부산대학교 도서관

Various authors have designed multirate filter banks for modeling a given process. The existing algorithms, however, suffer from some major drawbacks; for example, some of these filter banks are not signal adaptive; in most of the cases the, filter computations are not efficient; moreover, the existing signal-adapted filter banks is not always optimal. To address these limitations, we propose an optimal signal matched filter bank (SMFB) to maximize the coding gain. The objective is to achieve spectral majorization and complete decorrelation of the sub-band signals. The filter bank is designed here to perfectly reconstruct only the given signal, instead of every signal from the finite energy signal space, as is typically done. These objectives are achieved by whitening the outputs of the analysis filter bank, within as well as across the channels. With these orthogonal whitened signals as inputs, the synthesis filter bank is designed to reconstruct a delayed version of the given signal. A time and order recursive least squares algorithm has been developed for the SMFB, with a single realization of the signal. The recursions of the algorithm lead to a lattice structure. Since the filter coefficients are not directly available from the lattice, a fast algorithm is developed to estimate the same using the lattice parameters. Simulation results are presented to verify the efficacy of the proposed theory. We also present an automated real-time sleep apnea detection algorithm using SMFB which gives a 92.27 % accuracy, 88.60 % sensitivity, and 94.31 % specificity on a publicly available MIT PhysioNet Apnea-ECG dataset. [ABSTRACT FROM AUTHOR]