Flicker-Noise Spectroscopy (FNS), a general approach to the extraction and parameterization of resonant and stochastic
components contained in medical time series, is presented. The basic idea of FNS is to treat the correlation links present
in sequences of different irregularities, such as spikes, "jumps", and discontinuities in derivatives of different orders, on
all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and
analyze the information are power spectra and difference moments (structural functions), which complement the
information of each other. The structural function stochastic component is formed exclusively by "jumps" of the
dynamic variable while the power spectrum stochastic component is formed by both spikes and "jumps" on every level
of the hierarchy. The information "passport" characteristics that are determined by fitting the derived expressions to the
experimental variations for the stochastic components of power spectra and structural functions are interpreted as the
correlation times and parameters that describe the rate of "memory loss" on these correlation time intervals for different
irregularities. The number of the extracted parameters is determined by the requirements of the problem under study.
Application of this approach to the analysis of tremor velocity signals for a Parkinsonian patient is discussed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.