Adaptive filtering of ECG baseline wander

Baseline wander removal is a classical problem in ECG signal processing. We present a cascade adaptive filter to remove the baseline wander in the ECG preserving the overlapped deterministic low frequency components of the ECG, such as ST segment components. This cascade adaptive filter works in two stages. The first stage is an adaptive notch filter at zero frequency. The second stage is an adaptive impulse correlated filter that estimates the ECG signal correlated with the QRS occurrence. In both stages the LMS algorithm is used with different gain constants μ1 and μ2. We analyse the frequency response of the filter as a function of the μ1 and μ2 parameters, selecting those more appropriated for baseline removal. Finally, the performance of the filter is studied on an actual ECG affected by baseline drift.