AI Summary & Key Takeaways
AI InsightThe results show that utilizing only the ICA technique to extract fECG signals is insufficient and that additional algorithms, such as those indicated above, should be used together.
📌 3 Key Takeaways
Abstract
Fetal electrocardiogram (fECG) is a signal that contains vital information about the health of the fetus throughout pregnancy. During pregnancy, it is important to monitor and analyse this signal because it represents the electrical activity of the developing fetal heart. Early detection of fetal ECG problems during the fetus' development is crucial because it allows early treatment and provides knowledge about diseases that may emerge at a later time. Extraction of fetal ECG from the abdomen ECG signal is valuable in these aspects. In order to extract the fetal ECG from the recorded abdomen ECG signals correctly, it must be handled appropriately. It could be challenging to separate the fetal ECG signal from other physiological artifacts and noises in the mother abdominal signal. In this study, signal processing techniques were used to separate the fetus ECG signal from real abdominal ECG recordings. These methods include Ensemble Empirical Based Denoising, Finite Impulse Response Filter, Independent Component Analysis, and Pan & Tompkins approach. The results show that utilizing only the ICA technique to extract fECG signals is insufficient and that additional algorithms, such as those indicated above, should be used together. The mECG and fECG signals can be successfully extracted using the suggested approach.