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Ballistocardiography reconstruction based on optical fiber sensor using deep learning algorithm

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Abstract

Ballistocardiography (BCG) is the record of body recoils resulted from heart ejection during each cardiac cycle. To detect the detail information in the BCG signal, high sensitivity optical fiber Mach-Zehnder interferometer (MZI) is adopted to fabricate the cushion-type monitor. However, the bias point of the interferometer drifts with the environment affection, which will result in signal fading. In this paper, generative adversarial network (GAN) is proposed to solve the signal distortion problem in the BCG monitoring. The results show that GAN can reconstruct BCG signals with a good performance.

© 2021 The Author(s)

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