Baseline wander removal for bioelectrical signals by quadratic variation reduction

•A novel approach to baseline wander removal for bioelectrical signals is proposed.•The approach is based on the notion of quadratic variation reduction.•Baseline wander is estimated solving a constrained convex optimization problem.•The algorithm has linear complexity and admits a simple implementation.•Results on ECG, EEG and EMG recordings highlight the effectiveness of the approach.

Baseline wander is a low-frequency additive noise affecting almost all bioelectrical signals, in particular the ECG. In this paper, we propose a novel approach to baseline wander estimation and removal for bioelectrical signals, based on the notion of quadratic variation reduction. The quadratic variation is meant as a measure of variability for vectors or sampled functions, and is a consistent measure in this regard. Baseline wander is estimated solving a constrained convex optimization problem where quadratic variation enters as a constraint. The solution depends on a single parameter whose value is not critical, as proven by a sensitivity analysis. Numerical results confirm the effectiveness of the approach, which outperforms state-of-the-art algorithms. The algorithm compares favorably also in terms of computational complexity, which is linear in the size of the vector to detrend. This makes it suitable for real-time applications as well as for applications on devices with reduced computing power, such as handheld devices.