基于FCEEMD的心跳信号和呼吸信号分离研究

为了快速、实时、准确地分离由非接触人体生理信号监测系统所采集到的信号,将快速互补集合经验模态分解(fast complementary ensemble empirical mode decomposition,FCEEMD)引入到人体生理信号处理领域,在原始信号中成对添加符号相反的白噪声信号,并对其进行经验模态分解(EMD),获得有限个固有模态函数(IMFs)进而实现原始信号的分离,使用固定筛分次数停止准则以保证该算法的快速实时性;仿真算例和实际实验都表明,该算法可有效解决模态混叠,快速获得准确的心跳信号和呼吸信号。

Research on separation of heartbeat signal and respiration signal based on FCEEMD

The signal collected by a non-contact physiological signal monitoring system should be separated fast and accurately in realtime.In this paper,the fast complementary ensemble empirical mode decomposition (FCEEMD) was used for the physiological signal processing,in which white noise was added in pairs into the original signal and the empirical mode decomposition (EMD) was used to obtain a series of intrinsic mode functions(IMFs).The separation of the original signal was realized,a fixed iterations time of sifting to the stoppage criterion is used to ensure that the algorithm is fast in real-time.By analyzing the simulation and actual signals,the results show that the algorithm can solve the mode-mixing problem effectively and obtain heartbeat and respiration signals accurately and quickly.