Adjacent Channel Weight Dependable RLS Adaptive Filter with VMD based Artifact Removal Mechanism in Fetal ECG Separation.

This paper proposes an adjacent channel weight dependable recursive least square adaptive filter (ACWD-RLS) with variational mode decomposition (VMD) based artifact removal mechanism in separating the fetal ECG (FECG) components from the pregnant mother abdominal ECG (AECG). This approach requires the two abdominal, and a single thorax ECG to extract the FECG component present in the AECG. The algorithm uses three independent VMD decomposition algorithms in which one decomposes the thorax ECG while the other two decompose the abdominal ECGs of adjacent channels. The presence of baseline wander (BW) and powerline interference (PLI) is detected from the modes obtained from each VMD algorithm. The approach then removes the BW and PLI modes from the decomposed modes to eliminate the artifacts. The work also proposes an ACWD-RLS filter that contains two sections of the RLS filter namely the main section and secondary section, where the weight update in the main section depends on the weight estimated in the secondary section. The performance of the VMD-based artifact removal algorithm in suppressing the BW and PLI artifacts was evaluated utilizing the MIT-BIH arrhythmia and MIT-BIH noise stress dataset, while the Synthetic dataset of Physionet and real-world Daisy dataset was utilized in the validation of the proposed ACWD-RLS approach in fetal ECG extraction. The proposed VMD-based BW and PLI artifact removal mechanism result in a correlation coefficient and output signal-to-noise ratio (SNR) of 0.988 and 14.23dB respectively with a signal to BW noise ratio of 5dB. The evaluation metrics namely percent root mean square difference (PRD), fetal to maternal SNR (fmSNR), recall, fetal R-peak detection accuracy (PDA), F1-score, and root mean square error (RMSE) are utilized to evaluate the FECG separation process. The evaluation results show that the algorithm yields an PDA of 95.54% and 97.96% in real-world Daisy and Synthetic datasets respectively.