Method and apparatus for the estimation of anesthetic depth using wavelet analysis of the electroencephalogram
First Claim
1. A method for extracting information from an observed signal representing measured brain activity of a subject in order to evaluate the state of the CNS of said subject, said method comprising:
- a) acquiring a plurality of reference signals, each said reference signal corresponding to a distinct CNS state obtained from a reference subject or subjects;
b) selecting a transformation function which, when applied to one of said reference signals or said observed signal, yields a set of coefficients;
c) selecting a statistical function which, when applied to said set of coefficients derived from one of said reference signals, or a subset of said set of coefficients, yields a reference data set which characterizes the distinct CNS state corresponding to said set of coefficients;
d) applying said transformation and statistical function to said plurality of reference signals to produce a plurality of reference data sets which distinguish between the distinct CNS states corresponding to said reference signals;
e) observing the brain activity of said subject to produce said observed signal;
f) applying said transformation and statistical function to said observed signal to produce an observed data set;
g) comparing the observed data set to one or more of said reference data sets; and
h) computing a numerical value or values representative of said state of the CNS of said subject which results from said comparison.
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Abstract
A method and apparatus to monitor the neurologic state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the neurologic state of a patient undergoing general anesthesia involve a significant time delay between the patient'"'"'s true hypnotic state and the computed indices. The present invention reduces this time delay by using a different analysis technique applied to spontaneous EEG. A wavelet decomposition and statistical analysis of the observed EEG is conducted and compared to reference data to provide a numerical indicator. In addition, this indicator is more consistent with the patient'"'"'s loss of consciousness indicated by the loss of count event than previous systems.
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Citations
36 Claims
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1. A method for extracting information from an observed signal representing measured brain activity of a subject in order to evaluate the state of the CNS of said subject, said method comprising:
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a) acquiring a plurality of reference signals, each said reference signal corresponding to a distinct CNS state obtained from a reference subject or subjects;
b) selecting a transformation function which, when applied to one of said reference signals or said observed signal, yields a set of coefficients;
c) selecting a statistical function which, when applied to said set of coefficients derived from one of said reference signals, or a subset of said set of coefficients, yields a reference data set which characterizes the distinct CNS state corresponding to said set of coefficients;
d) applying said transformation and statistical function to said plurality of reference signals to produce a plurality of reference data sets which distinguish between the distinct CNS states corresponding to said reference signals;
e) observing the brain activity of said subject to produce said observed signal;
f) applying said transformation and statistical function to said observed signal to produce an observed data set;
g) comparing the observed data set to one or more of said reference data sets; and
h) computing a numerical value or values representative of said state of the CNS of said subject which results from said comparison. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31)
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- 25. The method of claim 25 wherein at least one said specific frequency band is chosen such that the statistical representation of said subset of coefficients differentiates between distinct CNS states.
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32. A system for extracting information from an observed signal representing measured brain activity of a subject in order to evaluate the state of the CNS of said subject, given a plurality of reference signals, each said reference signal corresponding to a distinct CNS state obtained from a reference subject or subjects, given a transformation function which, when applied to said observed signal and each of said reference signals, or portions thereof, yields a set of coefficients, and given a statistical function which, when applied to the said set of coefficients derived from each said reference signal, or portions thereof, yields a number of reference data sets which characterize each said distinct reference signal and discriminates between said reference signals, said system comprising:
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a. sensor for observing the electrical brain activity of said subject to produce an observed signal; and
b. digital signal processor for i) applying said transformation function to at least one of said reference signals, or portions thereof, to yield at least one set of coefficients;
ii) applying said statistical function to said set of coefficients derived from each said reference signal, or portions thereof, to yield at least one reference data set;
iii) applying said transformation function and said statistical function to said observed signal to produce an observed data set;
iv) comparing the observed data set to one or more of said reference data sets; and
v) computing a numerical value or values representative of said state of the CNS of said subject which results from said comparison.
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33. A computer program product for extracting information from an observed signal representing measured brain activity of a subject in order to evaluate the state of the CNS of said subject, given a plurality of reference signals, each said reference signal corresponding to a distinct CNS state obtained from a reference subject or subjects, given a transformation function which, when applied to said observed signal and each of said reference signals, or portions thereof, yields a set of coefficients, and given a statistical function which, when applied to said set of coefficients derived from each said reference signal, or portions thereof, yields a number of reference data sets which characterize each said distinct reference signal and discriminates between said reference signals, said computer program product comprising a computer usable medium having computer readable program code embodied in said medium for:
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a. applying said transformation function to at least one of said reference signals, or portion thereof, to yield at least one set of coefficients;
b. applying said statistical function to said at least one set of coefficients derived from said at least one reference signal, or portion thereof, to yield at least one reference data set;
c. applying said transformation function and said statistical function to said observed signal to produce an observed data set;
d. comparing the observed data set to said at least one reference data set; and
e. computing a numerical value or values representative of said state of the CNS of said subject which results from said comparison.
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36. A computer system comprising the computer program of claim 35 and data processor.
Specification