Métodos para a eliminação de flutuações de linha base em sinais de eletrocardiograma: estudo comparativo

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 ambulatory way. Under these acquisition conditions the ECG is strongly affected by some types of noise, mainly by baseline (BL) wander. Currently in the literature, there are several methods for the elimination of BL, but no comparative study has been found that quantitatively evaluates these methods using the same signals. In this work a spectral characterization of the BL was made where it has been proven that these contain spectral components up to 3.14 Hz which exceeds the values established by the American Heart Association. We also implemented nine methods for the elimination of BL, which are interpolation using cubic splines, FIR filtering, IIR filtering, LMS adaptive filtering, moving average filter, independent component analysis, interpolation and successive median subtractions, decomposition in empirical modes and wavelet filtering. They were evaluated qualitatively and quantitatively. For the quantitative evaluation, the following similarity metrics were used: absolute maximum distance, sum of squares of distances and percentage of mean square error distance. Several experiments were performed using synthetic ECG signals, real QT Database ECG, artificial and real BL noises from the Noise Stress Test Database. The best results were obtained by the method based on FIR high pass filtering with cutoff frequency of 0.67 Hz.