Fractal index analysis of human electroencephalogram signals
AssigneesThe Regents of the University of California, U.S. Government as represented by the Department of Veterans Affairs
InventorsTodd S. Zorick, Mark Mandelkern
Life of this patent
Estimating the term…
Abstract
A system and method for Multifractal-Detrended Fluctuation Analysis (MF-DFA) on digitized Human EEG signals is presented. A list of Hurst exponents, or Hurst exponent spectrum (“h” values) are generated, and multifractal singularity spectrum indices (“D(h)” values) produce a graph that approximates an inverted parabola. The output multifractal DFA spectrum is able to represent key features of the internal neuronal dynamics for the cortical neurons underlying the scalp-placed electrode which records the signals.
Claims38
An apparatus for analyzing human electroencephalogram (EEG) signals, comprising:
- (a) a processor;
- and
- (b) programming executable on the processor and configured for:
- (i) acquiring a digitized set of sequential EEG voltage recordings as a function of time;
- (ii) performing multifractal-detrended fluctuation analysis (MF-DFA) on the set of sequential EEG voltage recordings;
- and
- (iii) outputting a MF-DFA spectrum corresponding to the set of sequential EEG voltage recordings.
An apparatus for analyzing human EEG signals, comprising:
- (a) a processor;
- (b) programming executable on the processor and configured for:
- (i) acquiring a digitized set of sequential EEG voltage recordings as a function of time;
- (ii) subtracting a mean voltage value from each EEG voltage recording in the set of sequential EEG voltage recordings to generate an EEG profile;
- (iii) selecting a sequence of scales corresponding to a length of a segment of consecutive data points within the EEG profile;
- (iv) for each scale, dividing the EEG profile into non-overlapping segments of equal scale;
- (v) performing a fit to points within each segment of the EEG profile to a polynomial of a detrending order to generate a variance of residual values for each segment;
- (vi) constructing a sequence of q values;
- (vii) generating a spectrum of generalized Hurst exponents h for each value q in the sequence of q values;
- and
- (viii) generating a MF-DFA tau(q) spectrum as a function of each of the generalized Hurst exponents h for each value q.
A method for analyzing human EEG signals, comprising:
- acquiring a digitized set of sequential EEG voltage recordings as a function of time;
- subtracting a mean voltage value from each EEG voltage recording in the set of sequential EEG voltage recordings to generate an EEG profile;
- selecting a sequence of scales corresponding to a length of a segment of consecutive data points within the EEG profile;
- for each scale, dividing the EEG profile into non-overlapping segments of equal scale;
- performing a fit to points within each segment of the EEG profile to a polynomial of a detrending order to generate a variance of residual values for each segment;
- constructing a sequence of q values;
- generating a spectrum of generalized Hurst exponents h for each value q in the sequence of q values;
- and
- generating a MF-DFA tau(q) spectrum as a function of each of the generalized Hurst exponents h for each value q.
Dependencies are inferred from the claim text.