Midpoint-based empirical decomposition for nonlinear trend estimation

This paper presents a new method for nonlinear trend estimation of non-stationary signals, by which the trend can be self-adaptively decomposed through calculating the midpoint-based local means. In this method, the so-called midpoints are proposed to construct the local mean of a signal instead of two envelopes in the classical empirical mode decomposition (EMD) algorithm, thus resulting in the midpoint-based empirical decomposition. Furthermore, a negentropy-based statistical method is presented to justify decomposition of the trend. Simulation results indicate that the new algorithm improves the performance of signal decomposition and trend estimation in comparison with the classical EMD algorithm. The proposed method also shows the value in self-adaptively estimating the nonlinear respiratory component from non-invasively measured ventilation signals.