Abstract
Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal monitoring and fetal health assessment. However, NI-FECG is inevitably contaminated by a variety of unwanted noise. Therefore, NI-FECG denoising is a challenging task. There is a need for effective techniques that can preserve most components of the morphology of fetal electrocardiography (FECG) and meanwhile eliminating the noise. In this paper, we propose a deep convolutional encoding-decoding framework for NI-FECG denoising. The network contains multiple convolution and deconvolution layers. End-to-end mappings from corrupted signals to the clean ones are learned in the network. Experimental results on two different datasets show that our network achieves better performance than two other state-of-the-art methods namely band-pass Butterworth filter and wavelet soft-threshold denoising method. The exceeding performance shows a promising method for noise cancellation of NI-FECG signals.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61772574, 61375080 and U1811462 and in part by the Key Program of the National Social Science Fund of China with Grant No. 18ZDA308.
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Zhong, W., Guo, X., Wang, G. (2020). Non-invasive Fetal Electrocardiography Denoising Using Deep Convolutional Encoder-Decoder Networks. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_1
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DOI: https://doi.org/10.1007/978-981-32-9682-4_1
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