Biomedical signals often represent non-stationary and non-linearly coupled time series
resulting from a non-linear superposition of underlying modes which are indicative of the current
state of the biomedical system monitored. Their non-linear coupling and non-stationary nature
exacerbates their interpretation and presumes profound expert knowledge and experience. Recently,
an empirical nonlinear analysis method for complex, non-stationary time series has been
pioneered by N. E. Huang. It is commonly referred to as Empirical Mode Decomposition (EMD)
and adaptively and locally decomposes such time series into a sum of oscillatory modes, called
Intrinsic Mode Functions (IMF). Their Hilbert-Huang transform provides exact time-frequency
spectra and their related instantaneous amplitudes and energies. Thereby new and important insights
can be gained as each relevant mode can be extracted in isolation. This provides new
insights into their interdependencies and allows to identify typical signatures when the latter start
to behave abnormally. Classical time series analysis methods fail to provide such insights as they
are not prepared to deal with non-stationary and non-linearly coupled signals. The contribution
reviews the technique of EMD and related algorithms and shortly discusses recent applications to
biomedical problems.
Keywords: empirical mode decomposition (EMD), time series analysis, non-stationary, brain status data
