Abstract
This paper focuses on the fetal electrocardiogram (fECG) recorded transabdominally. This method could become very efficient and essential tool in monitoring and diagnosing endangered fetuses during the pregnancy and the delivery. The greatest challenge connected with this kind of monitoring is the amount of noise that is recorded within the desired signal. Thus, the extraction of the fECG from the composite abdominal signal is discussed. The authors’ aim is to introduce the most suitable representatives from the Least Mean Squares (LMS) and Recursive Least Square (RLS) based Finite Impulse Response (FIR) Adaptive Filters. Experimental results suggest the ideal combination of the chosen filters’ settings (Step size, filter length, forgetting factor etc.). Results of fECG extraction are evaluated by the objective parameters, namely Percentage Root-Mean-Square Difference (PRD), input and output Signal to Noise Ratios (SNRs), and Root Mean Square Error (RMSE).
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This paper has been elaborated within the framework of the Project SP2016/146 of the Student Grant System, VSB-TU Ostrava, Czech Republic.
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Kahankova, R., Martinek, R., Bilik, P. (2018). Non-invasive Fetal ECG Extraction from Maternal Abdominal ECG Using LMS and RLS Adaptive Algorithms. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_27
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