Method and apparatus for determining the cerebral state of a patient with fast response

• US 20030055355A1
• Filed: 04/15/2002
• Published: 03/20/2003
• Est. Priority Date: 10/16/2000
• Status: Active Grant

• Current Assignee: Instrumentarium Dental Incorporated (Envista Holdings Corporation)
• Original Assignee: Instrumentarium Dental Incorporated (Envista Holdings Corporation)
• Inventors: Viertio-Oja, Hanna Elina
• Granted Patent: US 6,801,803 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 2505/05 Surgical care
• CPC Class Codes: A61B 5/1106 to assess neuromuscular blockade, e.g. to estimate depth of anaesthesia
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• 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 5/384 Recording apparatus or displays specially adapted therefor
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: G06F 2218/08 Feature extraction

Abstract

A method and apparatus for ascertaining the depth of anesthesia of the patient. In one embodiment, the entropy of the patient'"'"'s EEG signal data is determined as an indication of the depth of anesthesia. A frequency domain power spectrum quantity is obtained from the patient'"'"'s EMG signal data. The latter quantity can be updated more frequently than the EEG entropy due to its higher frequency. The EEG entropy indication and the EMG power spectrum indication can be combined into a composite indicator that provides an immediate indication of changes in the state of the patient. In a further embodiment, the frequency range over which the entropy of the biopotential signal from the patient is determined is selected to encompass both EEG signal data and EMG signal data and the entropy so determined used as an indication of the patients state. In addition, entropy of the EEG range alone can be used together with the entropy of the broadened range to indicate separately contributions from EEG and EMG. In a more generalized version of the further embodiment, a set of frequency components obtained from selected frequency ranges is utilized in determining entropy. The patient signal data from which the frequency components are extracted are obtained from time windows of differing lengths so that the resulting entropy indication will have optimally fast response times.

Claims

1. A method for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said method comprising the steps of:

(a) obtaining EEG signal data from the patient;

(b) obtaining EMG signal data from the patient;

(c) analyzing a sample of sequential EEG signal data to obtain a first indication indicative of the cerebral state of the patient;

(d) analyzing a sample of sequential EMG signal data temporally related to the EEG signal data sample to obtain a second indication indicative of electromyographic activity in the patient; and

(e) producing a composite indication from the first and second indications obtained at steps (c) and (d) indicative of the cerebral state of the patient.

3. The method according to claim 1 or claim 2 wherein step (c) is further defined as obtaining a measure of the complexity of the EEG signal data as the first indication.

4. The method according to claim 3 wherein step (c) is further defined as obtaining a measure of the entropy of the EEG signal data as the first indication.

5. The method according to claim 4 wherein step (c) is further defined as obtaining the spectral entropy of the EEG signal data as the first indication.

6. The method according to claim 4 wherein step (c) is further defined as obtaining the approximate entropy of the EEG signal data as the first indication.

7. The method according to claim 3 wherein step (c) is further defined as obtaining a Lempel-Ziv complexity measure of the EEG signal data as the first indication.

8. A method according to claim 3 wherein step (c) is further defined as obtaining the first indication from fractal spectrum analysis.

9. The method according to claim 1 or claim 2 wherein step (c) is further defined as obtaining the first indication from higher order frequency domain analysis including the bispectrum or trispectrum.

10. The method according to claims 1, 2 or 3 wherein step (c) is further defined as obtaining the first indication from frequency domain power spectral analysis of the EEG signal data.

11. The method according to claim 1 or claim 2 wherein step (c) is further defined as obtaining the first indication from a combination of analytical quantities obtained from the EEG signal data.

12. The method according to claim 11 wherein step (c) is further defined as employing a bispectral index (BIS) of the EEG signal data as the first indication.

13. The method according to claim 1 or claim 2 wherein prior to analyzing the samples in steps (c) and (d) the EEG and EMG signal data is subjected to spectral decomposition.

14. The method according to claim 13 further defined as carrying out the spectral decomposition by means of a Fourier transform.

15. The method according to claim 13 further defined as carrying out the spectral decomposition by employing a set of basis functions other than a Fourier set of functions.

16. The method according to claim 15 further defined as carrying out the spectral decomposition by employing a set of basic functions corresponding to wavelet transformation.

17. The method according to claims 1, 2 or 3 wherein step (d) is further defined as obtaining the second indication from a frequency domain power spectrum of the EMG signal data.

18. The method according to claim 3 wherein step (d) is further defined as obtaining a measure of the complexity of the EMG signal data as the second indication.

19. The method according to claim 1 or claim 2 further defined as repeating steps (c), (d), and (e) to update the composite indication.

20. The method according to claims 1, 2, 3, 4, or 5 further defined as one for ascertaining the hypnotic state of a patient.

21. The method according to claim 17 further defined as one for ascertaining the hypnotic state of a patient.

22. The method according to claim 20 further defined as repeating steps (c), (d), and (e) to update the composite indication.

23. The method according to claim 17 further defined as repeating steps (c), (d), and (e) to update the composite indication.

47. The method according to claim 1 or claim 2 wherein step (a) is further defined as obtaining the EEG signal data in a frequency range of approximately 0.5-32 Hz.

48. The method according to claim 1 or claim 2 wherein step (b) is further defined as obtaining EMG signal data in a range of approximately 32-150 Hz.

49. The method according to claim 48 further defined as notch filtering the EMG signal data to remove power line frequency harmonics.

51. The method according to claim 1, 2, 24, or 25 wherein the EEG signal data and the EMG signal data are obtained from a common signal source.

52. The method according to claim 51 wherein the EEG and EMG signal data are obtained from biopotential electrodes applied to the head of the patient.

53. The method according to claim 52 wherein at least the EMG signal data is obtained from electrodes applied to the forehead of the patient.

54. The method according to claim 1 or claim 2 further defined as processing the EEG and EMG signal data to detect artifacts.

56. The method according to claim 54 further defined as filtering the signal data to remove artifacts.

58. The method according to claim 54 further defined as preventing the use of signal data affected by artifacts.

60. The method according to claim 54 further defined as including the step of sensing the presence of electrical energy at electro surgical frequencies and as preventing the use of signal data affected by such an artifact.

62. The method according to claim 54 further defmed as detecting eye movement artifacts and preventing the use of signal data affected by such artifacts.

117. The apparatus according to claim 116 wherein said analyzing means is further defined as obtaining the spectral entropy of the EEG signal data as the indication.

2. A method for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said method rapidly indicating changes in such state and comprising the steps of:

(a) obtaining EEG signal data from the patient;

(b) obtaining EMG signal data from the patient, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of lower frequency;

(c) analyzing a sample of sequential EEG signal data to obtain a first indication indicative of the cerebral state of the patient, the length of a EEG signal data sample being such as to provide a cerebral state indication of desired accuracy;

(d) analyzing a sample of sequential EMG signal data temporally related to the EEG signal data sample to obtain a second indication indicative of electromyographic activity in the patient, it being possible to use a EMG signal data sample of shorter length than that of the EEG signal data sample due to the higher frequency of the EMG signal data; and

(e) producing a composite indication of the cerebral state of the patient from the first and second indications obtained at steps (c) and (d) indicative of the cerebral state of the patient, which composite indication can be updated at a repetition rate determined by the shorter sample length of the EMG signal data to rapidly indicate changes in the cerebral state of the patient.

24. A method for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said method comprising the steps of:

(a) obtaining biopotential signals from the patient, the biopotential signals containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of a lower frequency;

(b) analyzing a sample of sequential signal data over a frequency range that is sufficiently wide to include both the EEG and EMG signal data to obtain a measure of the complexity of the signal data; and

(c) providing the measure as an indication of the cerebral state of the patient.

26. The method according to claim 24 or claim 25 wherein step (b) is further defined as analyzing the biopotential signals over a frequency range extending from a frequency of about 0.5 Hz to a frequency which is above 32 Hz.

27. The method according to claim 24 or claim 25 wherein step (b) is further defined as obtaining a measure of the entropy of the signal data.

28. The method according to claim 26 wherein step (b) is further defined as obtaining a measure of the entropy of the signal data.

29. The method according to claim 27 wherein step (b) is further defined as obtaining the spectral entropy of the signal data.

30. The method according to claim 27 wherein step (b) is further defined as obtaining the approximate entropy of the signal data.

31. The method according to claim 24 or claim 25 wherein step (b) is further defined as obtaining a Lempel-Ziv complexity measure of the signal data.

32. A method according to claim 24 or claim 25 wherein step (b) is further defined as obtaining the complexity measure from fractal spectrum analysis.

33. The method according to claim 24 or claim 25 wherein prior to analyzing the sample in step (b) the biopotential signal is subjected to spectral decomposition.

34. The method according to claim 33 further defined as carrying out the spectral decomposition by means of a Fourier transform.

35. The method according to claim 33 further defined as carrying out the spectral decomposition by employing a set of basis functions other than a Fourier set of functions.

36. The method according to claim 35 further defined as carrying out the spectral decomposition by employing a set of basic functions corresponding to wavelet transformation.

37. The method according to claim 24 or claim 25 further defined as repeating steps (c), (d), and (e) to update the indication.

38. The method according to claim 24 or claim 25 further defined as one for ascertaining the hypnotic state of a patient.

39. The method according to claim 26 further defined as one for ascertaining the hypnotic state of a patient.

40. The method according to claim 27 further defined as one for ascertaining the hypnotic state of a patient.

41. The method according to claim 29 further defined as one for ascertaining the hypnotic state of a patient.

42. The method according to claim 38 further defined as repeating steps (c), (d), and (e) to update the composite indication.

43. The method according to claim 24 or claim 25 further defined as including the steps of analyzing a sample of the EEG signal data to obtain a complexity measure of the EEG signal data and providing the complexity measure of the EEG signal data as a further indication of the cerebral state of the patient.

44. The method according to claim 43 further defined as normalizing the further indication obtained from the analysis of the EEG signal data and the EEG-EMG signal data indication so that the further indication and EEG-EMG indication are equal in the absence of EMG signal data.

45. The method according to claim 44 wherein the normalizing is carried out by multiplying the complexity measure of the EEG signal data by a quantity comprising the logarithm of the number of frequency components used for computations for the EEG signal data complexity measure divided by the logarithm of the number of frequency components used for the computations for the combined EEG-EMG complexity measure.

46. The method according to claim 44 further defined as applying a constant to the normalized indications to maintain the normalization.

50. The method according to claim 24 or claim 25 further defmed as notch filtering to remove power line frequency harmonics.

55. The method according to claim 24 or 25 further defined as processing the biopotential signals or signal data to detect artifacts.

57. The method according to claim 55 further defined as filtering the signal data or biopotential signals to remove artifacts.

59. The method according to claim 55 further defined as preventing the use of signal data or biopotential signals affected by artifacts.

61. The method according to claim 55 further defined as including the step of sensing the presence of electrical energy at electro surgical frequencies and as preventing the use of biopotential signals or signal data affected by such an artifact.

63. The method according to claim 55 further defined as detecting eye movement artifacts and preventing the use of biopotential signals or signal data affected by such artifacts.

25. A method for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said method rapidly indicating changes in such state and comprising the steps of:

(a) obtaining biopotential signals from the patient, the biopotential signals containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of lower frequency;

(b) analyzing a sample of sequential signal data over a frequency range that is sufficiently wide to include both the EEG and EMG signal data to obtain a measure of the complexity of the signal data, it being possible to use a EMG signal data sample of shorter length than that of the EEG signal data sample due to the higher frequency of the EMG signal data; and

(c) providing the measure as an indication of the cerebral state of the patient, which indication can be updated at a repetition rate determined by the shorter sample length of the EMG signal data to rapidly indicate changes in the cerebral state of the patient.

64. A method for ascertaining the depth of anesthesia of a patient, said method rapidly indicating changes in the hypnotic state of the patient and comprising the steps of:

(a) obtaining biopotential signals from the patient, the biopotential signals containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of lower frequency;

(b) analyzing a sample of sequential signal data existing in a frequency range extending from a frequency of about 0.5 Hz to a frequency which is above 32 Hz to obtain a measure of the complexity of the signal data, it being possible to use a EMG signal data sample of shorter length than that of the EEG signal data sample due to the higher frequency of the EMG signal data;

(c) providing the complexity measure as a first indication of the cerebral state of the patient, which indication can be updated at a repetition rate determined by the shorter sample length of the EMG signal data to rapidly indicate changes in the cerebral state of the patient;

(d) analyzing a sample of EEG signal data to obtain a complexity measure of the EEG signal data;

(e) providing the complexity measure of the EEG signal data as a second indication of the cerebral state of the patient; and

(f) normalizing the first indication and second indication so that the first indication and second indication are equal in the absence of EMG signal data.

65. A method for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said method comprising the steps of:

(a) obtaining biopotential signal data from the patient, the biopotential signal data containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of a lower frequency;

(b) collecting the biopotential signal data over a plurality of time periods of differing lengths;

(c) subjecting the biopotential signal data of each of the time periods to spectral decomposition to obtain the frequency components of the collected biopotential signal data;

(d) selecting frequency components in a selected frequency band from the decomposed biopotential signal data for each of the time periods;

(e) analyzing the frequency components of a desired number of the frequency bands to obtain a measure of the complexity of the frequency components over a frequency range formed from the selected frequency bands; and

(f) providing the measure so obtained as an indication of the cerebral state of the patient.

66. The method according to claim 65 further defined as establishing the lengths of the time periods dependent on the frequency band of the frequency components selected in step (d).

67. The method according to claim 66 further defined as establishing the lengths of the time periods to provide a desired minimum number of cycles of biopotential signal data frequency components in the frequency band selected in step (d).

68. The method according to claim 65 further including the step of establishing the lengths of the time periods to produce optimally fast response time in ascertaining the cerebral state of the patient.

69. The method according to claim 65 further defined as utilizing the frequency components of at least two frequency bands.

70. The method according to claim 69 further defined as establishing the frequency bands in accordance with the magnitude of at least one of the EEG and EMG signal data.

71. The method according to claim 65 wherein the frequency bands are selected to include at least one frequency band containing primarily EMG signal data.

72. The method according to claim 65 wherein the frequency bands are selected to include at least one frequency band containing primarily EEG signal data.

73. The method according to claim 72 wherein the frequency bands are selected to include a plurality of frequency bands containing primarily EEG signal data.

74. The method according to claim 65 wherein step (e) is further defined as analyzing frequency components for frequency bands including EEG and EMG signal data.

75. The method according to claim 74 further including the steps of additionally analyzing frequency components for a frequency band or frequency bands including primarily EEG signal data and providing the complexity measure so obtained as a further indication of the cerebral state of the patient.

76. The method according to claim 65 wherein step (d) is further defined as selecting frequency components in two desired frequency bands for the biopotential signal data for at least one of the time periods;

step (e) is further defined as analyzing the frequency components in the two frequency bands to obtain the complexity measure; and

the method includes the step of determining the difference between the two complexity measures and providing the difference for use in the indication of the cerebral state of the patient.

77. The method according to claim 65 further defined as one for ascertaining the hypnotic state of the patient.

78. The method according to claim 65 wherein step (d) is further defined as obtaining a measure of the entropy of the EEG signal data as the indication.

79. The method according to claim 78 wherein step (d) is further defined as obtaining the spectral entropy of the EEG signal data as the indication.

80. The method according to claim 78 wherein step (d) is further defmed as obtaining the approximate entropy of the EEG signal data as the indication.

81. The method according to claim 65 wherein step (d) is further defined as obtaining a Lempel-Ziv complexity measure of the EEG signal data as the first indication.

82. A method according to claim 65 wherein step (d) is further defined as obtaining the measure of complexity from fractal spectrum analysis.

83. The method according to claim 65 further defined as carrying out the spectral decomposition by means of a Fourier transform.

84. The method according to claim 65 further defined as repeating steps (e) and (f) to update the indication.

85. The method according to claim 84 further defined as one for ascertaining the hypnotic state of a patient.

86. A method for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said method comprising the steps of:

(a) obtaining biopotential signal data from the patient, the biopotential signal data containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of a lower frequency;

(b) collecting the biopotential signal data over a plurality of time periods of differing lengths;

(c) subjecting the biopotential signal data of each of the time periods to decomposition to a set of basis function;

(d) selecting spectral components in a desired class of basis functions for each of the time periods;

(e) analyzing the spectral components of a desired number of the classes of basis functions to obtain a complexity measure of the spectral components from the desired number of classes of basis functions; and

(f) providing the measure so obtained as an indication of the cerebral state of the patient.

87. The method according to claim 86 wherein step (d) is further defined as selecting spectral components in two desired classes of basis function for the biopotential signal data for at least one of the time periods;

step (e) is further defined as analyzing the spectral components in the two classes to obtain the complexity measures; and

the method includes the step of determining the difference between the two complexity measures and providing the difference for use in the indication of the cerebral state of the patient.

88. Apparatus for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said apparatus comprising:

(a) means for obtaining biopotential signals from the patient, the biopotential signals containing EEG signal data and EMG signal data;

(b) means analyzing a sample of sequential EEG signal data to obtain a first indicator indicative of the cerebral state of the patient and analyzing a sample of sequential EMG signal data temporally related to the EEG signal data sample to obtain a second indicator indicative of electromyographic activity in the patient; and

(c) means producing a composite indicator from the first and second indicators indicative of the cerebral state of the patient.

89. The apparatus according to claim 88 wherein said analyzing means is further defined as using a length of an EEG signal data sample to determine the first indicator and using an EMG signal data sample of shorter length than the EEG signal data sample to determine said second indicator, said analyzing means completely updating said second indicator more frequently than said first indicator.

90. The apparatus according to claim 88 or claim 89 wherein said analyzing means is further defined as obtaining a measure of the complexity of the EEG signal data as the first indicator.

91. The apparatus according to claim 90 wherein said analyzing means is further defined as obtaining a measure of the entropy of the EEG signal data as the first indicator.

92. The apparatus according to claim 90 wherein said analyzing means is further defined as obtaining a Lempel-Ziv complexity measure of the EEG signal data as the first indicator.

93. The apparatus according to claims 88, 89, or 90 wherein said analyzing means is further defined as obtaining the second indication from a frequency domain power spectrum of the EMG signal data.

94. The apparatus according to claims 88, 89, 90, or 91 further defined as one for ascertaining the hypnotic state of a patient.

104. The apparatus according to claim 88 or claim 95 further defined as including means notch filtering the signal data to remove power line frequency harmonics.

105. The apparatus according to claim 88 or claim 95 further defmed as including means for processing the signal data to detect artifacts.

106. The apparatus according to claim 105 further defined as including means for filtering the signal data to remove artifacts.

95. Apparatus for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said apparatus comprising:

(a) means for obtaining biopotential signals from the patient, the biopotential signals containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of a lower frequency;

(b) means for analyzing a sample of sequential signal data over a frequency range that is sufficiently wide to include both the EEG and EMG signal data to obtain a measure of the complexity of the signal data; and

(c) means providing the complexity measure as an indicator of the cerebral state of the patient.

96. The apparatus according to claim 95 wherein said analyzing means is further defined as using a length of EEG signal data and using a EMG signal data sample of shorter length than the EEG signal data sample, and wherein said means updates said indicator a repetition rate determined by the shorter sample length of the EMG signal data to indicate changes in the cerebral state of the patient.

97. The apparatus according to claim 95 or claim 96 wherein said analyzing means is further defined as analyzing the signal data over a frequency range extending from a frequency of about 0.5 Hz to a frequency which is above 32 Hz.

98. The apparatus according to claim 95 or claim 96 wherein said analyzing means is further defined as obtaining a measure of the entropy of the signal data.

99. The apparatus according to claim 97 wherein said analyzing means is further defined as obtaining a measure of the entropy of the signal data.

100. The apparatus according to claim 95 or claim 96 wherein said analyzing means is further defined as obtaining a Lempel-Ziv complexity measure of the signal data.

101. The apparatus according to claim 95 or claim 96 further defined as one for ascertaining the hypnotic state of a patient.

102. The apparatus according to claim 95 or claim 96 wherein said analyzing means further defined as analyzing a sample of the EEG signal data to obtain a complexity measure of the EEG signal data and said providing means is further defined as providing the complexity measure of the EEG signal data as further indicator of the cerebral state of the patient.

103. The apparatus according to claim 102 further including means for normalizing the further indication obtained from the analysis of the EEG signal data and the EEG-EMG signal data indication so that the further indication and EEG-EMG indication are equal in the absence of EMG signal data.

107. Apparatus for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said apparatus comprising:

(a) means for obtaining biopotential signal data from the patient, the biopotential signal data containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of a lower frequency;

(b) means for recording the biopotential signal data over a plurality of time periods of differing lengths;

(c) means for spectrally decomposing the biopotential signal data of each of the time periods to obtain the frequency components of the collected biopotential signal data;

(d) means for selecting frequency components in a selected frequency band from the decomposed biopotential signal data for each of the time periods;

(e) means analyzing the frequency components of a desired number of the frequency bands to obtain a measure of the complexity of the frequency components over a frequency range formed from the selected frequency bands; and

(f) means providing the measure so obtained as an indication of the cerebral state of the patient.

108. The apparatus according to claim 107 wherein said recording means is further defined as establishing the lengths of the time periods dependent on the frequency band of the selected frequency components.

109. The apparatus according to claim 107 wherein said analyzing means is further defined as utilizing the frequency components of at least two frequency bands.

110. The apparatus according to claim 107 wherein said selecting means selects frequency bands including at least one frequency band containing primarily EMG signal data.

111. The apparatus according to claim 107 wherein said selecting means selects frequency bands including at least one frequency band containing primarily EEG signal data.

112. The apparatus according to claim 111 wherein said selecting means selects frequency bands including a plurality of frequency bands containing primarily EEG signal data.

113. The apparatus according to claim 107 further including means for additionally analyzing frequency components for a frequency band or frequency bands including primarily EEG signal data and means providing the complexity measure so obtained as a further indication of the cerebral state of the patient.

114. The apparatus according to claim 107 further including means for selecting frequency components in two desired frequency bands for the biopotential signal data for at least one of the time periods;

said analyzing means is further defined as analyzing the frequency components in the two frequency bands to obtain the complexity measure;

said apparatus includes means for determining the difference between the two complexity measures and said providing means provides the difference for use in the indication of the cerebral state of the patient.

115. The apparatus according to claim 107 further defined as one for ascertaining the hypnotic state of the patient.

116. The apparatus according to claim 107 wherein said analyzing means is further defined as obtaining a measure of the entropy of the EEG signal data as the indication.

118. The apparatus according to claim 116 wherein said analyzing means is further defined as obtaining the approximate entropy of the EEG signal data as the indication.

119. The apparatus according to claim 107 wherein said analyzing means is further defined as obtaining a Lempel-Ziv complexity measure of the EEG signal data as the indication.

120. A apparatus according to claim 107 wherein said analyzing means is further defined as obtaining the measure of complexity from fractal spectrum analysis.

121. The apparatus according to claim 107 wherein said spectral decomposition means is further defined as carrying out the spectral decomposition by means of a Fourier transform.

122. Apparatus for ascertaining the cerebral state of a patient, including a state resulting from the administration of a drug, said apparatus comprising:

(a) means for obtaining biopotential signal data from the patient, the biopotential signal data containing EEG signal data and EMG signal data, the EMG signal data being primarily of a higher frequency than the EEG signal data which is primarily of a lower frequency;

(b) means for recording the biopotential signal data over a plurality of time periods of differing length;

(c) means for subjecting the biopotential signal data of each of the time periods to decomposition to a set of basis function;

(d) means for selecting spectral components in a desired class of basis functions for each of the time periods;

(e) means analyzing the spectral components of a desired number of the classes of basis functions to obtain a complexity measure of the spectral components from the desired number of classes of basis function; and

(f) means for providing the measure so obtained as an indication of the cerebral state of the patient.

123. The apparatus according to claim 122 including means for further selecting spectral components in two desired classes of basis functions for the biopotential signal data for at least one of the time periods;

said analyzing means is further defined as analyzing the spectral components in the two classes to obtain the complexity measures;

said apparatus includes means for determining the difference between the two complexity measures and said providing means provides the difference for use in the indication of the cerebral state of the patient.