1. A method, comprising: extracting one or more test features from a time domain signal, wherein the one or more test features are represented by discrete data with a processing system; representing the discrete data for each of the one or more test features by a corresponding one or more fitting functions with the processing system, wherein each fitting function is defined in terms of a finite number of continuous basis functions and a corresponding finite number of expansion coefficients; compressing the fitting functions through Functional Principal Component Analysis (FPCA) with the processing system to generate corresponding sets of principal components of the fitting functions for each test feature, wherein each principal component for a given test feature is uncorrelated to each other principal component for the given test feature; calculating a distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system; classifying the test feature according to the distance calculated with the processing system; and adjusting a state of the processing system according to a classification of the test feature determined from the distance calculated.