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. Fourier spectral analysis has been the standard method for examining global amplitude-frequency distributions. However, Fourier Transform is valid under two crucial restrictions: 1) the system must be linear; and 2) the data must be strictly periodic or stationary. The available 'real-world' data are usually of finite duration, nonstationary, and from systems that are frequently intrinsically nonlinear. Under these conditions, Fourier analysis is of limited use. For lack of suitable alternatives, however, Fourier analysis is still employed to process such data. The loose application of Fourier analysis and the assumption of linearity and stationarity may lead to misleading conclusions. The recently developed Hilbert-Huang Transform (HHT) provides a powerful generic algorithm for data analysis. It has the advantages of being able to handle short, nonstationary, and nonlinear data sets. We are applying this method to investigate the effects of disease and aging to neuroautonomic control of heart rate.
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