Baseline wander estimation and removal by quadratic variation reduction

The baseline wander is a low frequency additive noise partially overlapping the band of ECG signal. This makes its removal difficult without affecting the ECG. In this work we propose a novel approach to baseline wander estimation and removal based on the notion of quadratic variation. The quadratic variation is a suitable index of variability for vectors and sampled functions. We derive an algorithm for baseline estimation solving a constrained convex optimization problem. The computational complexity of the algorithm is linear in the size of the ECG record to detrend, making it suitable for realtime applications. Simulation results confirm the effectiveness of the approach and highlight its ability to remove baseline wander. Eventually, the proposed algorithm is not limited to ECG signals, but can be effectively applied whenever baseline estimation and removal are needed, such as EEG records.