Samanta B
Department of Mechanical Engineering, Villanova University, Villanova, PA 10985, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:324-7. doi: 10.1109/IEMBS.2009.5333783.
The paper proposes a novel method of extracting features from physiological signals using intrinsic mode decomposition (IMD) and morphological signal processing (MSP). The complex, nonlinear and non-stationary biomedical signals are first decomposed into intrinsic mode functions (IMF). Next each IMF is subjected to MSP for extracting features, namely, pattern spectrum entropy, that characterize the shape-size complexity of the component signals. These along with other features like energy and sample entropy are extracted from the individual IMF as well as the cumulative sums of IMF for characterizing the signals. The procedure is illustrated using heart sound signals digitally recorded during cardiac auscultation representing different cardiac conditions.
本文提出了一种利用固有模式分解(IMD)和形态学信号处理(MSP)从生理信号中提取特征的新方法。首先将复杂、非线性和非平稳的生物医学信号分解为固有模式函数(IMF)。接下来,对每个IMF进行MSP以提取特征,即模式谱熵,它表征了分量信号的形状 - 大小复杂性。这些特征以及能量和样本熵等其他特征从各个IMF以及IMF的累积和中提取出来,用于表征信号。使用在心脏听诊期间数字记录的代表不同心脏状况的心音信号来说明该过程。