SEPARATION OF TWIN FETAL ECG FROM MATERNAL ECG USING EMPIRICAL MODE DECOMPOSITION TECHNIQUES

2017 ◽  
Vol 29 (06) ◽  
pp. 1750042 ◽  
Author(s):  
Marjan Salmanvandi ◽  
Zahra Einalou
Keyword(s):  
Empirical Mode Decomposition ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Sampling Frequency ◽  
Decomposition Techniques ◽  
Fetal Ecg ◽  
Mode Decomposition ◽  
Adaptive Noise

In this study, by using a combination of standard Empirical Mode Decomposition (EMD), Ensembling Empirical Mode Decomposition (EEMD), Completing Empirical Mode Decomposition (CEMD) and Principal Component Analysis (PCA), a new method was introduced to separate twin fetal heart rate (FHR) from maternal ECG. The data which were the results of modeling fetal and maternal ECG which be longed to 10 mothers with a sampling frequency of 250[Formula: see text]Hz. In this method, first R-wave of maternal ECG was determined, and then maternal QRS is removed. Further, to clarify these changes and increase resistance to environmental noises, PCA was used. In the next step, all FHRs related to twin fetuses were extracted from signals. Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was used for denoising. By using the proposed method for noise with an amplitude of over 10 dB, the FHR of the first and second (if any) fetuses were separated from maternal ECG with an accuracy of 93.3% and 91.1% respectively. The goal was to improve signal processing dimensions of fetal ECG and provides deeper insight about this issue using EEMD technique. It was tested on a twin fetus with the results suggesting its effectiveness even with increased number of fetuses with slight modifications.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

2020 ◽  
Vol 42 (2) ◽  
pp. 57-73
Author(s):  
Suya Han ◽  
Yufeng Zhang ◽  
Keyan Wu ◽  
Bingbing He ◽  
Kexin Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Second Harmonic ◽  
Ensemble Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Motion Artifacts ◽  
Intrinsic Mode Functions ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Harmonic Components

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

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