于胃食管反流病中食管测压时间序列的分析。结果表明,EMD可能被证明是分析食管测压数据的一项重要技术。