Time–frequency methods for trend removal in electrochemical noise data

Electrochemical current and potential noise signals in many cases exhibit a DC drift that should be removed prior to further data analysis. The theoretical ability of wavelet analysis and empirical mode decomposition to effectively remove only the DC drift component is evaluated based on their mutual performance for the first time. The correlation coefficient between individual signals after both trend removal techniques proved to be superior compared to the correlation coefficients between these and the signals after moving average, polynomial and linear trend removal. The residual power of these signals was compared and again the two time–frequency methods acknowledged their theoretical ability of removing only a well-defined part of the data.