Deep learning-based ballistocardiography reconstruction algorithm on the optical fiber sensor
Deep learning-based ballistocardiography reconstruction algorithm on the optical fiber sensor
复制标题
基于深度学习的光纤传感心冲击图重建算法
DOI：
10.1364/oe.452408
复制
发表时间：
2022-04-11
期刊：
影响因子：
3.8
通讯作者：
Yu, Changyuan
中科院分区：
文献类型：
--
作者：
Chen, Shuyang;Tan, Fengze;Yu, Changyuan
Ballistocardiography (BCG) is a vibration signal related to cardiac activity, which can be obtained in a non-invasive way by optical fiber sensors. In this paper, we propose a modified generative adversarial network (GAN) to reconstruct BCG signals by solving signal fading problems in a Mach-Zehnder interferometer (MZI). Based on this algorithm, additional modulators and demodulators are not needed in the MZI, which reduces the cost and hardware complexity. The correlation between reconstructed BCG and reference BCG is 0.952 in test data. To further test the model performance, we collect special BCG signals including sinus arrhythmia data and post-exercise cardiac activities data, and analyze the reconstructed results. In conclusion, a BCG reconstruction algorithm is presented to solve the signal fading problem in the optical fiber interferometer innovatively, which greatly simplifies the BCG monitoring system. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
