Computation of Fetal Heart Rate Variability from Abdominal ECG Using Adaptive Filtering and Independent Component Analysis

Investigating fetal electrocardiogram (fECG) is of critical importance for pregnant women to study about fetal health and its well-being. Generally, its extraction is preferred from the abdominal ECG (aECG) recordings, which consists of fECG, maternal ECG (mECG), and noises (such as, power line disturbances, motion artifact, uterine contraction, baseline wander, and high frequency noises, etc.). Accompanying noise in ECG causes loss of critical information and leads to misdiagnosis. The work presented in this paper extracts clean fECG from aECG, using the capability of independent component analysis (ICA) and adaptive filtering (AF). ICA is a blind source separation (BSS) technique, which is used for estimating multivariate data as a linear combination of statistically independent non-Gaussian signals (i.e., source signals). It is also a non-parametric technique and is independent of pattern averaging, making it an efficient algorithm for identification of atypical heartbeats in ECG signal. FastICA (FICA) is a fixed-point iterative algorithm that estimates the independent components (ICs) with maximum non-Gaussianity by minimizing the similarity between them. These ICs are subjected to adaptive filtering along with direct fECG as the reference signal for extracting clean fECG. This filtering helps in estimating the lost fECG signal during acquisition by canceling the background noises. In this work, an optimally converging least mean square (LMS) algorithm is used with proper selection of step size. The extracted fECG obtained from the filtering process is subjected to post-processing, by Savitzky-Golay filter, followed by a \(3^{rd}\) order band-pass filter, a derivative filter, and a P-point moving average filter for clear identification of R-peaks. From R-peak locations, heart rate variability (HRV) has been computed using the Pan-Tompkins algorithm to predict fetal heart abnormalities. This method is validated on the publicly available PhysioNet (ADFECG) database and obtained an F1-score of 92.68%. The estimated heart rate for the extracted fECG is found to be 78 bpm.