A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters
Yu Xuan, Xiangyu Zhang, Shuyue Stella Li, Zihan Shen, Xin Xie, Leibny Paola García, Roberto Togneri
Abstract
The detection of abnormal fetal heartbeats during pregnancy is important for monitoring the health conditions of the fetus. While adult ECG has made several advances in modern medicine, noninvasive fetal electrocardiography (FECG) remains a great challenge. In this paper, we introduce a new method based on affine combinations of adaptive filters to extract FECG signals. The affine combination of multiple filters is able to precisely fit the reference signal, and thus obtain more accurate FECGs. We proposed a method to combine the Least Mean Square (LMS) and Recursive Least Squares (RLS) filters. Our approach found that the Combined Recursive Least Squares (CRLS) filter achieves the best performance among all proposed combinations. In addition, we found that CRLS is more advantageous in extracting FECG from abdominal electrocardiograms (AECG) with a small signal-to-noise ratio (SNR). Compared with the state-of-the-art Multiple Sub-Filter Adaptive Noise Canceller (MSF-ANC) method, CRLS shows improved performance. The sensitivity, accuracy and F1 score are improved by 3.58%, 2.39% and 1.36%, respectively.
BibTeX
@inproceedings{icassp2023_anewapproachtoex,
title = {A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters},
author = {Yu Xuan and Xiangyu Zhang and Shuyue Stella Li and Zihan Shen and Xin Xie and Leibny Paola García and Roberto Togneri},
booktitle = {ICASSP 2023},
year = {2023}
}