Abstract
Fetal ECG contains possibly exact information that may support clinicians in making apt decisions during pregnancy and labor. Hence, extraction of clean fetal ECG is extremely crucial for fetal surveillance. This is done by placing electrodes on mother’s abdomen. However, it is tainted with various sources of noise. This paper compares LMS adaptive filter for FECG extraction with neural network-based adaptive filter. Twenty cases of clinical data were used. Experimental results showed superiority of later scheme in terms of SNR and MSE.
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Kaleem, A.M., Kokate, R.D. (2019). Performance Evaluation of Fetal ECG Extraction Algorithms. In: Sridhar, V., Padma, M., Rao, K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-13-5802-9_17
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DOI: https://doi.org/10.1007/978-981-13-5802-9_17
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