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.