Fractal filtering of channel data

The fractal dimension of subsets of time series data can be used to modulate the extent of filtering to which the data is subjected. In general, such fractal filtering makes it possible to retain large transient shifts in baseline with very little decrease in amplitude, while the baseline noise itself is markedly reduced (Strahle, W.C. (1988) Electron. Lett. 24, 1248-1249). The fractal filter concept is readily applicable to single channel data in which there are numerous opening/closing events and flickering. Using a simple recursive filter of the form: Yn = w.Yn-1 + (1 - w)Xn, where Xn is the data, Yn the filtered result, and w is a weighting factor, 0 less than w less than 1, we adjusted w as a function of the fractal dimension (D) for data subsets. Linear and ogive functions of D were used to modify w. Of these, the ogive function: w = [1 + p(1.5-D)]-1 (where p affects the amount of filtering), is most useful for removing extraneous noise while retaining opening/closing events.