USE OF EMPIRICAL MODE DECOMPOSITION AND HILBERT-HUANG TRANSFORM IN THE ANALYSIS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Time-frequency and temporal analyses have been widely used in biomedical signal processing. Historically, Fourier spectral analysis has been used for this purpose, but is valid only under extremely general conditions with some crucial restrictions. This paper presents the use of a new signal processing tools, namely the Empirical Mode Decomposition (EMD) and the Hilbert-Huang transform (HHT). This is an alternative approach to the analysis of non stationary and non linear signals, and is based on the assumption that any signal consists of different simple intrinsic mode oscillations. The application considered here is the analysis of neuronal signals using EMD and HHT, to be used to identify and decompose the rhythms in the central nervous system. Rhythms of the nervous system have been linked to important behavioral and cognitive states, including attention, memory, object recognition, sensory motor integration, perception, and language processing. Experimental data were collected from the cerebral cortex of several rats; one group had been exposed to the cigarette smoke in-utero, while the other group had not. The recordings were of event-related potentials produced in response to auditory stimulus. Validation of the algorithm was done by applying it to artificially constructed signals. Preliminary analyses indicate that the signals from unexposed and exposed rats do show differences in the frequency content. Instantaneous frequency information may be extracted from the HHT, providing information on the oscillations/changes. Temporal structure of the neuronal oscillations may also be analyzed using the intrinsic mode functions. Further work is being pursued along these lines on new sets of data, as well as comparison with other algorithms.