Decomposition of biomedical signals for enhancement of their time-frequency distributions

Bilinear time-frequency distributions have been widely utilized in the anal ysis of nonstationary biomedical signals. A problem often arises where the time-frequency components with small-amplitude values cannot be displayed c learly. This problem results from a masking effect on these components caus ed by the presence of high-energy slow waves and sharp patterns in the inpu t which produce large values in the time-frequency distribution. These larg e values often appear in the time-frequency plane as irregular patterns in the low-frequency range (due to slow waves), and as wide-band, impulsive co mponents at certain points in time (due to sharp patterns). In this work we present an effective signal pre-processing method using a nonlinear operat ion on wavelet coefficients. This method equalizes the energy of different time-frequency components in the data so that the masking effect is greatly reduced, while the original time-frequency features of the input signal ar e preserved. Comparative experiments on electroencephalographic data with a nd without using this method have shown a clear improvement in the readabil ity and sensitivity in bilinear time-frequency distributions. (C) 2000 The Franklin Institute. Published by Elsevier Science Ltd. All rights reserved.