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感应式体积描记术中的心源性振荡提取:总体经验模态分解

Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition.

作者信息

Abdulhay Enas, Guméry Pierre-Yves, Fontecave Julie, Baconnier Pierre

机构信息

PRETA team, TIMC-IMAG, Joseph Fourier University, La Tronche, France.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2240-3. doi: 10.1109/IEMBS.2009.5335004.

Abstract

The purpose of this study is to investigate the potential of the ensemble empirical mode decomposition (EEMD) to extract cardiogenic oscillations from inductive plethysmography signals in order to measure cardiac stroke volume. First, a simple cardio-respiratory model is used to simulate cardiac, respiratory, and cardio-respiratory signals. Second, application of empirical mode decomposition (EMD) to simulated cardio-respiratory signals demonstrates that the mode mixing phenomenon affects the extraction performance and hence also the cardiac stroke volume measurement. Stroke volume is measured as the amplitude of extracted cardiogenic oscillations, and it is compared to the stroke volume of simulated cardiac activity. Finally, we show that the EEMD leads to mode mixing removal.

摘要

本研究的目的是探究总体经验模态分解(EEMD)从感应式体积描记信号中提取心源性振荡以测量心搏量的潜力。首先,使用一个简单的心肺模型来模拟心脏、呼吸和心肺信号。其次,将经验模态分解(EMD)应用于模拟的心肺信号表明,模态混叠现象会影响提取性能,进而也会影响心搏量测量。心搏量通过提取的心源性振荡的幅度来测量,并将其与模拟心脏活动的心搏量进行比较。最后,我们表明EEMD可消除模态混叠。

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