Automatic segmentation and classification of ionic-channel signals

An automatic channel detection algorithm is proposed. This algorithm is based on sequential minimization of an index which is usually used in cluster analysis. The algorithm consists of two stages, namely segmentation and classification. In the first stage, the signal samples are segmented based on the assumption that the samples in each segment should be sequentially connected. In the second stage, the resultant segments are classified with no regard to their connectivities. The algorithm is computationally fast and globally optimum. The criterion function used in this algorithm is the ratio of the within-class variation over the between-class variation. An information-theoretic criterion that can be used mainly as a stopping rule in the segmentation stage is also proposed. Results on synthetic and real channel currents suggest that this algorithm will substantially increase the productivity of many laboratories involved in ionic-channel research.