  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.
