Extracting aperiodic components from a time-series wave data set
AssigneeBrigham Young University
InventorsBruce Leonard Brown, Suzanne B. Hendrix, Dawson W. Hedges
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Abstract
A method is described for extracting aperiodic components from a time-series wave data set for diagnosis purposes. The method may include collecting time-series wave data within a controlled environment were a plurality of contrasting conditions can be used in collecting the time-series wave data set. Aperiodic components can be extracted from the time-series wave data set and the aperiodic components can then be fitted to the plurality of contrasting conditions of the controlled environment to product regressed aperiodic components from which diagnostic determination can be made.
Claims21
A method for extracting aperiodic components from a time-series wave data set for classification purposes, comprising:
- subjecting a plurality of subjects to one or more contrasting conditions;
- under control of one or more computer systems configured with executable instructions, collecting, into a data store of the computer system, a time-series wave data set for each of the plurality of subjects that includes contrasting conditions;
- performing, using a component extraction module of the computer system, component analysis of each of the time-series wave data sets to extract a first set of aperiodic components for each of the plurality of subjects that represent the contrasting conditions, wherein performing component analysis comprises creating a covariance matrix of time points from the time-series wave data set and performing principal component analysis to extract the first set of aperiodic components from the covariance matrix;
- performing, using the component extraction module, component analysis of all of the first sets of aperiodic components to produce a second set of aperiodic components that represent the plurality of subjects and classifications of conditions associated with the plurality of subjects, wherein performing component analysis comprises creating a covariance matrix of all of the first sets of aperiodic components and performing principal component analysis to extract the second set of aperiodic components from the covariance matrix;
- selecting an individual subject for analysis and identifying the subject's first set of aperiodic components;
- and
- fitting, using a component fitting module of the computer system, the subject's first set of aperiodic components to the second set of aperiodic components to identify a relationship with the classifications of conditions.
A computer implemented method, comprising:
- subjecting a plurality of subjects to a plurality of contrasting conditions;
- under control of one or more computer systems configured with executable instructions,
- collecting time-series wave data for each of the plurality of subjects that includes the plurality of contrasting conditions;
- extracting an ASD (aperiodic spectral decomposition) component from the time-series wave data using spectral decomposition;
- and
- fitting the ASD component to the plurality of contrasting conditions thereby providing an RASD (regressed aperiodic spectral decomposition) component from which diagnostic determinations are made.
A system for extracting aperiodic components from a time-series wave data set, comprising:
- a processor;
- a memory device including instructions that, when executed by the processor, cause the processor to execute:
- a factoring module to perform component analysis of a time-series wave data set where principal components are extracted from the time-series wave data set that represent a plurality of factors used to collect the time-series wave data set;
- a regression module to create regressed principal components by performing regression analysis of the principal components;
- and
- an analysis module to analyze the regressed principal components to identify characteristics associated with the regressed principal components.
Dependencies are inferred from the claim text.