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

Bilinear time-frequency distributions have been widely utilized in the analysis of nonstationary biomedical signals. A problem often arises where the time-frequency components with small-amplitude values cannot be displayed clearly. This problem results from a masking effect on these components caused by the presence of high-energy slow waves and sharp patterns in the input which produce large values in the time-frequency distribution. These large 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 components at certain points in time (due to sharp patterns). In this work we present an effective signal pre-processing method using a nonlinear operation 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 are preserved. Comparative experiments on electroencephalographic data with and without using this method have shown a clear improvement in the readability and sensitivity in bilinear time-frequency distributions. (C) 2000 The Franklin Institute. Published by Elsevier Science Ltd. All rights reserved.