Cerebral biopotential analysis system and method

• US 5,010,891 A
• Filed: 04/12/1989
• Issued: 04/30/1991
• Est. Priority Date: 10/09/1987
• Status: Expired due to Term

• Current Assignee: Imperial Bank (Imperial Bancorp)
• Original Assignee: Aspect Medical Systems, Inc. (Medtronic Plc)
• Inventors: Chamoun, Nassib G.
• Primary Examiner(s): Jaworski, Francis
• Assistant Examiner(s): MANUEL, GEORGE C
• Application Number:
  US07/336,874
• Time in Patent Office: 748 Days
• Field of Search: 128/731, 128/732, 128/733, 364/413.02
• 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 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• 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 5/7257 using Fourier transforms
• CPC Class Codes: G06F 2218/08 Feature extraction

Abstract

Disclosed is a real time cerebral diagnostic apparatus and method for quantitatively evaluating, in a noninvasive manner, cerebral phenomena such as the depth and adequacy of anesthesia pain responses during surgical stress, acute cerebral ischemia, level of consciousness, degree of intoxication and ongoing normal and abnormal cognitive processes. A suitable electrode and amplifier system is used to obtain high resolution biopotentials from the regions of interest. Surface electroencephalographic (EEG) signals are filtered to allow the acquisition of frequencies between 2 and 500 Hz, then digitized and transmitted over a high speed serial line to a host computer where a 32 second long signal is divided into 128 consecutive 0.25 second intervals. Digital EEG data from unipolar leads is normalized and the dynamic phase and density relations within the signal are then characterized by estimating the third-order autocorrelation function or autobispectrum using either a frequency domain, or parametric approach. Paired EEG data from corresponding left and right hemisphere leads is used to characterize the dynamic phase and density relations between hemispheres by estimating the third order crosscorrelation function or crossbispectrum using either frequency domain or parametric techniques. Under certain specific filtering circumstances the power spectrum and crosspower spectrum are also computed. A reference clinical database is used to identify frequency pairs most sensitive to particular interventions or diagnostic states of interest. The values at these frequency pairs are then extracted from the patient'"'"'s autobicoherence, autobispectral density, autobiphase, crossbicoherence, crossbispectral density, and crossbiphase arrays. The ensemble of values for the particular diagnostic determination is used to compute an index which serves as the diagnostic criterion by which the patient'"'"'s state is judged. Any diagnostic index can be continuously displayed on a graphics terminal for real-time diagnostic monitoring or can be sent to a hard copy device to generate reports for the medical record.

Claims

1. A method of noninvasively detecting cerebral phenomena comprising the steps of:

acquiring electroencephalographic signals through at least one electrode from a body surface of a subject being analyzed;

filtering said electroencephalographic signals to obtain filtered signals having frequencies between 2 and 500 hertz;

dividing said filtered signals into a plurality of equally sized data records;

characterizing dynamic phase relations within said filtered signals by processing said filtered signals to generate bispectral values;

comparing said generated bispectral values to reference values to derive a diagnostic index that quantifies the detected cerebral phenomena.

2. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the step of acquiring electroencephalographic signals further comprises the step of attaching electrodes to a head of the subject being analyzed in order to obtain a unipolar electroencephalographic signal from each region of interest of both left and right hemispheres of the subject'"'"'s brain to which said electrodes are attached.

3. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said bispectral values generated in said step of characterizing said dynamic phase relations are autobispectral density values.

4. The method of noninvasively detecting cerebral phenomena of claim 3 where said step of generating autobispectral density values comprises the steps of:

computing fast Fourier transforms X_i (f) and P_Yi (f) of each of said data records i;

computing power spectra P_Xi (f) and P_Yi (f) of said data records i by squaring the magnitude of each element of said fast Fourier transforms X_i (f) and Y_i (f) respectively;

computing for at least one electrode an average complex triple product of data records acquired by said at least one electrode;

computing for said at least one electrode an average real triple product of data records acquired by said at least one electrode;

computing for said at least one electrode an autobispectral density value as the absolute value of said average complex triple product for said electrode.

5. The method of noninvasively detecting cerebral phenomena of claim 4 further comprising the step of computing an autobiphase value φ

(f₁,f₂) for at 1east one electrode such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2 (=tan.sup.-1[Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the average complex triple product for an electrode, and f₁ and f₂ designate limits of the frequency range over which the autobiphase computation is carried out.

6. The method of noninvasively detecting cerebral phenomena of claim 4 further comprising the step of computing an autobicoherence value R(f₁,f₂) for at least one electrode such that

7. The method of noninvasively detecting cerebral phenomena of claim 3 wherein said step of generating autobispectral density values comprises the steps of:

computing autocorrelation sequences R_2X (m) and R_2Y (m) of all data records acquired by at least one electrode;

determining the orders and coefficients of parametric models for power spectra of data records acquired by said at least one electrode;

computing power spectra P_X (f) and P_Y (f) of data records acquired by said at least one electrode;

computing third order moment sequences R_3X (τ

) and R_3Y (τ

) of data records acquired by said at least one electrode;

determining the orders and coefficients of parametric models of the bispectra of data records acquired by said at least one electrode;

computing for said at least one electrode a bispectrum of data records acquired by said at least one electrode.

8. The method of noninvasively detecting cerebral phenomena of claim 7 wherein said bispectrum is autobispectrum and further comprising the step of computing an autobispectral density value for at least one electrode as the absolute value of the bispectrum of all data records for said electrode.

9. The method of noninvasively detecting cerebral phenomena of claim 8 further comprising the steps of:

computing for at least one electrode a real triple product of data records acquired by said at least one electrode;

computing an autobicoherence value R(f₁,f₂) for said at least one electrode such that

space="preserve" listing-type="equation">R(f.sub.1,f.sub.2)=BD(f.sub.1,f.sub.2)/[BR(f.sub.1,f.sub.2)].sup.178

where BD(f₁,f₂) is the autobispectral density value for an electrode, BR(f₁,f₂) is the real triple product for the same electrode, and f₁ and f₂ designate limits of the frequency range over which bispectral computation is carried out.

10. The method of noninvasively detecting cerebral phenomena of claim 7 further comprising the step of computing an autobiphase value φ

(f₁,f₂) for said at 1east one electrode such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the bispectrum for an electrode, and f₁ and f₂ designate limits of the frequency range over which the bispectral computation is carried out.

11. The method of noninvasively detecting cerebral phenomena of claim 7 wherein said bispectrum is crossbispectrum and further comprising the step of computing a crossbispectral density value for each electrode pair as the absolute value of the crossbispectrum of all data records for each said electrode pair.

12. The method of noninvasively detecting cerebral phenomena of claim 11 further comprising the step of computing a crossbiphase value φ

(f₁,f₂) for each of said at least one electrode pair such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the crossbispectrum for an pair, and f₁ and f₂ designate limits of the frequency range over which the bispectral computation is carried out.

13. The method of noninvasively detecting cerebral phenomena of claim 12 further comprising the step of computing a crossbicoherence value R(f₁,f₂) for each of said at least one electrode pair such that

14. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said bispectral values generated in said step of characterizing said dynamic phase relations are autobicoherence values.

15. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said bispectral values generated in said step of characterizing said dynamic phase relations are autobiphase values.

16. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said step of acquiring electroencephalographic signals further comprises the step of attaching electrodes to the head of the subject being analyzed in order to obtain bipolar data sets of electroencephalographic signals from left and right hemispheres of the subject'"'"'s brain to which said electrodes are attached.

17. The method of noninvasively detecting cerebral phenomena of claim 16 wherein one bipolar data set is acquired from a frontal left hemisphere of the subject'"'"'s brain and another bipolar data set is acquired from a frontal right hemisphere of the subject'"'"'s brain.

18. The method of noninvasively detecting cerebral phenomena of claim 16 wherein one bipolar data set is acquired from a left occipital region of the subject'"'"'s brain and another bipolar data set is acquired from a right occipital region of the subject'"'"'s brain.

19. The method of noninvasively detecting cerebral phenomena of claim 16 wherein one bipolar data set is acquired from a left parietal region of the subject'"'"'s brain and another bipolar data set is acquired from a right parietal region of the subject'"'"'s brain.

20. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said bispectral values generated in said step of characterizing said dynamic phase relations are crossbispectral density values.

21. The method of noninvasively detecting cerebral phenomena of claim 20 where said step of generating said crossbispectral density values comprises the steps of:

computing fast Fourier transforms X_i (f) and Y_i (f) of said data records i;

computing power spectra P_Xi (f) and P_Yi (f) of said data records by squaring the magnitude of elements of said fast Fourier transforms X_i (f) and Y_i (f) respectively;

computing for at least one electrode pair an average complex triple product of all data records acquired by said at least one electrode pair;

computing for said at least one electrode pair an average real triple product of all data records acquired by each of said at least one electrode pair;

computing for said at least one electrode pair a crossbispectral density value as the absolute value of the average complex triple product for said electrode pair.

22. The method of noninvasively detecting cerebral phenomena of claim 21 further comprising the step of computing a crossbiphase value φ

(f₁,f₂) for said at least one electrode pair such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [lm(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the average complex triple product for an electrode pair, and f₁ and f₂ designate limits of the frequency range over which the crossbiphase computation is carried out.

23. The method of noninvasively detecting cerebral phenomena of claim 22 further comprising the step of computing a crossbicoherence value R(f₁,f₂) for said at least one electrode pair such that

24. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said bispectral values generated in said step of characterizing said dynamic phase relations ar crossbicoherence values.

25. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said bispectral values are generated in said step of characterizing said dynamic phase relations are crossbiphase values.

26. The method for noninvasively detecting cerebral phenomena of claim 1 wherein said step of acquiring electroencephalographic signals further comprises the step of analyzing said signals to determine lead failure.

27. The method of noninvasively detecting cerebral phenomena of claim 1 wherein said step of comparing further comprises the steps of:

organizing said generated bispectral values in at least one array of bispectral values;

selecting a physical phenomena to be diagnosed;

retrieving an appropriate bispectral reference array from a resident memory, said reference array containing frequency pairs that are most sensitive to the physical phenomena to be diagnosed;

adding data values in locations of each of said at least one array of bispectral values that are identified by the retrieved reference array as being locations containing data of significance to obtain a sum of said significant locations;

averaging the values stored in said significant locations to generate a diagnostic index relating to the cerebral phenomena to be detected.

28. The method of noninvasively detecting cerebral phenomena of claim 1 further comprising the steps of:

generating three arrays of bispectral data for each of three different states of the subject;

performing a paired Student'"'"'s t test comparing data in a first and a second array of said three arrays of bispectral data to produce a first t array and performing a paired Student'"'"'s t test comparing data in said second and a third array of said three arrays of bispectral data to produce a second t array;

comparing data values in said first t array with data values in corresponding locations in said second t array;

identifying those corresponding locations in said first and second t arrays that differ by more than a preselected amount, said identified locations representing those locations that are significant for detecting the cerebral phenomena.

29. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the cerebral phenomenon being detected is the depth of anesthesia in the subject being analyzed.

30. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the cerebral phenomena being detected are pain responses during surgical stress in the subject being analyzed.

31. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the cerebral phenomenon being detected is acute ischemia or infarction in the subject being analyzed.

32. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the cerebral phenomenon being detected is the level of consciousness of the subject being analyzed.

33. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the physical phenomenon being detected is the degree of cerebral intoxication of the subject being analyzed.

34. The method of noninvasively detecting physical phenomena of claim 1 wherein the physical phenomena being detected are normal or abnormal cognitive processes.

35. The method of noninvasively detecting cerebral phenomena of claim 1 further comprising the steps of:

generating three arrays of bispectral data for each of three different states of the subject;

performing statistical operations on said three arrays of bispectral data in order to identify those locations in said arrays that are significant for detecting the cerebral phenomena.

36. The system for noninvasively detecting cerebral phenomena of claim 1 further comprising:

means for generating three arrays of bispectral data for each of three different states of the subject;

means for statistically analyzing said arrays of bispectral data in order to identify those locations in said arrays that are significant for detecting the cerebral phenomena.

37. The method of noninvasively detecting cerebral phenomena of claim 1 wherein the cerebral phenomenon being detected is chronic ischemia or infraction in the subject being analyzed.

38. THe method of non-invasively detecting cerebral phenomena of claim 1 wherein said bispectral values are generated by computing the Fourier transform of the third order autocorrelation function of said filtered signals.

39. The method of non-invasively detecting cerebral phenomena of claim 1 wherein said bispectral values are generated by computing the Fourier transform of the third order crosscorrelation function of said filtered signals.

40. A system for noninvasively detecting cerebral phenomena comprising:

means for acquiring electroencephalographic signals through at least one electrode from a body surface of a subject being analyzed;

means for filtering said electroencephalographic signals to eliminate those signals having frequencies less than 2 hertz or frequencies greater than 500 hertz;

means for dividing said filtered signals into a plurality of equally sized data records;

means for generating bispectral values capable of characterizing dynamic phase relations within said filtered electroencephalographic signals;

means for comparing said generated bispectral values to reference values in order to derive a diagnostic index that quantifies the detected cerebral phenomena.

41. The system for noninvasively detecting cerebral phenomena of claim 40 further comprising a plurality of said means for acquiring electroencephalographic signals, each of said means for acquiring electroencephalographic signals being connected to said means for filtering.

42. The system for noninvasively detecting cerebral phenomena of claim 41 wherein said plurality of said means for acquiring electroencephalographic signals is a plurality of electrodes attachable to a head of a subject being analyzed to obtain a unipolar electroencephalographic signal from each of a plurality of regions of interest on both left and right hemispheres of the subject'"'"'s brain.

43. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said means for acquiring encephalographic signals comprises:

a plurality of surface electrodes for mounting on a surface of a head of the subject being analyzed;

means for providing electrosurgery protection including a radio frequency filter for limiting radio frequency current through said electrodes;

means for providing defibrillator protection for limiting voltage to said amplifier during a discharge;

means for amplifying said filtered signals for a high gain in order to maximize the dynamic range for high frequency, low energy wave components of said filtered signals;

means for feeding said signals to an analog-to-digital converter to convert said signals to digital signals.

44. The system for noninvasively detecting cerebral phenomena of claim 40 further comprising means for analyzing said signals received by each electrode in order to detect electrode failure.

45. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said bispectral values are autobispectral density values and further comprising means for organizing said autobispectral density values in at least one array of autobispectral density values.

46. The system for noninvasively detecting cerebral phenomena of claim 45 where said means for generating at least one array of autobispectral density values comprises:

means for computing fast Fourier transforms X_i (f) and Y_i (f) of each of said data records i;

means for computing power spectra P_Xi (f) and P_Yi (f) of said data records i by squaring the magnitude of elements of said fast Fourier transforms X_i (f) and Y_i (f) respectively;

means for computing for said at least one electrode an average complex triple product of data records acquired by said at least one electrode;

means for computing for said at least one electrode an average real triple product of data records acquired by said at least one electrode;

means for computing for said at least one electrode an autobispectral density value as the absolute value of the average complex triple product for said electrode.

47. The system of noninvasively detecting cerebral phenomena of claim 46 further comprising means for computing an autobiphase value φ

(f₁,f₂) for said at least one electrode such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the average complex triple product for an electrode, and f₁ and f₂ designate limits of the frequency range over which the autobiphase computation is carried out.

48. The system for noninvasively detecting cerebral phenomena of claim 46 further comprising means for computing an autobicoherence value R(f₁,f₂) for said at least one electrode such that

49. The system for noninvasively detecting cerebral phenomena of claim 45 where said means for generating at least one array of crossbispectral density values comprises:

means for computing fast Fourier transforms X_i (f) and Y_i (f) of each of said data records i;

means for computing power spectra P_Xi (f) and P_Yi (f) of said data records by squaring the magnitude of elements of said fast Fourier transforms X_i (f) and Y_i (f) respectively;

means for computing for at least one electrode pair an average complex triple product of all data records acquired by each of said at least one electrode pair;

means for computing for said at least one electrode an average real triple product of all data records acquired by for each of said at least one electrode pair;

means for computing for said at least one electrode pair a crossbispectral density value as the absolute value of the average complex triple product for said electrode pair.

50. The system for noninvasively detecting cerebral phenomena of claim 49 further comprising means for computing a crossbiphase value φ

(f₁,f₂) for said at least one electrode pair such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the average complex triple product for an electrode pair, and f₁ and f₂ designate limits of the frequency range over which the crossbiphase computation is carried out.

51. The system for noninvasively detecting cerebral phenomena of claim 50 further comprising means for computing an crossbicoherence value R(f₁,f₂) for said at least one electrode pair such that

52. The system for noninvasively detecting cerebral phenomena of claim 45 wherein said means for generating at least one array of autobispectral density values comprises:

means for computing autocorrelation sequences R_2X (m) and R_2Y (m) of all data records acquired by at least one electrode;

means for determining the orders and coefficients of parametric models for power spectra of data records acquired by said at least one electrode;

means for computing power spectra Px(f) and P_Y (f) of all data records acquired by said at least one electrode;

means for computing third order moment sequences R_3X (τ

) and R_3Y (τ

) of data records acquired by said at least one electrode;

means for determining the orders and coefficients of parametric models of the bispectra of data records acquired by said at least one electrode;

means for computing for said at least one electrode a bispectrum of data records acquired by said at least one electrode.

53. The system for noninvasively detecting cerebral phenomena of claim 52 wherein said bispectrum is autobispectrum and further comprising means for computing an autobispectral density value for each electrode as the absolute value of the bispectrum of data records for each electrode.

54. The system for noninvasively detecting cerebral phenomena of claim 53 further comprising:

means for computing for at least one electrode a real triple product of all data records acquired by said at least one electrode;

means for computing an autobicoherence value R(f₁,f₂) for said at least one electrode such that

space="preserve" listing-type="equation">R(f.sub.1,f.sub.2)=BD(f.sub.1,f.sub.2)/[BR(f.sub.1,f.sub.2)].sup.1/2

where BD(f₁,f₂) is the autobispectral density value for an electrode, BR(f₁,f₂) is the real triple product for the same electrode, and f₁ and f₂ designate limits of the frequency range over which autobicoherence computation is carried out.

55. The system for noninvasively detecting cerebral phenomena of claim 53 further comprising means for computing an autobiphase value φ

(f₁,f₂) for at least one electrode such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the bispectrum for an electrode, and f₁ and f₂ designate limits of the frequency range over which the autobiphase computation is carried out.

56. The system for noninvasively detecting cerebral phenomena of claim 52 wherein said bispectrum is autobispectrum and further comprising means for computing an autobispectral density value for each electrode a the absolute value of the bispectrum of data records for said electrode pair.

57. The system for noninvasively detecting cerebral phenomena of claim 56 further comprising:

means for computing for at least one electrode a real triple product of all data records acquired by said at least one electrode;

means for computing a crossbicoherence value R(f₁,f₂) for said at least one electrode pair such that

space="preserve" listing-type="equation">R(f.sub.1,f.sub.2)=BD(f.sub.1,f.sub.2)/[BR(f.sub.1,f.sub.2)].sup.1/2

where BD(f₁,f₂) is the crossbispectral density value for an electrode pair, BR(f₁,f₂) is the real triple product for the same electrode pair, and f₁ and f₂ designate limits of the frequency range over which the crossbicoherence computation is carried out.

58. The system for noninvasively detecting cerebral phenomena of claim 53 further comprising means for computing a crossbiphase value φ

(f₁,f₂) for at least one electrode pair such that;

space="preserve" listing-type="equation">φ

(f.sub.1,f.sub.2)=tan.sup.-1 [Im(BC(f.sub.1,f.sub.2))/Re(BC(f.sub.1,f.sub.2))]

where BC(f₁,f₂) is the crossbispectrum for an electrode pair, and f₁ and f₂ designate limits of the frequency range over which the crossbiphase computation is carried out.

59. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said bispectral values are autobicoherence variables and further comprising means for organizing said autobicoherence values in at least one array of autobicoherence values.

60. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said bispectral values are autobiphase values and further comprising means for organizing said autobiphase values in at least one array of autobiphase values.

61. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said bispectral values are crossbispectral density values and further comprising means for organizing said crossbispectral density values in at least one array of crossbispectral density values.

62. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said bispectral values are crossbicoherence values and further comprising means for organizing said crossbicoherence values in at least one array of autobicoherence values.

63. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said bispectral variables are crossbiphase values and further comprising means for organizing said crossbiphase values in at least one array of autobiphase values.

64. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said means for acquiring encephalographic signals further comprises means for obtaining bipolar data sets of electroencephalographic signals from different regions of a brain of said subject.

65. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said means for comparing further comprises:

means for organizing said generated bispectral values in an array of bispectral variables;

means for selecting a physical phenomena to be diagnosed;

means for retrieving an appropriate bispectral reference array from a resident memory, said reference array containing frequency pairs that are most sensitive to the physical phenomena to be diagnosed;

means for adding data values in locations of each of said at least one array of bispectral values that are identified by the retrieved reference array as being locations containing data of significance to obtain a sum of said significant locations;

means for averaging the values stored in said significant locations to generate a diagnostic index relating to the cerebral phenomena to be detected.

66. The system for noninvasively detecting cerebral phenomena of claim 40 further comprising:

means for generating three arrays of bispectral data for each of three different states of the subject;

means for performing a paired Student'"'"'s t test comparing the data in a first and a second array of said three arrays of bispectral data to produce a first t array and performing a paired Student'"'"'s t test comparing the data in said second and a third array of said three arrays of bispectral data to produce a second t array;

means for comparing each data value in said first t array with data values in corresponding locations in said second t array;

means for identifying those corresponding locations in said first and second t arrays that differ by more than a preselected amount, said identified locations representing those locations that are significant for detecting the cerebral phenomena.

67. The system for noninvasively detecting cerebral phenomena of claim 40 further comprising means for displaying a representation of a subject'"'"'s head, divided into a selected number of sections, said means for displaying including means for displaying a compressed continuous tracing of a computer diagnostic index determined from the signals acquired from an electrode positioned at a location represented by said section.

68. The system for noninvasively detecting cerebral phenomena of claim 64 wherein each displayed section includes a background of one of a plurality of colors, each of which colors is unique to a distinct selected range of possible values of a selected diagnostic index.

69. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said means for generating bispectral values comprises a means for computing the Fourier transform of the third order autocorrelation function of said filtered signals.

70. The system for noninvasively detecting cerebral phenomena of claim 40 wherein said means for generating bispectral values comprises a means for computing the Fourier transform of the third order crosscorrelation function of said filtered signals.