A novel approach to the extraction of fetal electrocardiogram based on empirical mode decomposition and correlation analysis.

Authors:
Saeed Mohaqeqi, PhD
Saeed Mohaqeqi, PhD
Shahed University
Deep Learning, Medical Image Processing, Python Programming
Karaj, Alborz | Iran (Islamic Republic of)

Australas Phys Eng Sci Med 2017 Sep 29;40(3):565-574. Epub 2017 May 29.

Department of Midwifery, Nursing and Midwifery Faculty, Zanjan University of Medical Sciences, Zanjan, Iran.

Fetal heart rate monitoring is the process of checking the condition of the fetus during pregnancy and it would allow doctors and nurses to detect early signs of trouble during labor and delivery. The fetal ECG (FECG) signal is so weak and also is corrupted by other signals and noises, mainly by maternal ECG signal. It is so hard to acquire a noise-free, precise and reliable FECG using the conventional methods. In this study, a combination of empirical mode decomposition (EMD) algorithms, correlation and match filtering is used for extracting FECG from maternal abdominal ECG signals. The proposed method benefits from match filtering ability to detect fetal signal and QRS complex to detect weak QRS peaks. The combined method, has been applied successfully on different signal qualities, even for signals that their analysis was hard and complicated for other methods. This method is able to detect R-R intervals with high accuracy. It was proved that the complete ensemble empirical mode decomposition method provides a better frequency resolution of modes and also requires less iterations that leads to a considerably less computational cost than EMD and ensemble empirical mode decomposition and can reconstruct the FECG completely from the calculated modes. We believe that this method opens a new field in non-invasive maternal abdominal signal processing so the FECG signal could be extracted with high speed and accuracy.

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Source
http://dx.doi.org/10.1007/s13246-017-0560-4DOI Listing
September 2017
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