Abstract
In this paper, we suggest a novel method for ECG baseline correction, exclusively based on pattern recognition tools, namely, dominant points (DPs). The DPs are computed by the Douglas-Peucker curve simplification algorithm. The so-computed DPs include peak and baseline points, the discrimination of which yields gradual piecewise linear estimation of the baseline wander (BaselineW) in two iterations. At each iteration, the current BaselineW is subtracted from the input signal according to the decomposition scheme: ECG approximately ECG(ZBLW) + BaselineW, where ECG(ZBLW) is the underlying baseline wander free ECG. The method targets many types of baseline deviations in a unified approach: baseline drift due to respiration, amplitude modulation due to perspiration and abrupt potential change due to electrode loose contact. We tested the developed method on a variety of ECG records including half synthesized records contaminated with different types of baseline deviations (simulated) noise, and on records from the MITBIH database presenting important baseline deviations, including normal and abnormal heart beats cases. The method showed good performance in computing a piecewise linear estimation of the baseline deviation and in extracting the ECG(ZBLW), which represents the clinically significant electrocardiogram information.