Transform domain features for ion-channel signal classification

The study of the behavior of ion-channels can provide significant information to detect metal ions and small organic molecules in solution. Discrimination of different ion-channels can be performed by extracting appropriate features from their signals and using them for classification. In this paper, we consider features extracted from the Fourier, wavelet and Walsh power spectra of the ion-channel signals. We compare the performance of all the three sets of features using support vector machines. We perform classification of signals from simulated and real ion-channels and present the results. Results obtained show that the transform domain features achieve high classification rates in addition to high sensitivity and specificity rates.