Deriving Baseline Detection Algorithms from Verbal Descriptions

a flat baseline. But usually, peak overlap, negative peaks and drifts of varying sign can occur, and even disturbances of the kind shown in figure 3 can be recorded. Thus, for correct peak measurement, and therefore, for correct quantification, a correcting signal, the baseline, is searched for ignoring peak overlap, separating positive peaks from negative ones, and following drifts and ruptures. Other approaches. Most automatic baseline detection strategies (e.g. [3]) assume a mainly horizontal and straight curve. Following this, baseline candidates are those points having small or zero slope. Thus, minima between overlapping peaks and the tips of negative peaks will be marked by mistake. This problem is usually handled by introducing a threshold upon the drift: the line connecting two adjacent points marked as baseline members may not exceed a given slope. Though including additional data points inthe decision, the strategy remains local. Threshold criteria on slope a