# Baseline wander removal methods for ECG signals: A comparative study

arXiv: https://arxiv.org/abs/1807.11359 · HTML: https://mlsanity.com/paper/1807.11359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal](https://mlsanity.com/paper/2208.00542/) (2022)
- [Cyclostationary Phase Conditioning for Medical Time Series Diffusion](https://mlsanity.com/paper/2609.34965/) (2026)

## Abstract (arXiv)

Cardiovascular diseases are the leading cause of death worldwide, accounting for 17.3 million deaths per year. The electrocardiogram (ECG) is a non-invasive technique widely used for the detection of cardiac diseases. To increase diagnostic sensitivity, ECG is acquired during exercise stress tests or in an ambulatory way. Under these acquisition conditions, the ECG is strongly affected by some types of noise, mainly by baseline wander (BLW). In this work were implemented nine methods widely used for the elimination of BLW, which are: interpolation using cubic splines, FIR filter, IIR filter, least mean square adaptive filtering, moving-average filter, independent component analysis, interpolation and successive subtraction of median values in RR interval, empirical mode decomposition and wavelet filtering. For the quantitative evaluation, the following similarity metrics were used: absolute maximum distance, the sum of squares of distances and percentage root-mean-square difference. Several experiments were performed using synthetic ECG signals generated by ECGSYM software, real ECG signals from QT Database, artificial BLW generated by software and real BLW from the Noise Stress Test Database. The best results were obtained by the method based on FIR high-pass filter with a cut-off frequency of 0.67 Hz.