Automatic segmentation and classification of ionic-channel signals.
Identification of ionic-channel types and their selectivity depends critically on the open channel current that can be resolved. In this paper, an automatic channel detection algorithm is proposed that 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. Results on synthetic and real channel currents are very encouraging and they suggest that this algorithm will substantially increase the productivity of many laboratories involved in ionic-channel research.
Moghaddamjoo
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Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, WI 53201.
D000465:Algorithms; D007473:Ion Channels / Q000502:physiology*; D008954:Models, Biological; D012815:Signal Processing, Computer-Assisted
D007473:Ion Channels