Fetal ECG Signal Extraction From Long-Term Abdominal Recordings Based on Adaptive QRS Removal and Joint Blind Source Separation

Despite wearable electronic devices having enormous potential applications, the recording quality has not sufficiently met the requirements for fetal disease detection. In this article, we aim to propose a novel framework for fetal electrocardiography (ECG) extraction, where the hybrid approach mainly considers the combination of a novel adaptive maternal QRS removal (AQRSR) algorithm and a tensor-based joint blind source separation (JBSS) approach. To adapt to the dynamic change of the ECG signal and suppress the powerful maternal heartbeat, AQRSR is explored with an adaptive template, which is generated by a model consisting of a generator, discriminator, and transformer. By replacing the oldest segmentation with the newest cycle, the approach is capable of quickly adapting to the estimated output to match the new input. In addition, due to the significant crossover between the target signal and the noise, even if the maternal ECG removal processing is implemented, the residual signal inevitably contains fetal ECG, part of maternal ECG, and noise. As a consequence of this, a novel JBSS that incorporates tensor decomposition is formulated to separate fetal heartbeat from long-term recordings contaminated with maternal movements noise and heartbeat interference. By dividing the collected data into multiple segments, this method can extract fetal ECG signals by exploiting the signals that are not only statistically independent within each segment but can be dependent on different segments. Experimental results show that even if the recorded signals are contaminated by the noise, the proposed framework has comparable better performance on fetal ECG extraction.