Hesar Hamed Danandeh, Hesar Amin Danandeh
Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
Department of Electrical and Computer Engineering and Advanced Technologies, Urmia University, Urmia, Iran.
Biomed Eng Lett. 2024 Mar 8;14(4):689-705. doi: 10.1007/s13534-024-00362-7. eCollection 2024 Jul.
Model-based Bayesian approaches have been widely applied in Electrocardiogram (ECG) signal processing, where their performances heavily rely on the accurate selection of model parameters, particularly the state and measurement noise covariance matrices. In this study, we introduce an adaptive augmented cubature Kalman filter/smoother (CKF/CKS) for ECG processing, which updates the noise covariance matrices at each time step to accommodate diverse noise types and input signal-to-noise ratios (SNRs). Additionally, we incorporate the dynamic time warping technique to enhance the filter's efficiency in the presence of heart rate variability. Furthermore, we propose a method to significantly reduce the computational complexity required for CKF/CKS implementation in ECG processing. The denoising performance of the proposed filter was compared to those of various nonlinear Kalman-based frameworks involving the Extended Kalman filter/smoother (EKF/EKS), the unscented Kalman filter/smoother (UKF/UKS), and the ensemble Kalman filter (EnKF) that was recently proposed for ECG enhancement. In this study, we conducted a comprehensive evaluation and comparison of the performance of various nonlinear Kalman-based frameworks for ECG signal processing, which have been proposed in recent years. Our assessment was carried out on multiple normal ECG segments extracted from different entries in the MIT-BIH Normal Sinus Rhythm Database (NSRDB). This database provides a diverse set of ECG recordings, allowing us to examine the filters' denoising capabilities across various scenarios. By comparing the performance of these filters on the same dataset, we aimed to provide a thorough analysis and identification of the most effective approach for ECG denoising. Two kinds of noises were introduced to such segments: 1-stationary white Gaussian noise and 2-non-stationary real muscle artifact noise. For evaluation, four comparable measures namely the SNR improvement, PRD, correlation coefficient and MSEWPRD were employed. The findings demonstrated that the suggested algorithm outperforms the EKF/EKS, EnKF/EnKS, UKF/UKS methods in both stationary and nonstationary environments regarding SNR improvement, PRD, correlation coefficient and MSEWPRD metrics.
基于模型的贝叶斯方法已广泛应用于心电图（ECG）信号处理，其性能在很大程度上依赖于模型参数的准确选择，特别是状态和测量噪声协方差矩阵。在本研究中，我们引入了一种用于ECG处理的自适应增强容积卡尔曼滤波器/平滑器（CKF/CKS），它在每个时间步更新噪声协方差矩阵，以适应不同的噪声类型和输入信噪比（SNR）。此外，我们纳入了动态时间规整技术，以提高滤波器在存在心率变异性时的效率。此外，我们提出了一种方法，可显著降低ECG处理中实现CKF/CKS所需的计算复杂度。将所提出滤波器的去噪性能与各种基于非线性卡尔曼的框架进行了比较，这些框架包括扩展卡尔曼滤波器/平滑器（EKF/EKS）、无迹卡尔曼滤波器/平滑器（UKF/UKS）以及最近提出用于ECG增强的集合卡尔曼滤波器（EnKF）。在本研究中，我们对近年来提出的各种基于非线性卡尔曼的ECG信号处理框架的性能进行了全面评估和比较。我们的评估是在从麻省理工学院 - 贝斯以色列女执事医疗中心正常窦性心律数据库（NSRDB）的不同条目中提取的多个正常ECG片段上进行的。该数据库提供了各种各样的ECG记录，使我们能够在各种场景下检查滤波器的去噪能力。通过在同一数据集上比较这些滤波器的性能，我们旨在对ECG去噪的最有效方法进行全面分析和识别。向这些片段引入了两种噪声：1 - 平稳白高斯噪声和2 - 非平稳真实肌肉伪迹噪声。为了进行评估，采用了四个可比较的指标，即SNR改善、PRD、相关系数和MSEWPRD。研究结果表明，在平稳和非平稳环境中，就SNR改善、PRD、相关系数和MSEWPRD指标而言，所建议的算法优于EKF/EKS、EnKF/EnKS、UKF/UKS方法。
