Prototyping of Fetal QRS Complex Detection Algorithm
Hardware Prototyping of an Efficient Fetal QRS Complex Detection Algorithm for Fetal Heart Rate Monitoring
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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 respi...
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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