TY - GEN
T1 - Ballistocardiography reconstruction based on optical fiber sensor using deep learning algorithm
AU - Chen, Shuyang
AU - Tan, Fengze
AU - Lyu, Weimin
AU - Yu, Changyuan
N1 - Funding Information:
National Natural Science Foundation of China (61971372); Research Grants Council, University Grants Committee (Grant 15200718).
Publisher Copyright:
© OSA 2021, © 2021 The Author(s)
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85119618376&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85119618376
T3 - Optics InfoBase Conference Papers
BT - Optoelectronics and Communications Conference, OECC 2021
PB - OSA - The Optical Society
T2 - 26th Optoelectronics and Communications Conference, OECC 2021
Y2 - 3 July 2021 through 7 July 2021
ER -