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 electrophysiological signal from a subject in order to evaluate the level of depression of the ANS (Autonomous Nervous System) of said subject, said method comprising:
- a) acquiring at least two reference signals, said at least two reference signals corresponding to two distinct ANS states obtained from one or more reference subject or subjects;
b) selecting a transformation function which, when applied to one of said at least two reference signals, yields a set of coefficients;
c) selecting a statistical function which, when applied to said set of coefficients derived from one of said at least two reference signals, or a subset of said set of coefficients, yields a reference data set which characterizes the distinct ANS state corresponding to said one of at least two reference signals;
d) applying said transformation and statistical function to said at least two reference signals to produce reference data sets which distinguish between the distinct ANS state(s) corresponding to said reference signals;
e) observing the electrophysiological activity of said subject to produce said observed electrophysiological signal;
f) applying said transformation and statistical function to said observed electrophysiological 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 level of depression of the ANS of said subject which results from said comparison;
wherein said at least two reference signals correspond to distinct ANS states which are two extreme states.
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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 patients 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
49 Claims
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1. A method for extracting information from an observed electrophysiological signal from a subject in order to evaluate the level of depression of the ANS (Autonomous Nervous System) of said subject, said method comprising:
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a) acquiring at least two reference signals, said at least two reference signals corresponding to two distinct ANS states obtained from one or more reference subject or subjects; b) selecting a transformation function which, when applied to one of said at least two reference signals, yields a set of coefficients; c) selecting a statistical function which, when applied to said set of coefficients derived from one of said at least two reference signals, or a subset of said set of coefficients, yields a reference data set which characterizes the distinct ANS state corresponding to said one of at least two reference signals; d) applying said transformation and statistical function to said at least two reference signals to produce reference data sets which distinguish between the distinct ANS state(s) corresponding to said reference signals; e) observing the electrophysiological activity of said subject to produce said observed electrophysiological signal; f) applying said transformation and statistical function to said observed electrophysiological 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 level of depression of the ANS of said subject which results from said comparison; wherein said at least two reference signals correspond to distinct ANS states which are two extreme states. - View Dependent Claims (2, 3, 4, 5, 7, 9, 12, 19, 20, 47)
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37. A method for extracting information from an observed electrophysiological signal from a subject in order to evaluate the level of depression of the ANS of said subject, said method comprising:
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a) acquiring at least one reference signal, said at least one reference signal corresponding to a distinct ANS state obtained from one or more reference subject or subjects; b) selecting a transformation function which, when applied to said at least one reference signal, yields a set of coefficients; c) selecting a statistical function which, when applied to said set of coefficients derived from said at least one reference signal, or a subset of said set of coefficients, yields a reference data set which characterizes the distinct ANS state corresponding to said at least one reference signal; d) applying said transformation and statistical function to said at least one reference signal to produce one or more reference data sets which distinguish the distinct ANS state(s) corresponding to each reference signal; e) observing the electrophysiological activity of said subject to produce said observed electrophysiological signal; f) applying said transformation and statistical function to said observed electrophysiological signal to produce an observed data set; g) comparing the observed data set to one or more said reference data sets; h) computing a numerical value or values representative of said level of depression of the ANS of said subject which results from said comparison; wherein said comparison is done by computing the difference between said observed and reference data sets using a vector p-norm.
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38. A method for extracting information from two observed electrophysiological signals from a subject, in order to evaluate the levels of depression of the CNS (Central Nervous System) and ANS (Autonomous Nervous System) of said subject, said method comprising:
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a) acquiring at least two CNS reference signals, said at least two CNS reference signals corresponding to two distinct CNS states obtained from one or more reference subject or subjects; b) acquiring at least two ANS reference signals, said at least two reference signals corresponding to two distinct ANS states obtained from one or more reference subject or subjects; c) selecting a first transformation function which, when applied to one of said at least two CNS reference signals, yields a first set of coefficients; d) selecting a second transformation function which, when applied to one of said at least two ANS reference signals, yields a second set of coefficients; e) selecting a first statistical function which, when applied to said first set of coefficients derived from one of said at least two CNS reference signals, or a subset of said set of coefficients, yields a first reference data set which characterizes the distinct CNS state corresponding to said one of at least two CNS reference signals; f) selecting a second statistical function which, when applied to said second set of coefficients derived from one of said at least two ANS reference signals, or a subset of said set of coefficients, yields a second reference data set which characterizes the distinct ANS state corresponding to said one of at least two ANS reference signals; g) applying said first transformation and first statistical function to said at least two CNS reference signals to produce reference data sets which distinguish between the distinct CNS states corresponding to said CNS reference signals; h) applying said second transformation and second statistical function to said at least two ANS reference signals to produce reference data sets which distinguish between the distinct ANS states corresponding to said ANS reference signals; i) observing the CNS electrophysiological activity of said subject to produce a first observed electrophysiological signal; j) observing the ANS electrophysiological activity of said subject to produce a second observed electrophysiological signal; k) applying said first transformation and first statistical function to said first observed electrophysiological signal to produce the CNS observed data set; l) applying said second transformation and second statistical function to said second observed electrophysiological signal to produce the ANS observed data set; m) comparing the CNS and ANS observed data sets to said CNS and ANS reference data sets, respectively; and n) computing a numerical value or values representative of said levels of depression of the CNS and ANS of said subject, respectively, which results from said comparison wherein said at least two CNS reference signals correspond to distinct CNS states which are two extreme states, and wherein said at least two ANS reference signals correspond to distinct ANS states which are two extreme states. - View Dependent Claims (39, 40, 41, 42, 43, 44, 48, 49)
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45. A method for administrating anesthesia drugs to a subject in order to control the level of depression of the CNS of said subject, said method comprising:
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a) acquiring at least two reference signals, said at least two reference signals corresponding to two distinct CNS states obtained from one or more reference subject or subjects; b) selecting a transformation function which, when applied to one of said at least two reference signals, yields a set of coefficients; c) selecting a statistical function which, when applied to said set of coefficients derived from one of said at least two reference signals, or a subset of said set of coefficients, yields a reference data set which characterizes the distinct CNS state corresponding to said one of at least two reference signals; d) applying said transformation and statistical function to said at least two reference signals to produce 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 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 said reference data sets; h) computing a numerical value or values representative of said level of depression of the CNS of said subject which results from said comparison i) controlling automatically or manually the administration of anesthesia drugs based on the said computed numerical value or values representative of said level of depression of the CNS of said subject wherein said at least two reference signals correspond to distinct CNS states which are two extreme states. - View Dependent Claims (46)
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Specification