论文标题
使用集合卡尔曼过滤器的新型心电图降级计划
A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter
论文作者
论文摘要
心电图（ECG）的监测提供重要信息以及任何心血管异常。可穿戴电子技术技术的最新进展使紧凑型设备能够在家庭环境中获取个人生理信号。但是，信号通常被高级噪声污染。因此，有效的ECG过滤方案是艰巨的需求。在本文中，开发了一种使用集合卡尔曼滤波器（ENKF）的新方法来降低心电图信号。我们还深入探索了各种过滤算法，包括Savitzky-Golay（SG）过滤器，合奏经验模式分解（EEMD），归一化最小均值平方（NLMS），递归最小二乘（RLS）滤镜（RLS）过滤器，总变异型（TVD），波特和扩展的Kalman滤波器（EKFERSISION）。使用了MIT-BIH噪声应力测试数据库的数据。提出的方法显示，来自修改后的MIT-BIH数据库的平均信号与噪声比（SNR）为10.96，根差百分比为150.45，相关系数为0.959。
Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts.
