Abstract
Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a method for fetal ECG extraction and characterization based on wavelet analysis and the least mean square (LMS) adaptive filtering algorithm. First, abdominal signals and thoracic signals were processed by the LMS algorithm. The abdominal signal was taken as the original input of the LMS adaptive filtering system, and the thoracic signal as the reference input. Finally, the processed wavelet coefficients were. The results indicated that the proposed algorithm can be used for extracting automatically fetal ECG from abdominal signals.
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The authors would like to thank the anonymous reviewers for their insightful comments and recommendations.
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Ziani, S., El Hassouani, Y. (2020). Fetal Electrocardiogram Analysis Based on LMS Adaptive Filtering and Complex Continuous Wavelet 1-D. In: Farhaoui, Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_26
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DOI: https://doi.org/10.1007/978-3-030-23672-4_26
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