Abstract
A Fetal Electrocardiogram (FECG) signal will contain some potential information which is precise for assisting the clinicians to make appropriate decisions that are timely at the time of pregnancy and labor. Extraction and detection of such FECG signals from their composite maternal signals of the abdomen using very powerful and advanced methodologies is a very critical need in case of fetal monitoring. The mining of pure FECG signals from the abdominal of the prenatal woman is focused in this paper. This FECG signal is very vulnerable to noise and is very difficult to process it accurately with no significant distortion that can imped its use. So, in order to be able to get some proper information on the status of the fetus or its condition it is important to be able to improve the abdominal signal and its SNR. Since the wavelet transform is well in providing information in time and frequency, the combination of Stationary Wavelet Transform (SWT), Weighted Least Square Regression (WLSR) and parallel Extended Kalman Filter (Par-EKF) are used for FECG extraction. The analysis for different values of Signal to Noise Ratio (SNR) between fetal and maternal ECG’s is implemented using MATLAB. As a Result, the highest SNR obtained is 33.5 which showed that the proposed method is efficient in extracting more information about FECG from Maternal ECG (MECG).
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