静息态下基于变分模态分解的生命体征检测

Vital Signs Detection Based on Variational Modal Decomposition in Resting State

传统的无线生命体征监测方法在心跳和呼吸信号的分离方面容易存在谐波残留现象,针对这一情况,提出了一种基于变分模态分解(VMD)的生命信号检测方法。该方法使用毫米波段调频连续波(FMCW)雷达进行生命体征信号获取,根据心跳及呼吸的频率特征,使用VMD算法将主要信号分解为不同模态,保证了各模态之间信号频率范围互不重叠,分离出较为完整且无谐波残留的呼吸及心跳信号。实验结果表明,所提算法能够有效提取出目标的呼吸及心跳信号,且相比传统的模态分解算法具有更高的鲁棒性和稳定性,具有良好的信噪比(SNR),提高了测量精度和距离。

Traditional wireless vital signs detection methods are prone to residual harmonics in the separation of heartbeat and breathing signals.In response to this situation,a vital signal detection method based on variational modal decomposition(VMD)is proposed.This method uses millimeter-band frequency modulated continuous wave(FMCW)radar to obtain vital signs signals.According to the frequency characteristics of heartbeat and respiration,the main signal is decomposed into different modes including more complete respiration and heartbeat signals without harmonic residuals by using the VMD algorithm to ensure the frequency range between each mode does not overlap with each other.Experimental results show that the proposed algorithm can effectively extract the breathing and heartbeat signals of target.Compared with the traditional modal decomposition algorithm,it has higher robustness and stability,has a good signal-to-noise ratio(SNR),and improves measurement accuracy and distance.