Baseline wander removal methods for ECG signals: A comparative study

TL;DR

This study compares nine baseline wander removal methods for ECG signals, evaluating their effectiveness using synthetic and real data, and finds the FIR high-pass filter with a 0.67 Hz cutoff to be most effective.

Contribution

It provides a comprehensive comparison of nine BLW removal techniques for ECG signals, highlighting the superior performance of FIR high-pass filtering.

Findings

FIR high-pass filter with 0.67 Hz cutoff performs best

Multiple evaluation metrics used for comprehensive assessment

Synthetic and real ECG data used for validation

Abstract

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.