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Fetal heart rate monitoring from maternal body surface potentials using independent component analysis
Author(s) -
CHEN Wenxi,
ZHU Xin,
NEMOTO Tetsu,
KOBAYASHI Toshio,
SAITO Toshiyuki
Publication year - 2004
Publication title -
animal science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 38
eISSN - 1740-0929
pISSN - 1344-3941
DOI - 10.1111/j.1740-0929.2004.00215.x
Subject(s) - independent component analysis , noise (video) , fetal heart rate , pattern recognition (psychology) , artificial intelligence , computer science , heart rate , medicine , blood pressure , image (mathematics)
The fetal heart rate is indispensable for monitoring the health of unborn cattle fetuses. To monitor the fetal heart rate, a method employing independent component analysis (ICA) to extract the fetal electrocardiogram (fECG) from potentials measured on the maternal body surface and composed of a mixture of the maternal ECG (mECG), fECG, baseline drift and noise is described. A mixing of the raw data was simplified using a linear time‐invariant model. To separate the fECG from the mECG, baseline drift, and noise, an ICA strategy was applied, using a hyperbolic tangent as the contrast function and treating mutual information with the minimization principle to find the optimum demixing matrix to derive the fECG from the measured signals. After the feasibility of this method was shown on simulated signals obtained by randomly mixing pure fECG, pure mECG, low frequency sinusoidal drift and noise, real signals from three cloned pregnant Holstein cows with 157, 177 and 224‐day gestation periods were used to verify the separation method. The results show that the fECG, mECG, low‐frequency sinusoidal drift and noise can be clearly segregated in simulations, and that the fECG, mECG, baseline drift and noise can be successfully derived from real signals. The ICA approach has great potential in effectively detecting the fECG from maternal body surface potentials.