Monitoring of the cerebral state of a subject

• US 20070010755A1
• Filed: 07/07/2005
• Published: 01/11/2007
• Est. Priority Date: 07/07/2005
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Salmi, Elina, Viertio-Oja, Hanna, Jaaskelainen, Satu, Sarkela, Mika, Scheinin, Harry, Maksimow, Anu, Langsjo, Jaako
• Granted Patent: US 7,783,343 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves

Abstract

The invention relates to a method and apparatus for monitoring the cerebral state of a subject. At least one of a first and second parameters is derived from the physiological signal data obtained from the subject, wherein the first parameter is indicative of the sum of spectral values of the physiological signal data in a first frequency band lying above high frequency EEG activity and the second parameter is indicative of the relative magnitudes of a first sum of k-th order spectral values and a second sum of n-th order spectral values. The second sum is calculated over a frequency band lying above high frequency EEG activity (k>2 and n>1). A state index is then formed, which is dependent on the at least one parameter and indicative of the cerebral state of the subject. The first and second parameters may be used in the Bispectral Index (BIS™) algorithm.

Claims

1. A method for monitoring the cerebral state of a subject, the method comprising the steps of:

obtaining physiological signal data from a subject, the physiological signal data including EEG signal components;

deriving at least one of a first and second parameters from the physiological signal data, wherein the first parameter is indicative of the sum of spectral values of the physiological signal data in a first frequency band lying above high frequency EEG activity and the second parameter is indicative of the relative magnitudes of a first sum of k-th order spectral values and a second sum of n-th order spectral values, wherein the second sum is calculated over a second frequency band lying above high frequency EEG activity, and wherein k is an integer greater than two and n is an integer greater than one; and

forming a state index which is dependent on the at least one parameter derived in the deriving step, the state index being indicative of the cerebral state of the subject.

2. A method according to claim 1, wherein

the deriving step includes deriving the first parameter only, and

the forming step includes deriving the state index, in which the state index has a predetermined relationship with the first parameter.

3. A method according to claim 1, wherein

the deriving step includes deriving the second parameter only, and

the forming step includes deriving the state index, in which the state index has a predetermined relationship with the second parameter.

4. A method according to claim 1, wherein

the deriving step includes deriving both the first and second parameters;

the method further comprises a step of deriving a burst suppression index indicative of burst suppression in the physiological signal data; and

the forming step includes forming a weighted sum of the first parameter, the second parameter, and the burst suppression index.

5. A method according to claim 1, wherein the deriving step includes deriving the first parameter, in which the first parameter is indicative of the sum of power spectral values of the physiological signal data in the first frequency band.

6. A method according to claim 1, wherein the deriving step includes deriving the first parameter, in which the first parameter is indicative of the sum of bispectral values of the physiological signal data in the first frequency band.

7. A method according to claim 1, wherein the deriving step includes deriving the first parameter, in which the first parameter is indicative of the sum of k-th order spectral values of the physiological signal data in the first frequency band and k is an integer greater than three.

8. A method according to claim 1, wherein the deriving step includes deriving the second parameter, in which the k-th and n-th order spectral values are bispectral values.

9. A method according to claim 1, wherein the deriving step includes deriving the second parameter, in which the second parameter is indicative of the ratio of the first and second sums.

10. A method according to claim 9, wherein the deriving step includes deriving the second parameter, in which the second frequency band extends from a frequency of about 80 Hz to a frequency of about 95 Hz.

11. A method according to claim 10, wherein the deriving step includes deriving the second parameter, in which the first sum is calculated over a frequency band extending from a frequency of about 0.5 Hz to a frequency of about 95 Hz.

12. A method according to claim 9, wherein the deriving step further includes calculating the logarithm of the ratio.

13. A method according to claim 5, wherein the deriving step includes the sub-steps of:

calculating, based on the physiological signal data, a first power measure indicative of the sum of power spectral values in a third frequency band whose lower limit is greater than 70 Hz;

calculating, based on the physiological signal data, a second power measure indicative of the sum of power spectral values in a fourth frequency band containing substantially EEG signal components only; and

deriving the first parameter from the ratio of the first and second power measures.

14. A method according to claim 13, wherein the third frequency band extends from a frequency of about 105 Hz to a frequency of about 145 Hz.

15. A method according to claim 13, wherein the fourth frequency band extends from a frequency of about 11 Hz to a frequency of about 20 Hz.

16. A method according to claim 13, wherein the deriving step further includes calculating the logarithm of the ratio of the first and second power measures.

17. A method according to claim 1, further comprising a step of administering at least one drug to the subject, wherein the at least one drug belongs to a group of NMDA antagonists.

18. An apparatus for monitoring the cerebral state of a subject, the apparatus comprising:

means for receiving physiological signal data from a subject, the physiological signal data including EEG signal components;

first calculation means for deriving at least one of a first and second parameters from the physiological signal data, wherein the first parameter is indicative of the sum of spectral values of the physiological signal data in a first frequency band lying above high frequency EEG activity and the second parameter is indicative of the relative magnitudes of a first sum of k-th order spectral values and a second sum of n-th order spectral values, wherein the second sum is calculated over a second frequency band lying above high frequency EEG activity, and wherein k is an integer greater than two and n is an integer greater than one; and

second calculation means for forming a state index which is dependent on the at least one parameter, the state index being indicative of the cerebral state of the subject.

19. An apparatus according to claim 18, further comprising third calculation means for deriving, based on the physiological signal data, a burst suppression index indicative of burst suppression in the physiological signal data.

20. An apparatus according to claim 19, wherein the second calculation means are configured to form a weighted sum of the first parameter, the second parameter, and the burst suppression index.

21. An apparatus according to claim 19, wherein the first calculation means are configured to derive the first parameter based on power spectral values of the physiological signal in the first frequency band.

22. An apparatus according to claim 19, wherein the first calculation means are configured to derive the first and second sums based on bispectral values.

23. An apparatus according to claim 21, wherein the first calculation means are configured to calculate, based on the physiological signal data, a first power measure indicative of the power of the physiological signal data in a third frequency band whose lower limit is greater than 70 Hz, a second power measure indicative of the power of the physiological signal data in a fourth frequency band containing substantially EEG signal components only, and to derive the first parameter from the ratio of the first and second power measures.

24. An apparatus according to claim 23, wherein the third frequency band extends from a frequency of about 105 Hz to a frequency of about 145 Hz.

25. An apparatus according to claim 23, wherein the fourth frequency band extends from a frequency of about 11 Hz to a frequency of about 20 Hz.

26. An apparatus according to claim 18, wherein the first calculation means are configured to calculate the ratio of the first and second sums.

27. An apparatus according to claim 26, wherein the second frequency band extends from a frequency of about 80 Hz to a frequency of about 95 Hz.

28. An apparatus according to claim 27, wherein the first calculation means are configured to calculate the first sum over a frequency band extending from a frequency of about 0.5 Hz to a frequency of about 95 Hz.

29. An apparatus according to claim 23, wherein the first calculation means are configured to calculate a logarithm of the ratio of the first and second power measures.

30. An apparatus according to claim 26, wherein the first calculation means are further configured to calculate a logarithm of the ratio of the first and second sums.

31. An apparatus for monitoring the cerebral state of a subject, the apparatus comprising:

a first data processing unit configured to receive physiological signal data from a subject, the physiological signal data including EEG signal components;

a second data processing unit configured to derive at least one of a first and second parameters from the physiological signal data, wherein the first parameter is indicative of the sum of spectral values of the physiological signal data in a first frequency band lying above high frequency EEG activity and the second parameter is indicative of the relative magnitudes of a first sum of k-th order spectral values and a second sum of n-th order spectral values, wherein the second sum is calculated over a second frequency band lying above high frequency EEG activity, and wherein k is an integer greater than two and n is an integer greater than one; and

a third data processing unit configured to form a state index which is dependent on the at least one parameter, the state index being indicative of the cerebral state of the subject.

32. A computer program product for an apparatus monitoring a patient, the computer product comprising:

a first program code portion for receiving physiological signal data obtained from a subject, the physiological signal data including EEG signal components; and

a second program code portion configured to derive at least one of a first and second parameters from the physiological signal data, wherein the first parameter is indicative of the sum of spectral values of the physiological signal data in a first frequency band lying above high frequency EEG activity and the second parameter is indicative of the relative magnitudes of a first sum of k-th order spectral values and a second sum of n-th order spectral values, wherein the second sum is calculated over a second frequency band lying above high frequency EEG activity, and wherein k is an integer greater than two and n is an integer greater than one.

33. A computer program product according to claim 32, wherein the second program code portion is further configured to supply the at least one parameter to the apparatus.

34. A computer program product according to claim 32, further comprising a third program code portion configured to form a state index which is dependent on the at least one parameter, the state index being indicative of the cerebral state of the subject.