  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.
