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Application of genetic algorithms in fuzzy wavelet neural network for fetal electrocardiogram extraction

In this paper, we propose a novel fuzzy wavelet neural networks method to extract fetal electrocardiogaram from cutaneous potential abdominal and noise contaminations electrocardiogram recordings. As the fuzzy wavelet neural networks is adaptive to the non-linear and time-varying features of electrocardiogram signal therefore, the fuzzy wavelet neural networks has been used to extract the fetal electrocardiogaram signal. Using this fuzzy wavelet neural networks approach, the maternal electrocardiogram has been suppressed from the abdominal electrocardiogram by correlation detraction, so that the output can be considered as only fetal electrocardiogaram. The structure of fuzzy wavelet neural networks is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the extraction fetal electrocardiogaram accuracy and general capability of the fuzzy wavelet neural networks system, an efficient genetic algorithm approach is used to adjust the parameters of dilation, translation, weights, and membership functions.