Fetal ECG Extraction Using Input-Mode and Output-Mode Adaptive Filters With Blind Source Separation

Abstract

This article presents two new approaches of fetal electrocardiogram (ECG) signal (FECG) separation using the input-mode adaptive filter (IMAF) and the output-mode adaptive filter (OMAF). Both approaches use the recursive least-squares (RLS) and the least-mean-squares (LMS) algorithms and a single-reference-generation block. In the IMAF, the filter’s primary input is connected directly to the abdominal signal. The reference signal is generated by windowing the abdominal signal according to the locations of the QRS MECG pulses. In the OMAF, the filter’s primary input is connected to the output stage of a blind source separation block. The reference signal is generated by windowing the raw FECG signal, from the BSS output, according to the locations of the QRS pulses of the extracted MECG signal. We selected the null space idempotent transformation matrix (NSITM) as the BSS algorithm used in this work. Results from real Daisy and Physionet databases show the successful extraction of the FECG signal. Results from synthesized data from Physionet databases, using OMAF, show considerable improvement in extraction performances over NSITM and IMAF when the fetal-to-maternal signal-to-noise ratio (fmSNR) increases from −30 to 0 dB. This study demonstrated that the OMAF is a feasible algorithm for FECG extraction.