Modified local discriminant bases and its applications in signal classification [biomedical signal examples]

One of the major challenges in classification problems, based on the signal decomposition approach, is to identify the right basis function and its derivatives that can provide optimal features to distinguish the classes. With the vast amount of available libraries of orthonormal bases, it is hard to select an optimal set of basis functions for a specific dataset. To address this problem, pruning algorithms based on certain selection criteria, are needed. The local discriminant bases (LDB) algorithm is one such algorithm, which efficiently selects a set of significant basis functions from the library of orthonormal bases based on a certain defined dissimilarity measure. The selection of this dissimilarity measure is critical as they indirectly contribute to the performance accuracy of the LDB algorithm. In this paper, we study the impact of the dissimilarity measures on the performance of the LDB algorithm with two classification examples. Two biomedical signal databases used are: 1) vibroarthographic signals (VAG) - 89 signals with 51 normal and 38 abnormal; and 2) pathological speech signals - 100 signals with 50 normal and 50 pathological. Classification accuracies of 76.4% with the VAG database and 96% with the pathological speech database were obtained. This modified method of signal analysis using LDB has shown its powerfulness in analyzing non-stationary signals.