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

Abstract

A method and apparatus for ascertaining the cerebral state of a patient. The method/apparatus may find use in 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 cerebral state. 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 cerebral state of the patient. In another embodiment, the frequency range over which the entropy of the biopotential signal from the patient is determined is broadened to encompass both EEG signal data and EMG signal data and the entropy so determined used as an indication of the patient's cerebral state.

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.    
     
     
         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 an 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.    
     
     
         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 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 18  wherein in step (d) a noise level is established below which the EMG signal data is considered to be zero.  
     
     
         20 . The method according to  claim 1  or  claim 2  further defined as repeating steps (c), (d), and (e) to update the composite indication.  
     
     
         21 . The method according to claims  1 ,  2 ,  3 ,  4 , or  5  further defined as one for ascertaining the hypnotic state of a patient.  
     
     
         22 . The method according to claims  17  further defined as one for ascertaining the hypnotic state of a patient.  
     
     
         23 . The method according to  claim 21  further defined as repeating steps (c), (d), and (e) to update the composite indication.  
     
     
         24 . The method according to  claim 17  further defined as repeating steps (c), (d), and (e) to update the composite indication.  
     
     
         25 . 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 . 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.    
     
     
         27 . The method according to  claim 25  or  claim 26  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.  
     
     
         28 . The method according to  claim 25  or  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 a measure of the entropy of the signal data.  
     
     
         30 . The method according to  claim 28  wherein step (b) is further defined as obtaining the spectral entropy of the signal data.  
     
     
         31 . The method according to  claim 28  wherein step (b) is further defined as obtaining the approximate entropy of the signal data.  
     
     
         32 . The method according to  claim 25  or  claim 26  wherein step (b) is further defined as obtaining a Lempel-Ziv complexity measure of the signal data.  
     
     
         33 . A method according to  claim 25  or  claim 26  wherein step (b) is further defined as obtaining the complexity measure from fractal spectrum analysis.  
     
     
         34 . The method according to  claim 25  or  claim 26  wherein prior to analyzing the sample in step (b) the biopotential signal is subjected to spectral decomposition.  
     
     
         35 . The method according to  claim 34  further defined as carrying out the spectral decomposition by means of a Fourier transform.  
     
     
         36 . The method according to  claim 34  further defined as carrying out the spectral decomposition by employing a set of basis functions other than a Fourier set of functions.  
     
     
         37 . The method according to  claim 36  further defined as carrying out the spectral decomposition by employing a set of basic functions corresponding to wavelet transformation.  
     
     
         38 . The method according to  claim 25  or  claim 26  further defined as repeating steps (c), (d), and (e) to update the indication.  
     
     
         39 . The method according to  claim 25  or  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 28  further defined as one for ascertaining the hypnotic state of a patient.  
     
     
         42 . The method according to  claim 29  further defined as one for ascertaining the hypnotic state of a patient.  
     
     
         43 . The method according to  claim 39  further defined as repeating steps (c), (d), and (e) to update the composite indication.  
     
     
         44 . The method according to  claim 25  or  claim 26  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.  
     
     
         45 . The method according to  claim 44  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.  
     
     
         46 . The method according to  claim 45  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.  
     
     
         47 . The method according to  claim 45  further defined as applying a constant to the normalized indications to maintain the normalization.  
     
     
         48 . 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.  
     
     
         49 . 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 32-300 Hz.  
     
     
         50 . The method according to  claim 49  further defined as notch filtering the EMG signal data to remove power line frequency harmonics.  
     
     
         51 . The method according to  claim 25  or  claim 26  further defined as notch filtering to remove power line frequency harmonics.  
     
     
         52 . The method according to  claim 1 ,  2 ,  25 , or  26  wherein the EEG signal data and the EMG signal data are obtained from a common signal source.  
     
     
         53 . The method according to  claim 52  wherein the EEG and EMG signal data are obtained from biopotential electrodes applied to the head of the patient.  
     
     
         54 . The method according to  claim 53  wherein at least the EMG signal data is obtained from electrodes applied to the forehead of the patient.  
     
     
         55 . 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 25  or  26  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 to remove artifacts.  
     
     
         58 . The method according to  claim 56  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 affected by artifacts.  
     
     
         60 . The method according to  claim 56  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 detecting excessive muscle activity in the patient from the EMG signal data and preventing the use of EEG signal data affected by the muscle activity.  
     
     
         62 . The method according to  claim 56  further defined as detecting excessive muscle activity in the patient from the biopotential signals or signal data and preventing the use of EEG signal data affected by the muscle activity.  
     
     
         63 . 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 signal data affected by such an artifact.  
     
     
         64 . The method according to  claim 56  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.  
     
     
         65 . The method according to  claim 55  further defined as detecting simultaneous spikes in both the EEG signal data and EMG signal data as eye movement artifacts and preventing the use of signal data affected by such artifacts.  
     
     
         66 . The method according to  claim 56  further defined as detecting simultaneous spikes in both the EEG signal data and EMG signal data as eye movement artifacts and preventing the use of biopotential signals or signal data affected by such artifacts.  
     
     
         67 . 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.    
     
     
         68 . 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.    
     
     
         69 . The apparatus according to  claim 68  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.  
     
     
         70 . The apparatus according to  claim 68  or  claim 69  wherein said analyzing means is further defined as obtaining a measure of the complexity of the EEG signal data as the first indicator.  
     
     
         71 . The apparatus according to  claim 70  wherein said analyzing means is further defined as obtaining a measure of the entropy of the EEG signal data as the first indicator.  
     
     
         72 . The apparatus according to  claim 70  wherein said analyzing means is further defined as obtaining a Lempel-Ziv complexity measure of the EEG signal data as the first indicator.  
     
     
         73 . The apparatus according to claims  68 ,  69 , or  70  wherein said analyzing means is further defined as obtaining the second indication from a frequency domain power spectrum of the EMG signal data.  
     
     
         74 . The apparatus according to claims  68 ,  69 ,  70 , or  71  further defined as one for ascertaining the hypnotic state of a patient.  
     
     
         75 . 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.    
     
     
         76 . The apparatus according to  claim 75  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.  
     
     
         77 . The apparatus according to  claim 75  or  claim 76  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.  
     
     
         78 . The apparatus according to  claim 75  or  claim 76  wherein said analyzing means is further defined as obtaining a measure of the entropy of the signal data.  
     
     
         79 . The apparatus according to  claim 77  wherein said analyzing means is further defined as obtaining a measure of the entropy of the signal data.  
     
     
         80 . The apparatus according to  claim 75  or  claim 76  wherein said analyzing means is further defined as obtaining a Lempel-Ziv complexity measure of the signal data.  
     
     
         81 . The apparatus according to  claim 75  or  claim 76  further defined as one for ascertaining the hypnotic state of a patient.  
     
     
         82 . The apparatus according to  claim 75  or  claim 76  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.  
     
     
         83 . The apparatus according to  claim 82  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.  
     
     
         84 . The apparatus according to  claim 68  or  claim 75  further defined as including means notch filtering the signal data to remove power line frequency harmonics.  
     
     
         85 . The apparatus according to  claim 68  or  claim 75  further defined as including means for processing the signal data to detect artifacts.  
     
     
         86 . The apparatus according to  claim 85  further defined as including means for filtering the signal data to remove artifacts.