US20140187973A1 - System and method for tracking brain states during administration of anesthesia - Google Patents

System and method for tracking brain states during administration of anesthesia Download PDF

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US20140187973A1
US20140187973A1 US14/115,682 US201214115682A US2014187973A1 US 20140187973 A1 US20140187973 A1 US 20140187973A1 US 201214115682 A US201214115682 A US 201214115682A US 2014187973 A1 US2014187973 A1 US 2014187973A1
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patient
drug
processor
phase
time
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Emery N. Brown
Patrick L. Purdon
Aylin Cimenser
Eran A. Mukamel
Michael J. Prerau
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Definitions

  • the present invention relates to systems and methods for tracking brain states of a patient under anesthesia and, more particularly, to systems and methods for correlating anticipated effects of a given anesthetic compound administered to a patient with characteristics of the patient's brain state during the administration of the given anesthetic compound to more accurately track the effects of the given anesthetic compound and the actual brain state of the patient.
  • Obvious variables include physical attributes of the patient, such as age, state of general health, height, or weight, but also less obvious variables that are extrapolated, for example, based on prior experiences of the patient when under anesthesia.
  • these variables are compounded with the variables of a given anesthesiologists' practices and the variables presented by a particular anesthetic compound or, more so, combination of anesthetic compounds, the proper and effective administration of anesthesia to a given patient can appear to be an art and a science.
  • the present invention overcomes the aforementioned drawbacks by providing a system and method for determining the state of a patient's brain under anesthesia using readily-available monitoring information, such as from a patient's electroencephalography (EEG).
  • EEG electroencephalography
  • the present invention recognizes that anesthetic compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of such information.
  • the present invention aids the correlation of the physiological characteristics and signatures to a state of the patient's brain.
  • a system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties includes a plurality of sensors configured to acquire physiological data from the patient and at least one processor.
  • the processor is configured to assemble the physiological data into sets of time-series data associated with an origin location of the patient, transform each set of time-series data into a spectrum information, and determine coherence information with respect to the associated origin locations associated with the time-series of data.
  • the processor is further configured to identify signatures within at least one of the spectrum information and the coherence information indicative of at least one of a current state and a predicted future state of the patient and generate a report using the signatures including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.
  • a system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties includes a plurality of sensors configured to acquire physiological data from the patient, a user interface configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties, and at least one processor.
  • the processor is configured to identify signature profiles indicative of at least one of a current state and a predicted future state of the patient based on the indication and assemble the physiological data into sets of time-series data.
  • the processor is further configured to analyze the sets of time-series data using the identified signature profiles and generate a report including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.
  • FIG. 1 is a schematic illustration of a system for determining the state of a patients brain under anesthesia in accordance with the present invention.
  • FIG. 2 is a flow chart setting forth the steps of a method for determining the state of a patient's brain under anesthesia in accordance with the present invention
  • FIG. 3A is a series of spectrograms acquired under different drug or patient characteristics.
  • FIG. 3B is a spectrogram and associated EEG waveforms showing the overlapping influence of different drugs administered to a patient.
  • FIG. 4 is a series of EEG waveforms collected to illustrate variations therein that can be observed as corresponding with respective patient states.
  • FIG. 5 is a collection of data readouts including EEG waveforms, a frequency analysis, and a spectrogram illustrating key markers within the data and reflected in each data readout.
  • FIG. 6 is a collection of spectrograms of the radial current density estimated at each of a plurality of electrode sites.
  • FIG. 7 is a collection of EEG waveforms and spectrograms illustrating key markers within the data and reflected in each data format.
  • FIG. 8 is a set of graphs, phase-amplitude histograms, and EEG waveforms illustrating phase-amplitude analysis as a mechanism for determining and predicting future patient states.
  • FIG. 9 is a collection of EEG waveforms and spectrograms illustrating key markers within the data and reflected in each data format.
  • FIGS. 10-15 are graphs, each figure corresponding to a different drug, that illustrate the ability to create “spectral templates” for each of a plurality of exemplary drugs, which can be used in accordance with the present invention.
  • the present invention recognizes that anesthetic compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of physiological characteristics and signatures therein based on a selected anesthesia compound. Using the physiological characteristics and signatures associated with the selected anesthesia compound, the present invention aids the correlation of the physiological characteristics and signatures to a state of the patient's brain.
  • a system 10 configured for use in accordance with the present invention includes a patient monitoring device 12 , such as a physiological monitoring device, illustrated in FIG. 1 as an electroencephalography (EEG) electrode array.
  • EEG electroencephalography
  • the patient monitoring device may also include mechanisms for monitoring galvanic skin response (GSR), for example, to measure arousal to external stimuli.
  • GSR galvanic skin response
  • One specific realization of this design utilizes a frontal Laplacian EEG electrode layout with additional electrodes to measure GSR.
  • Another realization of this design incorporates a frontal array of electrodes that could be combined in post-processing to obtain any combination of electrodes found to optimally detect the EEG signatures described earlier, also with separate GSR electrodes.
  • Another realization of this design utilizes a high-density layout sampling the entire scalp surface using between 64 to 256 sensors for the purpose of source localization, also with separate GSR electrodes.
  • the patient monitoring device 12 is connected via a cable 14 to communicate with a monitoring system 16 .
  • cable 14 and similar connections can be replaced by wireless connections between components.
  • the monitoring system 18 may be further connected to a dedicated analysis system 18 .
  • the monitoring system 16 and analysis system 18 may be integrated.
  • the patient monitoring device 12 may be an EEG electrode array, for example, a 64-lead EEG electrode array.
  • EEG electrode array for example, a 64-lead EEG electrode array.
  • greater spatial accuracy can be achieved by increasing the number of electrodes from 64 to 128, 256, or even higher.
  • the present invention can be implemented with substantially less electrodes.
  • the monitoring system 16 may be configured to receive raw signals acquired by the EEG electrode array and assemble, and even display, the raw signals as EEG waveforms.
  • the analysis system 18 may receive the EEG waveforms from the monitoring system 16 and, as will be described, analyze the EEG waveforms and signatures therein based on a selected anesthesia compound, determine a state of the patient based on the analyzed EEG waveforms and signatures, and generate a report, for example, as a printed report or, preferably, a real-time display of signature information and determined state.
  • a report for example, as a printed report or, preferably, a real-time display of signature information and determined state.
  • the functions of monitoring system 16 and analysis system 18 may be combined into a common system.
  • a method for analysis and reporting in accordance with the present invention begins at process block 200 with the selection of a desired drug, such as anesthesia compound or compounds, and/or a particular patient profile, such as a patient's age height, weight, gender, or the like. Such selection may be communicated through a user interface 20 of FIG. 1 . Furthermore, drug administration information, such as timing, dose, rate, and the like, in conjunction with the above-described EEG data may be acquired and used to estimate and predict future patient states in accordance with the present invention. As will be described, the present invention recognizes that the physiological responses to anesthesia vary based on the specific compound or compounds administered, as well as the patient profile.
  • the present invention accounts for this variation between an elderly patient and a younger patient. Furthermore, the present invention recognizes that analyzing physiological data for signatures particular to a specific anesthetic compound or compounds administered and/or the profile of the patient substantially increases the ability to identify particular indicators of the patient's brain being in a particular state and the accuracy of state indicators and predictions based on those indicators.
  • drugs are examples of drugs or anesthetic compounds that may be used with the present invention: Propofol, Etomidate, Barbiturates, Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines, Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane, Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil, and the like.
  • the present invention recognizes that each of these drugs, induces very different characteristics or signatures, for example, within EEG data or waveforms.
  • FIG. 15 provides EEG data for one prominent drug, propofol, and associated states.
  • FIG. 3A a plurality of spectrograms corresponding, as labeled, to patients having been administered Propofol, Dexmedetomidine, Sevoflurane, and Ketamine are illustrated.
  • FIG. 3A shows a spectrogram of an elderly patient.
  • the spectrograms vary substantially, so as to be visually distinct, based on the administered drug and/or patient profile. This is particularly true, for example, when multiple drugs are combined, such as illustrated in FIG. 3B .
  • the present invention recognizes the substantial variation in physiological data acquired from a patient and the signatures contained therein.
  • the present invention Based on a selected drug or drugs and/or the patient profile and, by faking this recognition into account, the present invention provides systems and methods for tracking brain states during the administration of anesthesia that is greatly improved over traditional systems.
  • a summary of exemplary “spectral templates” for each of a plurality of exemplary drugs is provided in the “examples” section. These “spectral templates” can be used to automatically identify a current or project a future state of the patient.
  • EEG data EEG data
  • FIG. 4 a series of EEG waveforms in the time domain are illustrated. As is clear in a side-by-side comparison such as illustrated in FIG. 4 , these EEG waveforms vary appreciably.
  • the present invention provides systems and methods for analyzing acquired physiological information from a patient, analyzing the information and the key indicators included therein, and extrapolating information regarding a current and/or predicted future state of the patient.
  • the physiological data is processed. Processing can be done in the electrode or sensor space or extrapolated to the locations in the brain. As will be described, the present invention enables the tracking of the spatiotemporal dynamics of the brain by combining spectrogram and global coherence analyses. As will be apparent, reference to “spectrogram” in may refer to a visual representation of frequency domain information, such as represented in, for example, FIG. 3A . However, a “spectrogram” within the context of the present invention need not be visually represented or displayed. Rather, within the context, for example, of processing and report generation, the spectrogram may be an intermediate processing step from which reports or visual representations are ultimately created. For example, referring to FIG.
  • EEG waveforms in the time domain 500 have a spectrum 502 and can be translated into a spectrogram 504 .
  • Laplacian referencing can be performed at process block 208 to estimate radial current densities perpendicular to the scalp at each electrode site of, for example, the monitoring device 12 of FIG. 1 . Accordingly, though “spectrogram” processing is performed, a visual representation of the spectrogram need not be displayed.
  • V i m (t) denotes the voltage recording at the m th closest electrode to electrode i.
  • the EEG recorded at a particular location was locally referenced to an average of the EEG recorded at the neighbors.
  • spectrograms at each electrode site are computed.
  • spectrograms of the radial current density estimated at each of a plurality of electrode sites are illustrated.
  • the spectrograms reflect, as illustrated in FIG. 7 , key signatures commonly identified or tracked in the time domain EEG waveforms.
  • two time domain EEG waveforms and associated spectrograms are illustrated, one set 700 corresponding to light Dexmedetomine sedation and one set 702 corresponding to deep Dexmedetomine sedation.
  • spindles 704 are visible and in the set 702 corresponding to deep Dexmedetomine sedation, strong slow wave oscillations 706 are visible.
  • the local referencing is preferable so that distinct temporal patterns in the spectrogram at different electrode sites can be identified. This is in contrast to an average or single electrode referencing scheme, which would lead to an erroneous conclusion that approximately the same temporal pattern was present in the spectrogram at each electrode site.
  • the spectrum of the surface Laplacian at the location of the i th electrode site is estimated by averaging over K non-overlapping segments:
  • an eigenvalue decomposition can be computed of a cross-spectral matrix at each spectral frequency as a function of time.
  • spectral and eigenvalue-based global coherence analyses can be used to track the spatiatemporal dynamics of the brain's anesthetic state.
  • the global coherence analysis detects strong coordinated a activity in the occipital sites when the patient is awake that shifts to the frontal sites when the patient becomes unconscious.
  • method-of-moments estimates of the i*n element of the cross-spectral matrix at a frequency f can be computed as:
  • X i k (f) and X j k (f) are the tapered Fourier transforms of the current density estimates from electrode sites i and j, respectively, at frequency f.
  • C x (f) is an N ⁇ N matrix of cross-spectra.
  • An orthogonal basis can be obtained by performing a Karhunen-Loeve transform at each frequency, f:
  • U(f) H is chosen so that under the Karhunen-Loeve transform the cross-spectral matrix in the new basis:
  • This ratio is called the global coherence.
  • C Global (f) is close to 1.
  • examining the contributions of different sites to the corresponding eigenvector by using the elements of the weight matrix provides a summary of coordinated activity at this frequency.
  • These elements are row weights.
  • the row weights can be obtained by the absolute value square of the elements of the row of U(f) H , which leads to the eigenvector with the highest eigenvalue.
  • process blocks 208 - 212 yield two pieces of valuable information, namely, the spectrogram and global coherence information, which show different spatiotemporal activity at different states of the patient receiving anesthesia.
  • the spectrograms will show strong occipital ⁇ activity.
  • the spectrograms will show a loss of ⁇ activity and an increase in ⁇ activity in the occipital sites and strong ⁇ and ⁇ activity in the frontal sites, increased power in the ⁇ , ⁇ , and ⁇ ranges in the frontal sites will occur after loss of consciousness, consistent with the well-known pattern of anteriorization.
  • global coherence and weight matrices along with spectrograms provide a first level of data for determining a current state and predicting a future state of a patient's brain under anesthesia. Further details regarding initial testing and validation of such processes are provided in Cimenser A, Purdon P L, Pierce E T, Walsh J L, Salazar-Gomez A F, Harrell P G, Tavares-Stoeckel C, Habeeb K, Brown E N (2011) Tracking brain states under general anesthesia by using global coherence analysis. Proceedings of the National Academy of Sciences of the United States of America 108:8832-8837.
  • phase-amplitude analysis is performed that considers the amplitude of a given signal with respect to the phase of other signals and vice versa.
  • spectral analysis of EEG recordings allows the present invention to track systematic changes in the power in specific frequency bands associated with administration of anesthesia, including changes in ⁇ (1-4 Hz), ⁇ (5-8 Hz), ⁇ (8-14 Hz), ⁇ (12-30 Hz), and ⁇ (30-80 Hz).
  • spectral analysis treats oscillations within each frequency band independently, ignoring correlations in either phase or amplitude between rhythms at different frequencies.
  • Power spectral measures are invariant with respect to changes in the complex phase of a signal's Fourier transform. It is thus natural to extend power spectral analysis by using measures that are sensitive to signal phase. Bispectral analysis can detect the presence of correlation in the phases of oscillation at different frequencies. Bispectrum-based statistics have been used in quantitative clinical depth-of-anesthesia monitors, in a manner that compares the bispectrum across broad low- and high-frequency ranges.
  • phase-amplitude analysis instead of a traditional “cross-frequency correlation”, phase-amplitude analysis, is used, in phase-amplitude analysis, the amplitude or envelope of activity in one frequency band is consistently largest at a particular phase of a lower frequency rhythm. For example, given two non-overlapping frequency bands, then in phase-amplitude coupling, the amplitude of the activity in the higher frequency band is consistently highest at a particular phase of the lower frequency rhythm.
  • an analysis can be performed to measure phase-amplitude coupling in a time-resolved fashion to identify at least two distinct modes of phase-amplitude coupling corresponding to shallow and deep planes of anesthesia, respectively.
  • a time-varying phase-amplitude modulogram M(t, ⁇ ) can be created that describes the relative ⁇ (or other) amplitude at a particular phase at each SO cycle.
  • ultra-low-frequency drift is removed by subtracting a least-square errors spline fit to the signal with one knot for every 2 minutes (or other selected duration) of data.
  • a band-pass filter may be applied to extract the rhythmic component within each frequency band of interest, x b (t),b ⁇ ,SO ⁇ .
  • Symmetric finite impulse response filters designed using a least-squares approach SO: passband 0.1-1 Hz, transition bands 0.085-0.1 and 1-1.15 Hz, ⁇ 17 dB attenuation in stop bands, order 2207 at 250 Hz; ⁇ : passband 8-13.9 Hz, transition bands 5.9-8 and 13.9-16 Hz, ⁇ 60 dB attenuation in stop bands, order 513
  • and SO phase, ⁇ (t) arg[z SO (t)].
  • the modulogram is computed by assigning each temporal sample to one of, for example, 18 equally spaced phase bins based on the instantaneous value of ⁇ /(t), then averaging the corresponding values of A(t) within, for example, a 2-minute epoch:
  • M ⁇ ( t , ⁇ ) ⁇ t - ⁇ ⁇ ⁇ t 2 t + ⁇ ⁇ ⁇ t 2 ⁇ ⁇ ⁇ - ⁇ ⁇ ⁇ t 2 ⁇ + ⁇ ⁇ ⁇ t 2 ⁇ A ⁇ ( t ′ ) ⁇ ⁇ ⁇ ( ⁇ ⁇ ( t ′ ) - ⁇ ′ ) ⁇ ⁇ ⁇ ′ ⁇ ⁇ t ′ 2 ⁇ ⁇ ⁇ t - ⁇ ⁇ ⁇ t 2 t + ⁇ ⁇ ⁇ t 2 ⁇ A ⁇ ( t ′ ) ⁇ ⁇ t ′ ; Eqn . ⁇ 7
  • FIG. 8 illustrates two distinct patterns of phase-amplitude modulation. Namely, a first phase-amplitude modulation is similar to slow wave sleep (peak-max—i.e., high-frequency activity is highest at the peak of the low-frequency oscillation, corresponding to a low-frequency phase of 0), and a second phase-amplitude modulation foreshadows the return of consciousness (trough-max—i.e., high-frequency activity is highest at the trough of the low-frequency oscillation, corresponding to a low-frequency phase of +/ ⁇ ).
  • Slow oscillation phase modulates alpha/beta (8-14 Hz) amplitude, in relation to probability of response, can be studied and is reflected in FIG. 8 .
  • a group behavioral curves 802 show the probability of response to click and verbal stimuli during induction in the first graph 804 and emergence in the second graph 806 .
  • a set of phase-amplitude histograms 808 show the relationship between the slow oscillation phase (y-axis, shown with reference sinusoid) and mean-normalized alpha/beta amplitude as a function of time (x-axis) relative to LOG 810 and ROC 812 .
  • a trough-max phase-amplitude relationship can be observed at the LOC/ROC transition points, where the amplitude of alpha is maximal at the slow oscillation troughs.
  • a peak-max phase-amplitude relationship can be observed during the unconscious state, where the amplitude of alpha is maximal at slow oscillation peaks.
  • These trough-max and peak-max modulation patterns can be observed in raw EEG traces 814 , which shows the trough-max and 816 , which shows the peak-max, respectively.
  • the trough-max pattern re-appears during emergence prior to ROC, illustrating that it can be used to predict when patients are able to regain consciousness during anesthesia.
  • the transition to the trough-max pattern occurs reliably before return of consciousness, the trough-max relationship to predict when patients are likely to recover consciousness while emerging from anesthesia, in cases where trough-max modulation is absent, due to pathology that impairs alpha waves, or drug choice (e.g., sevoflurane), or where electrode placement makes detection of trough-max modulation difficult, the absence or loss of peak-max modulation could also be used to predict recovery of consciousness during emergence. More particularly, during emergence from propofol anesthesia, the peak-max modulation relationship between the phase of the slow oscillation and higher frequencies changes to the trough-max modulation relationship, and does so prior to the return of consciousness, with little change to the underlying power spectrum. The trough-max modulation has a frontal distribution, whereas the peak-max modulation is distributed approximately uniformly across the scalp.
  • phase-amplitude information can provide a reliable indicator of a current or probable future patients state.
  • the peak-max modulation pattern represents a state of unconsciousness that is more profound than that observed during trough-max modulation, but less profound than burst-suppression
  • the peak-max modulation pattern could be used as a target for maintenance of a surgical level of anesthesia.
  • the trough-max modulation represents a state of unconsciousness that is less profound than the peak-max modulation, one where patients can respond to external stimuli
  • the trough-max modulation pattern could be used as a target for maintenance of sedation.
  • elderly patients often exhibit diminished alpha waves or a lack of alpha waves.
  • monitoring can also be performed by calculating slow oscillation phase-amplitude modulation across a broad-band frequency range including theta, alpha, beta, and gamma bands.
  • MI(t) can be defined, as the Kullback-Leibler divergence, in bits, between M (t, ⁇ ) and a uniform phase distribution over the interval ( ⁇ , ⁇ ):
  • MI ⁇ ( t ) ⁇ - ⁇ ⁇ ⁇ M ⁇ ( t , ⁇ ) 2 ⁇ ⁇ ⁇ log 2 ⁇ M ⁇ ( t , ⁇ ) ⁇ ⁇ ⁇ . Eqn . ⁇ 8
  • the power spectrum and phase-amplitude coupling may be complementary sources of information about brain dynamics.
  • a combination of both measures may reveal greater structure than either analysis alone.
  • the EEG power spectrum during gradual administration of anesthesia shows a broad-band peak that begins in gamma frequencies, and decreases in frequency and bandwidth into the low-beta and alpha bands with increasing doses of anesthesia resulting in loss of consciousness.
  • the gamma and beta range effects are associated with a reduced probability of response to external stimuli. Power within this traveling peak is strongest in frontal EEG channels. This reverses after recovery of consciousness.
  • the traveling peak frequency can be quantified, for example, as the median between 2 and 40 Hz, and calculate the bandwidth using the interquartile range between the same limits.
  • phase-amplitude modulation effect and systems and methods for monitoring thereof is best observed using a local average of several electrodes, such as the surface Laplacian. Otherwise, phase-amplitude modulation effect can be poorly resolved or not observable.
  • a beamforming procedure may be used to improve estimation of phase-amplitude modulation.
  • ⁇ (t): [ ⁇ 1 (t), ⁇ 2 (t), . . . , ⁇ N (t)] T
  • s(t): [s 1 (t), s 2 (t), . . .
  • s N (t)] T denote the alpha rhythm and slow oscillation time-series which are obtained by band-pass filtering x(t) in the frequency bands of 8-14 Hz and 0.1-1 Hz, respectively.
  • the amplitude of the alpha rhythm is modulated by the phase of the slow oscillation during anesthesia, based on an analysis of single-channel Laplacian-derived EEG.
  • the problem reduces to reconstructing a single phase-amplitude modulation relationship based on the observation through the multi-channel array of EEG sensors.
  • a viable solution is given by beamforming.
  • the idea of beamforming is to form a scalar signal based on the array observations in order to minimize an appropriate cost function representing the underlying system model.
  • the amplitude of ⁇ W (t) and the phase (argument) of s W (t) are given by:
  • phase-amplitude modulation relation is defined as:
  • a W ( ⁇ ; t ): E p w ⁇ A w ( t )
  • a suitable model for estimating A W ( ⁇ ) is given by its truncated Fourier expansion to the first L terms, with L ⁇ 3. This reduced-order model enforces a smooth phase-amplitude modulation relation, which is consistent with empirical observations.
  • a suitable cost function for estimating A W ( ⁇ ) is given by the following quadratic form:
  • t w ( ⁇ ) denotes the inverse function of ⁇ w (t)
  • p( ⁇ ) is the prior distribution of the slow oscillation phase
  • E t denotes temporal averaging.
  • the best such beamformer can be chosen by minimizing the resulting cost function over w.
  • This corresponds to a cost minimization formulation for estimating the reduced-order phase-amplitude modulation relation that is most consistent with the data (in the sense of the above quadratic cost function).
  • the overall optimization procedure can be expressed as:
  • the inner minimization can be carried out by linear regression and the resulting solution can be expressed explicitly in terms of A w (t) and ⁇ w (t).
  • the outer minimization can be performed using standard optimization routines. In particular, since the constraints on w k form a convex set, the interior point method for the outer minimization stage can be employed.
  • phase-amplitude modulation of frontal EEG under anesthesia undergoes two different patterns of modulation, corresponding to depth of anesthesia.
  • the first pattern occurring before and after the loss of consciousness, consists of maximum alpha amplitude occurring at the trough (surface-negative) of the slow oscillation, which can be referred to as the “trough-max” pattern.
  • trough-max maximum alpha amplitude occurring at the trough (surface-negative) of the slow oscillation
  • peak-max the relationship reverses and maximum alpha amplitude occurs at the peak (surfacepositive) of the slow oscillation.
  • Patient 1 Trough-Max Peak-Max Trough-Max Peak-Max Bipolar 0.26 0.39 0.22 0.76 Laplacian 1.08 0.73 0.98 1.00 Optimized 1.23 0.91 1.33 1.67
  • the beamforming method produced the largest modulation depth, followed by the Laplacian method, with bipolar referencing showing the lowest modulation depth in both regimes.
  • the beamforming method provides a means to obtain electrode weights that minimize the least-squares error in a parametric sinusoidal model of the phase-amplitude relationship. This optimal weighting of EEG electrodes allows for improved detection of phase-amplitude modulation across time and patients. This method could be useful in studies of phase-amplitude modulation in the EEG under anesthesia, as well as other conditions where this phenomenon might arise.
  • the above-described selection of an appropriate analysis context based on a selected drug or drugs (process block 200 ), the acquisition of data (process block 204 ), and the analysis of the acquired data (process blocks 206 - 214 ) set the stage for the new and substantially improved real-time analysis and reporting on the state of a patient's brain as an anesthetic or combination of anesthetics is being administered and the recovery from the administered anesthetic or combination of anesthetics occurs.
  • the present invention provides a mechanism for considering each of these separate pieces of data and more to accurately indicate and/or report on a state of the patient under anesthesia and/or the indicators or signatures that indicate the state of the patient under anesthesia.
  • any and ail of the above-described analysis and/or results can be reported and, in addition, can be coupled with a precise statistical characterizations of behavioral dynamics. That is, behavioral dynamics, such as the points of loss-of-consciousness and recovery-of-consciousness can be precisely, and statistically calculated and indicated in accordance with the present invention. To do so, the present invention may use dynamic Bayesian methods that allow accurate alignment of the spectral and global coherence analyses relative to behavioral markers.
  • a state-space model with two state variables representing a probability of response and a conditional probability of correct response can be used to correlate outcomes with a predicted future state. Probability densities of model parameters and the response probability are computed within a Bayesian framework to provide precise statistical characterizations of behavioral dynamics.
  • the experiment consists of K stimulus trials. On any trial these are three possible outcomes for the response to the verbal stimulus; the subject may respond correctly, respond incorrectly or not respond.
  • the observed data at trail k is the pair (m k, n k ) which can assume the values ⁇ (1,1), (1,0), (0,0) ⁇ .
  • the observation model at trail k is therefore:
  • p k ,q k ) [ p k ( q k ) 1-n k ] m k (1 ⁇ p k ) 1-m k Eqn. 17;
  • ⁇ k and ⁇ k are zero-mean Gaussian random variables with variances ⁇ ⁇ 2 ⁇ k and ⁇ n 2 ⁇ k where ⁇ k is the time elapsed between verbal stimulus trials k ⁇ 1 and k.
  • p k , q k , 4 ) p k m k ⁇ ( 1 - p k ) 4 - m k ⁇ ( m k n k ) ⁇ q k n k ⁇ ( 1 - q k ) m k - n k . Eqn . ⁇ 22
  • M ) f ⁇ ( M
  • the observation models (Eqs. 17-19, 22) define f (M
  • f( ⁇ ) we chose the independent prior distributions for x v,0 , z v,0 , x c,0 and z c0 to be uniform distributions each on the interval [0,100].
  • the set of free parameters includes all the x 0 and ail the ⁇ 2 . These are computed using the Bayesian approach, which assumes that prior information about the parameters improves the parameter estimates. For all the x 0 , uniform prior distribution, uniform (a, b), can be chosen. For all the ⁇ 2 , the conjugate inverse gamma prior distribution, inverse gamma ( ⁇ , ⁇ ) can be chosen. Assuming values of 0 and 100 for a and b respectively to reflect the fact that the patient's behavior markers correlates perfectly at the beginning of tracking, ⁇ and ⁇ can be chosen to be 5 and 1, making the inverse gamma prior distribution non-informative.
  • a Bayesian analysis implementation such as described in A. C. Smith, S.
  • the result is a report that can be coupled with a precise statistical characterization of behavioral dynamics. That is, behavioral dynamics, such as the points of loss-of-consciousness and recovery-of-consciousness (or other selected states) can be precisely, and statistically calculated and indicated in accordance with the present invention. Specifically, this report can aid clinicians by allowing information that was previously unknown or incapable of being discerned from traditional monitoring systems to be identified and communicated and/or used by the clinician and/or monitoring system to identify particular states of a given patient.
  • the report may serve as part of a “human in-the-loop” operational strategy, whereby the above-described systems automatically detects spectral features of interest and the report serves as a mechanism by which to highlight or communicate the spectral features and the information extrapolated therefrom to inform clinicians of a given or predicted future state and the reasoning therebehind.
  • the report may include topographic maps of the patient's scalp or source localization maps on a brain image rendering to provide information relating to the location within the brain from which the EEG activity is being received.
  • FIG. 9 provides two sets of time domain EEG waveforms and associated spectrograms acquired during clinical use of Propofol.
  • a first set 900 illustrates two time domain EEG waveforms, a first EEG waveform demonstrating delta oscillation 902 and the second EEG waveform demonstrating delta-alpha oscillation 904 .
  • an associated spectrogram 906 displays the same delta oscillation and delta-alpha oscillation, but in form that is able to be more readily interpreted, particularly in real time.
  • a second set 908 illustrates two time domain EEG waveforms, a first EEG waveform demonstrating delta-alpha oscillation 910 and the second EEG waveform demonstrating burst suppression 912 . While simultaneously reading and interpreting the separate EEG waveforms 910 , 912 is difficult, an associated spectrogram 914 displays the same delta-alpha oscillation and burst suppression, but in form that is able to be more readily interpreted in real time.
  • a substantial amount of new and important signatures that were previously difficult to consider or understand and/or were previously unknown or not understood as reliable without considering drug and/or patient-specific signature information can be reliably determined and used to track a current state of a patient.
  • sevoflurane shows increased slow, delta, theta, and alpha power.
  • the increase in theta power is pronounced and visible in the power spectrum.
  • Slow oscillation phase-amplitude coupling shows that high frequency activity is greatest on the rising phase ( ⁇ /2) of the slow oscillation.
  • ketamine When ketamine is administered, power in beta and gamma bands increase.
  • the two drugs act on the EEG in an antagonistic fashion.
  • ketamine If enough ketamine is administered, it reduces or abolishes both slow and alpha power, and increases gamma power.
  • Dexmedetomidine shows increased slow, delta, and sigma (12-16 Hz) power at lower doses consistent with sedation. At higher doses the EEG is dominated by slow oscillations.
  • the above-referenced report can indicate, predict, and/or track onset of loss of consciousness and recovery of consciousness based on increased gamma (25 to 40 Hz) and beta (12 to 25 Hz) activity; transition to unconsciousness and in the unconscious state, and recovery of consciousness based on increased/decreased slow (0 to 1 Hz), delta (1 to 4 Hz), theta (4 to 8 Hz), and alpha (8 to 12 Hz) activity; anesthetic drug administration and loss of consciousness/recovery of consciousness based on reduced theta (4 to 8 Hz) power; loss of consciousness and recovery of consciousness associated with changes in the ratio of alpha and delta (1 to 4 Hz) power in the occipital region of the scalp; states of profound unconsciousness by identifying strong global coherence in the alpha band; states of profound anesthesia based on strong association between global coherence and the state of anteriorization; profound unconsciousness based on strong modulation of the theta (4 to 8 Hz), alpha (8 to 12 Hz
  • spectral templates for each of a plurality of exemplary drugs can be provided and used in accordance with the present invention.
  • EEG data was acquired from operating room surgical cases during the administration of anesthesia. The data was separated into three age demographics: young (>35), middle-aged (36-59), and elderly ( ⁇ 60); and by drug: propofol, sevoflurane, isoflurane, dexmedetomidine, and ketamine. Spectrograms from each of the cases were analyzed to identify temporal intervals containing recurring spectral motifs correlated with putative unconscious and deep levels of anesthesia. These intervals were used to compute median spectra for each of the drug/demographic pairings.
  • propofol has studied.
  • the spectral motifs for propofol included three salient spectral peaks: a low frequency ( ⁇ 1 Hz) oscillation, a traveling peak (spanning gamma through alpha), and a broadband gamma.
  • FIG. 11 provides information similar to that of FIG. 10 , in this case, with respect to sevoflurane.
  • the spectral motifs for sevoflurane included two salient spectral peaks: a broad low frequency oscillation (spanning ⁇ 1 Hz, delta, and theta bands), and a traveling peak.
  • FIG. 12 provides information similar to that of FIGS. 10 and 11 , in this case, with respect to isoflurane.
  • the spectral motifs for isoflurane included two salient spectral peaks: a broad low frequency oscillation (spanning ⁇ 1 Hz, delta and theta bands), and a traveling peak.
  • FIG. 13 provides information similar to that of FIGS. 10-12 , in this case, with respect to dexmedetomidine.
  • the data for young and elderly patients were limited and showed spectral heterogeneity.
  • the spectral motifs for dexmedetomidine included three salient spectral peaks: low frequency oscillation, a non-stationary “spindle” peak spanning alpha and sigma bands, and broadband gamma.
  • FIG. 14 provides information similar to that of FIGS. 10-13 , in this case, with respect to ketamine.
  • Data was acquired from one middle aged patient. The effects, however, seem marked between putative unconscious and deep states of anesthesia.
  • the spectral motifs for ketamine included three salient spectral peaks: the low frequency oscillation, and a non-stationary low gamma peak, and broadband high gamma (up to 150 Hz).
  • the present invention has ready use and clinical need in fields of medicine other than anesthesiology.
  • other medical specialties either use or have interest in the use of drugs such as those described above and other similar drugs.
  • the above-described systems and methods are useful for managing drugs in a wide variety of situations.
  • the present invention can be used during pharmacological therapies to induce and maintain sedation.
  • the present invention can be particularly useful in the intensive care unit where intense therapies are administered and clinicians can benefit from additional monitoring and feedback.
  • the present invention can be of use in outpatient settings, including outpatient treatments involving pharmacologically-induced sleep, involving sedation, for example, using dexmedetomidine.
  • the present invention can be used in psychiatry settings to aid in the treatment of depression with ketamine, for example. These are but a few of the wide-variety of clinical and non-clinical settings where the present invention can be readily applied.

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