Akhbari Mahsa, Shamsollahi Mohammad B, Jutten Christian, Armoundas Antonis A, Sayadi Omid
Biomedical Signal and Image Processing Laboratory (BiSIPL), Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran. GIPSA-Lab, Department of Images and Signals, CNRS and University of Grenoble-Alpes, France.
Physiol Meas. 2016 Feb;37(2):203-26. doi: 10.1088/0967-3334/37/2/203. Epub 2016 Jan 15.
In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 ms and 22 ms, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 ms and 29 ms, the proposed method achieves better accuracy and smaller variability with respect to other methods.
在本文中，我们提出了一种用于心电图（ECG）信号去噪和提取基准点（FP）的有效方法。该方法基于一个非线性动态模型，该模型使用高斯函数对ECG波形进行建模。为了估计模型参数，我们使用了扩展卡尔曼滤波器（EKF）。在这个称为EKF25的框架中，高斯函数的所有参数以及ECG动态模型中的ECG波形（P波、QRS复合波和T波）都被视为状态变量。在本文中，动态时间规整方法用于估计非线性ECG相位观测值。我们将这种新方法与线性相位观测模型进行了比较。使用线性和非线性EKF25进行ECG去噪，以及使用非线性EKF25进行基准点提取和ECG间期分析是本文的主要贡献。与其他基于EKF的技术的性能比较表明，对于-8 dB的输入信噪比，所提出的方法能产生更高的输出信噪比，平均信噪比提高12 dB。为了评估FP提取性能，我们将所提出的方法与基于部分折叠吉布斯采样器的方法和一种既定的基于EKF的方法进行了比较。对于我们提出的方法，所有数据库中所有FP的平均绝对误差和均方根误差分别为14 ms和22 ms，在使用非线性相位观测时具有优势。这些误差明显小于其他方法获得的误差。对于ECG间期分析，所提出的方法的绝对平均误差和约为22 ms，均方根误差约为29 ms，相对于其他方法具有更高的准确性和更小的变异性。
