A Comprehensive Analysis of Fetal Electrocardiogram Signals

This paper reviews different feature extraction and analysis methods for FECG signals. The FECG signals are very critical in monitoring the health of the fetuses; hence, most of the time, they are masked by maternal ECG and noise and introducing significant challenges during the analysis of the same signal. In this paper, several feature-extraction techniques are reviewed and compared. Techniques included, but were not limited to, time-domain, frequency-domain, and time-frequency domain methods such as PCA and ICA. We also review some newer approaches for machine learning, including deep learning methods that could be applied further to enhance the performance of signal separation and classification performance. Then, each method is tested on synthetic and realworld datasets, emphasizing computational efficiency and clinical applicability. By elaborating on the strengths and weaknesses of each approach, this paper summarize the results that give insight into optimum strategies to improve FECG signal processing for better diagnosis regarding fetal health.