Hilbert–Huang transformation-based time-frequency analysis methods in biomedical signal applications

Abstract

Hilbert–Huang transformation, wavelet transformation, and Fourier transformation are the principal time-frequency analysis methods. These transformations can be used to discuss the frequency characteristics of linear and stationary signals, the time-frequency features of linear and non-stationary signals, the time-frequency features of non-linear and non-stationary signals, respectively. The Hilbert–Huang transformation is a combination of empirical mode decomposition and Hilbert spectral analysis. The empirical mode decomposition uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions. Hilbert transforms are then used to transform the intrinsic mode functions into instantaneous frequencies, to obtain the signal’s time-frequency-energy distributions and features. Hilbert–Huang transformation-based time-frequency analysis can be applied to natural physical signals such as earthquake waves, winds, ocean acoustic signals, mechanical diagnosis signals, and biomedical signals. In previous studies, we examined Hilbert–Huang transformation-based time-frequency analysis of the electroencephalogram FP1 signals of clinical alcoholics, and ‘sharp I’ wave-based Hilbert–Huang transformation time-frequency features. In this paper, we discuss the application of Hilbert–Huang transformation-based time-frequency analysis to biomedical signals, such as electroencephalogram, electrocardiogram signals, electrogastrogram recordings, and speech signals.