Fetal Electrocardiogram (FECG) signal contains potentially precise
information that could assist clinicians in making more appropriate
and timely decisions during labor. A Back-propagation Neural
Network and Adaptive Linear Neural Network have been designed to
extract the FECG from the abdominal ECG to assess the fetus during
the pregnancy and labor. The neural network was trained to
recognize the normal waveform and filtered out the unnecessary
artifacts including noises in the ECG signal, including power line
interference, motion artifacts, baseline drift, ECG amplitude
modulation with respiration and other composite noises. The
performance of the designed algorithm for FHR extraction is 93.75%.
The algorithm has been modeled using VHDL for hardware modeling of
FHR monitoring system, which has been synthesized and fitted into
Altera's Stratix II EP2S15F484C3 using the Quartus II version 7.2
Web Edition where the logic and DSP block utilization were 89% and
50% respectively. This research will open up a passage to
biomedical researchers and physicians to advocate an excellent
understanding of FECG signal and its analysis procedures for FHR
monitoring system.
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