【摘要】 为解决心跳信号易被呼吸谐波和其他噪声干扰而难以提取的问题，提出一种结合遗传算法（GA）和反向传播（BP）神经网络的聚类经验模态分解体征提取模型。首先，采用动目标检测法滤除超宽带（UWB）雷达所接收回波信号中的静止杂波；然后利用距离门选择方法提取出体表振动信号，对其进行聚类经验模态分解得到固有模态函数分量；最后通过GA-BP神经网络对固有模态函数分量转化后的特征向量进行权值训练，以贝叶斯正则化作为BP的训练函数重构心肺信号，并与原始聚类经验模态分解重构信号进行比较。仿真实验结果表明，在不同信噪比下，GA-BP神经网络提取的信号与实际结果吻合度更高，可有效提高呼吸与心跳信号的提取准确度。更多还原

【Abstract】 In order to solve the problem that heartbeat signal is easy to be disturbed by respiratory harmonics and other noise,a clustering ensemble empirical mode decomposition(EEMD)sign extraction model combining genetic algorithm(GA)and back propagation(BP)neural network is proposed. Firstly,moving targets detection(MTD)is used to filter the static clutter from the echo signal received by ultra wide band(UWB)radar;Secondly,the body surface vibration signal is extracted by the distance gate selection method,and the intrinsic mode functions component is obtained by EEMD decomposition of the body surface vibration signal;Finally,the weight of the feature vector transformed by intrinsic mode functions component is trained by GA-BP neural network,and Bayesian regularization is used as the training function of BP to reconstruct the cardiopulmonary signal,which is compared with the original EEMD reconstructed signal. The simulation results show that under different signal-to-noise ratios,signals extracted by GA-BP neural network has higher consistency with the actual results,and can effectively improve the extraction accuracy of respiratory and heartbeat signals.更多还原