Method and apparatus for the estimation of anesthesia depth

• US 7,603,168 B2
• Filed: 04/17/2008
• Issued: 10/13/2009
• Est. Priority Date: 07/12/2002
• Status: Expired due to Fees

• Current Assignee: Bionova Technologies Incorporated
• Original Assignee: Bionova Technologies Incorporated
• Inventors: Huzmezan, Mihai, Bibian, Stéphane, Dumont, Guy Albert, Ries, Craig Robert, MacLeod, Bernard Ansell, Ahmadi, Hosaain Cyrus, Zikov, Tatjana, Puil, Ernest
• Primary Examiner(s): Mallari; Patricia
• Assistant Examiner(s): Toth; Karen E
• Application Number:
  US12/148,151
• Publication Number:
  US 20080294063A1
• Time in Patent Office: 544 Days
• Field of Search: 600/508, 600/509, 600515-519, 600/521, 600/544, 600/545
• US Class Current: 600/509
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/7217 of noise originating from a therapeutic or surgical apparatus, e.g. from a pacemaker
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device

Abstract

A method and apparatus to monitor and control the anesthesia state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the anesthesia state of a patient undergoing general anesthesia involve a significant time delay between the patient'"'"'s true anesthesia state and the computed indices. The present invention reduces this time delay by using a novel analysis technique applied to spontaneous electrophysiological activity. A transformation and statistical analysis of the observed electrophysiological activity is conducted and compared to reference data to provide numerical indicators. In addition, these indicators are consistent with the levels of depression of patients CNS and ANS states. This method is illustrated in detail by CNS monitoring of electroencephalogram signals.

Claims

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 with a processor, said method comprising:

a) acquiring at least two reference signals, said at least two reference signals corresponding to two distinct ANS states obtained from a 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 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 with the processor 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.

2. The method of claim 1 wherein said observed electrophysiological signal and reference signals representing measured cardiovascular activity of the subject.

3. The method of claim 2 wherein said observed electrophysiological signal and reference signals representing measured cardiovascular activity of a subject comprises one or more electrocardiograms.

4. The method of claim 1 wherein said reference subject or subjects are not the same individual as said subject.

5. The method of claim 1 wherein said statistical function is selected from the group histogram, probability density function, standard deviation, or variance.

6. The method of claim 1 used to measure electrophysiological activity in said subject to ascertain the level of analgesia of said subject.

7. The method of claim 1 used to measure electrophysiological activity in said subject to ascertain the effects of anesthesia and analgesia agents on the ANS of said subject.

8. The method of claim 1 used to measure electrophysiological activity in said subject to ascertain titration and dosage profiles of anesthesia and analgesia compounds end medicaments.

9. The method of claim 1 wherein said transformation function is a wavelet transform.

10. The method of claim 1 wherein said transformation function is any transform with joint time and frequency localization properties.

21. The method of claim 1 for 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 ANS of said subject.

11. A method for extracting information from an observed electrophysiological signal from a subject in order to evaluate the level of depression of the autonomic nervous system (ANS) of said subject with a processor, said method comprising:

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 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 with the processor 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.

12. 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 with a processor, said method comprising:

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 date 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 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 with the processor 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.

13. The method of claim 12 wherein said observed electrophysiological signals and reference signals represent electroencephalograms and electrocardiograms.

14. The method of claim 12 wherein said first and second observed electrophysiological signals are the same signal.

15. The method of claim 12 used to measure electrophysiological activity in said subject to ascertain the levels of consciousness and analgesia, respectively, of said subject.

16. The method of claim 12 used to measure electrophysiological activity in said subject to ascertain the effects of anesthesia and analgesia agents on the CNS and ANS, respectively, of said subject.

17. The method of claim 12 used to measure electrophysiological activity in said subject to ascertain titration and dosage profiles of anaesthesia and analgesia compounds and medicaments.

18. The method of claim 12 wherein said first and second transformation functions are any transform with joint time and frequency localization properties.

22. The method of claim 12 for controlling automatically or manually the administration of anesthesia drugs based on the said computed numerical value or values representative of said levels of depression of the CNS and ANS of said subject.

23. The method of claim 12 for controlling automatically or manually the levels of consciousness and analgesia of said subject based on the said computed numerical value or values representative of said levels of depression of the CNS and ANS of said subject.

19. A method for administrating anesthesia drugs to a subject in order to control the level of depression of the central nervous system (CNS) of said subject with a processor, said method comprising:

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 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 with the processor 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 with the processor 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.

20. The method of claim 19 wherein said observed and reference CNS signals representing measured brain activity of a subject am electroencephalograms.