Deep learning-based ballistocardiography reconstruction algorithm on the optical fiber sensor
Shuyang Chen, Fengze Tan, Weimin Lyu, Huaijian Luo, Jianxun Yu, Jiaqi Qu, Changyuan Yu
Abstract
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.