Transform domain features for ion-channel signal classification using support vector machines

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 analytes can be performed by extracting appropriate features from the ion-channel signals and using them for classification. In this paper, we consider features extracted from the Fourier, Wavelet and Walsh-Hadamard domain representations of the ion-channel signals. The proposed approach uses the power distribution information in the transform domains as features for discrimination. We compare the performance of all the three sets of features using support vector machines for classification of analytes and present the results. Results obtained show that the transform domain features achieve high classification rates in addition to high sensitivity and specificity rates.