基于 NLMS 的改进型自适应去噪方法在 ECG 信号中的应用。
Improved NLMS-based adaptive denoising method for ECG signals.
机构信息
School of Electrical Engineering, Anhui Polytechnic University, Wuhu, Anhui, China.
Key Laboratory of Detection Technology and Energy Saving Devices, Wuhu, Anhui, China.
出版信息
Technol Health Care. 2021;29(2):305-316. doi: 10.3233/THC-202659.
BACKGROUND
Traditional least mean square algorithm (LMS) tends to converge faster and thus the larger the steady-state error of the algorithm.
OBJECTIVE
In order to solve this issue, an improved adaptive normalized least mean square (NLMS) ECG signal denoising algorithm is proposed through utilizing the NLMS and the least mean square algorithm with added momentum term (MLMS).
METHODS
The algorithm firstly performs LMS adaptive filtering on the original ECG signal. Then, the algorithm uses the relative error of the prior error signal and the posterior error signal before and after filtering to adaptively determine the iteration step factor. Finally, the expected error is set to determine whether the denoising meets the expected requirements. This method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology.
RESULTS
Experimental results have shown that the proposed algorithm can achieve good denoising for the target signal, and the average signal to noise ratio (SNR) of the proposed method is 17.6016, the RMSE is only 0.0334, and the average smoothness index R is only 0.0325.
CONCLUSION
The proposed algorithm effectively removes the original ECG signal noise, and improves the smoothness of the signal the denoising efficiency.
背景
传统的最小均方算法（LMS）往往收敛速度更快，因此算法的稳态误差越大。
目的
为了解决这个问题，通过利用 NLMS 和带附加动量项的最小均方算法（MLMS），提出了一种改进的自适应归一化最小均方（NLMS）ECG 信号去噪算法。
方法
该算法首先对原始 ECG 信号进行 LMS 自适应滤波。然后，该算法使用滤波前后误差信号的相对误差，自适应地确定迭代步长因子。最后，设置期望误差以确定去噪是否满足预期要求。该方法应用于麻省理工学院建立的 MIT-BIH ECG 数据库。
结果
实验结果表明，所提出的算法可以很好地对目标信号进行去噪，该方法的平均信噪比（SNR）为 17.6016，均方根误差（RMSE）仅为 0.0334，平均平滑度指数 R 仅为 0.0325。
结论
所提出的算法有效地去除了原始 ECG 信号噪声，提高了信号的平滑度和去噪效率。
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