Fetal electrocardiogram (FECG) non-invasively obtained through abdominal recordings serves as a promising diagnostic tool for fetal health monitoring during pregnancy. However, in the abdominal ECG (AECG) signal, FECG overlaps with maternal ECG (MECG) in both temporal and spectral domains in addition to interference from various sources like electromyogram, electrogastrogram, motion artifacts and other noises. The objective of this paper is to eliminate MECG components from AECG signal to extract FECG signal through FIR adaptive noise canceller (ANC) with filter coefficients updated using adaptive algorithms. Adaptive filters are suitable for current problem of interest and Least Mean Square (LMS) and its variants are analyzed for the problem of FECG extraction. We have compared the four variants of LMS such as normalized LMS (NLMS), sign-error algorithm, least mean fourth (LMF) algorithms for FECG extraction. The algorithms are evaluated using real-time abdominal ECG recordings acquired from daisy database. The performance of each algorithm is evaluated using various parameters like sensitivity, accuracy, positive predictive values and [Formula: see text] score. Further, the convergence rate for different algorithms are plotted and analyzed. From the simulation results, it is observed that the LMF algorithm outperforms its counterparts by providing an accuracy and positive predictive value of 73.3%, sensitivity of 100% and [Formula: see text] measure of 84.5%. The convergence plots obtained justify that LMF algorithm has a faster convergence rate compared to the other variants of LMS.
Distributed adaptive LMF algorithm for sparse parameter estimation in Gaussian mixture noise