ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter.
ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter.
复制标题
使用心电图动态模型的参数作为扩展卡尔曼滤波器的状态进行心电图去噪。
DOI：
10.1109/iembs.2007.4352848
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Shamsollahi,MohammadB
中科院分区：
文献类型：
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作者：
Sayadi,Omid;Sameni,Reza;Shamsollahi,MohammadB
本文提出了一种基于扩展卡尔曼滤波(EKF)的有效滤波方法。该方法基于一种改进的非线性动态模型，该模型先前被引入用于生成合成的心电信号。我们建议将简单动力学作为模型参数的控制方程。因为我们对这些新的状态变量没有任何观察，所以它们被认为是隐态。对MIT-BIH信号的定量评估表明，对于-5分贝的信号，平均信噪比提高了12分贝。结果表明，在没有这些新动态的情况下，与扩展卡尔曼滤波的输出相比，输出信噪比有所提高。
In this paper an efficient filtering procedure based on the extended Kalman filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics.