AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model

Abstract

Objective. The accurate decomposition of a mother’s abdominal electrocardiogram (AECG) to extract the fetal ECG (FECG) is a primary step in evaluating the fetus’s health. However, the AECG is often affected by different noises and interferences, such as the maternal ECG (MECG), making it hard to evaluate the FECG signal. In this paper, we propose a deep-learning-based framework, namely ‘AECG-DecompNet’, to efficiently extract both MECG and FECG from a single-channel abdominal electrode recording. Approach. AECG-DecompNet is based on two series networks to decompose AECG, one for MECG estimation and the other to eliminate interference and noise. Both networks are based on an encoder-decoder architecture with internal and external skip connections to reconstruct the signals better. Main results. Experimental results show that the proposed framework performs much better than utilizing one network for direct FECG extraction. In addition, the comparison of the proposed framework with popular single-channel extraction techniques shows superior results in terms of QRS detection while indicating its ability to preserve morphological information. AECG-DecompNet achieves exceptional accuracy in the precision metric (97.4%), higher accuracy in recall and F 1 metrics (93.52% and 95.42% respectively), and outperforms other state-of-the-art approaches. Significance. The proposed method shows a notable performance in preserving the morphological information when the FECG within the AECG signal is weak.