Baseline correction of ECG using regression estimation method

Abstract

The presence of baseline wander in ECG signal severely affects the quality of ECG signal. Baseline wander is generally eliminated at the initial stage of preprocessing of ECG signal. In this paper a method using regression estimation is applied to ECG signal to remove the baseline wander. This method uses two stage median filter and regression estimation for smoothing and correction of baseline. The cross correlation is performed between the baseline wander containing ECG signal with corrected ECG signal. Then cross-correlation coefficient is calculated. The method evolves with the lowess and loess of local regression estimation, rlowess and rloess of robust local regression estimation. We found highest coefficient of 0.9914 for the baseline wander amplitude of 0.5mV using local regression method (lowess) and lowest coefficient of 0.9688 for the amplitude of 0.8mV using robust local regression method (rlowess). Further this method is validated on MIT-BIH, Noise Stress Test Database (NSTD).