WO2006122349A1 - Procede et dispositif pour surveiller la conscience pendant l'anesthesie - Google Patents

Procede et dispositif pour surveiller la conscience pendant l'anesthesie Download PDF

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WO2006122349A1
WO2006122349A1 PCT/AU2006/000643 AU2006000643W WO2006122349A1 WO 2006122349 A1 WO2006122349 A1 WO 2006122349A1 AU 2006000643 W AU2006000643 W AU 2006000643W WO 2006122349 A1 WO2006122349 A1 WO 2006122349A1
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signal
biosignal
response
aep
stimulus
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PCT/AU2006/000643
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English (en)
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David Burton
Eugene Zilberg
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Compumedics Medical Innovations Pty Ltd
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Priority claimed from AU2005902521A external-priority patent/AU2005902521A0/en
Application filed by Compumedics Medical Innovations Pty Ltd filed Critical Compumedics Medical Innovations Pty Ltd
Publication of WO2006122349A1 publication Critical patent/WO2006122349A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4821—Determining level or depth of anaesthesia
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/38—Acoustic or auditory stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • This invention relates to the field of apparatus and methods for monitoring consciousness during treatment with anaesthesia.
  • US patent 5,458,117 describes a real-time, high- resolution biopotential analysis system using EEG data.
  • the system of US 5,458,117 uses a sorting scheme for EEG biosignal values including auto/cross power spectrum, bispectrum, and higher-order spectrum array of any dimension and any frequency band, into a one-dimensional distribution function of the values it contains.
  • the one-dimensional distribution is then divided into fixed bands that can be combined to produce a diagnostic index.
  • the system calculates variables from the simple ratio or product of any two sorted or descriptive values calculated from the transformed EEG biosignal.
  • United States patent 6, 167,298 describes what is claimed to be the first invention for monitoring EEG for alert/non-alert state of consciousness.
  • AEPs auditory evoked potentials
  • AEPs are measurable biosignals appearing in an EEG measurement resulting from an auditory stimulus.
  • AEPs are brain responses "evoked” as a result of auditory stimulus presentation.
  • Techniques are available for the extraction of wanted auditory evoked potentials from the background random EEG signal.
  • the AEP waveform is visually identified by a number of waveform peaks and troughs, and is described by the AEPs latency and amplitude characteristics. The latency is normally measured in milliseconds, and is the time interval between a selected point on the stimulus signal and a selected feature on the AEP waveform, such as a peak.
  • the amplitude of the AEP signal is normally measured in millivolts, and is the difference between a peak and the following trough of an AEP waveform.
  • the latency of an AEP is thought to indicate its physiological source. For example, fast responses are measurable between the time of stimulus and 15 msec, and waveforms are denoted I to VII. "Middle latency responses" (MLRs) are those responses measurable between 10 and 50 msec from a stimulus event, and considered in the art to be representative of the transition of consciousness state during anaesthesia. MLRs are associated with awareness, consciousness depth or attention level. Middle latency responses associated with AEP stimuli (ML-AEP) comprises signals in the 10 to 50 msec post-auditory stimulus signal component latency range.
  • ML-AEP signal amplitudes range from 0.5 uV to 3.0 uV.
  • Typical signal classification includes: N0, P0, Pa, Nb, and the less frequent Pc and Nc signals, which are occasionally seen with the larger time-base range of 10 to 80 msec.
  • the most frequently studied signal ML-AEP are: a) Na, occurring with a latency of 15-20 msec and Pa, which occurs with a latency of 25-30 msec.
  • “Slow" AEP responses are those occurring from 50 to 200 msec, and are considered to be representative of brain cortex activity. Longer latency AEPs occurring greater than 200 msec are associated with audio perception.
  • TP41 is characteristic of an ML-AEP. TP41 may habituate or adjust subject to the number of stimuli that have been presented to a subject. It is highly localised over the lateral temporal scalp locations of each hemisphere, and evoked by left, right and binaural click stimulation. TP41 has been noted as being of definite neural origins, with experiments showing no overall EMG activity amongst relaxed subjects, during on-line monitoring of temporal channels. During the incidence of temporalis and post auricular muscle reflexes, EMG activity appeared with much shorter latencies.
  • TP41 as a measure from the auditory cortex region by way of the lateral temporal electrode placement regions provides possibilities for the elicitation of awareness measures useful during anaesthesia), attention and brain processing measures, potentially useful for Alzheimer onset and treatment investigations, auditory measures, useful for hearing threshold an and impairment measures, and drowsiness or fatigue measures, useful for avoiding driver, pilot or other critical daytime performance accidents or inefficiencies.
  • Clinically useful AEPs known in the art includes receptor potentials that originate from the cochlear and neurogenic potentials that originate from the acoustic nerve or neural populations within the central nervous system.
  • ABR auditory brain response
  • P6 is an example of a polarity-latency reference to an ABR component, designating a scalp-positive peak with a latency of 6 msec.
  • N90 is an example, of a polarity-latency reference to an ABR component, designating a scalp-negative peak with a latency of 90 msec.
  • P300 is an example of a polarity-latency reference to an ABR component, designating a scalp-positive peak with a latency of 300msec.
  • N1 is an example of a polarity-order reference to an ABR component, designating the first acoustic nerve response negative wave polarity, or for example the Pa, Na, Pb, or Nb MLR components.
  • Standard ABR measures suffer from a number of major problems, including significant variation in signal-to-noise ratios and the influence of regions of low frequency phase cancellation upon high frequency activity.
  • AER stimuli used for measuring awareness during anaesthesia includes clicks.
  • the AEP waveform appearing in an EEG trace is visually identified by a number of waveform peaks and troughs, and is described by the latency and amplitude characteristics of an AEP.
  • the latency is normally measured in milliseconds. It is the time interval between a selected point on the stimulus signal and a selected feature on the AEP waveform, such as a peak.
  • the amplitude of the AEP signal is normally measured in millivolts, and is the difference between a peak and the following trough of an AEP waveform.
  • brain responses include AEPs and the indirect responses of the brain such as the psychological demands resulting from the situation and have little relation to the specific sensory stimuli.
  • Event-related potentials are time-locked to a specific event.
  • An event could include physical stimuli such as an auditory tone, a change in stimulus such as frequency shift from 1000 Hz to 2000Hz, a missing stimulus such as omitted from an otherwise regular chain of stimuli, or a target assigned stimulus.
  • AEPs are a subset of the ERPs. It.has been observed that 40 Hz
  • the ERP reportedly disappears during anaesthesia.
  • the ERP may contain information on the number of auditory nerves that are excited and their location on the basilar membrane,
  • Auditory sensory related AEPs are indicative of the auditory pathways from the cochlear to the cortex.
  • ERPs can be divided into two categories including direct auditory sensory related, and PSP-related potentials.
  • the auditory sensory-related AEPs are indicative of the auditory pathways from the cochlear to the cortex, while the PCPs are related to higher-level processing or discrimination brain processes related, for example, to a change detection process.
  • the slow and particularly the late ERPs are closely associated with though or perceptual based processes such as discrimination.
  • “Slow” or “late” ERPs can be elicited by many audio stimulus including the simpler tonal stimuli, to more complex speech or sentences of verbal stimuli. Auditory sensory related potentials are referred to as “obligatory" brain responses to the physical properties of the stimulus. "Novel” stimuli, including select words or a sentence or any sequence of words. Off-line averaging and analysis gesnerally support this type of auditory stimuli. The analysis depends upon the recording analysis of each of the respective speech-related-responses. Eye movements measured in an electro-oculogram (EOG) are a further type of measurable biosignal.
  • EOG electro-oculogram
  • PCP processing contingent potential
  • a subject treated with an anaesthetic substance is in a state of consciousness that can be measured using EEG apparatus and methods. Eyes may be taped closed as a precaution against premature awakening and the subsequent disastrous visual consequences for an individual being operated upon.
  • An AER based algorithm comprising EOG artefact differentiation (from legitimate AER signals) and subsequent cancellation or AER response is useful in reliable anaesthesia monitoring.
  • Figure 1 shows a typical placement of EEG electrodes and location of biosignais.
  • Figure 2a shows a typical EEG biosignal recording and a classification of waveforms.
  • Figure 2b shows a typical EEG biosignal recording decomposition analysis for anaesthesia-based AER monitoring.
  • Figure 3 shows a representation of the model of EEG biosignal analysis incorporated in the invention.
  • Figure 4 shows a representative AER data histogram.
  • Figure 5 shows the key measures for and AER-based monitoring system.
  • Figure 6 an example of the signal morphology vector set.
  • Figure 7 shows a representation of steps of a hierarchical awareness detection sequence
  • Figure 8 shows a schematic diagram of the steps taken in depth-of- anaesthesia monitoring system.
  • Figure 9 shows a simple electrode placement of two electrodes.
  • Figure 10 shows an embodiment of a headgear for measuring different biosignals for measuring consciousness.
  • Figure 11 shows an embodiment of a sound-emitting device as an earpiece.
  • Figure 12 shows a schematic of an auto-calibrated AEP system.
  • Figure 13 shows a schematic of an auto-calibrated AEP system.
  • Figure 14 shows a schematic diagram including the elements of an embodiment of an awareness state monitoring system incorporate a simple EEG electrode system as illustrated in Figure 9 is shown in Figure 14 .
  • Figure 15 shows the steps used for calculating the AEP component variations when multiple component analysis processes are utilised.
  • Figure 16 shows a schematic diagram of an embodiment incorporating real- time recording of data from different imaging methods.
  • Figure 17 shows a schematic diagram of an embodiment incorporating measures of eyelid movement, EEG, and AER in response to stimuli in determining a state of awareness.
  • Figure 18a shows a schematic diagram of an embodiment incorporating the state of awareness of an individual patient.
  • Figure 18b shows output AEP and BIO indices from the method in Fig. 18a.
  • Figure 19 shows a flow-process model for implementation of the method of the invention.
  • Figure 20 shows example data from an experiment implementing an embodiment of the invention.
  • the invention provides a method for monitoring, analysing, displaying and storing a subject's state of consciousness together with physiological senses during anaesthesia.
  • the method comprises the steps measuring a biosignal such as an EEG from a being under anaesthetic, invoking a stimulus and inducing a response in the EEG signal attributable to the stimulus, combining processed continuous EEG biosignals comprising multiple AEP latency bands, including any combination from ABR to long latency signals processed for the derivation of cognitive status, including PCP or hearing functions, induced by a stimulus.
  • the multiple AER bands can comprise any group or combinations of a first (0-5 ms), fast (2-20 ms), middle (10-100 ms), slow (50-300 ms), or late (150-1000 ms) latency AER waveforms.
  • the processed AER can comprise any combination of stimuli test paradigms along with typical AEP responses including: a) transient responses capable of eliciting AEP signals reflective of eighth nerve CAP, ABR waves including I, Il , Ill, IV, V, middle-latency AEP (MLAEP) waves including Na, Pa, TP41 , Pb, Nb, slow "vertex" potential waves including P1 , N1, P2, N2, mismatch negativity (MMN), Processing Negativity (Nd), N2b, P3a, or P3b N400, P600; or b) steady-state responses capable of eliciting AEP signals reflective of cochlear microphonic signals, frequency following response, greater than 60 Hz auditory steady state response (ASSR), 40 Hz
  • the method includes the use of any combination of latency band and related functions including first latency AEP signals (0-5 ms); anatomy of the cochlear, eighth nerve, eighth nerve compound action potential (CAP); fast latency AEP signals (2-20 ms); auditory brainstem response (ABR) waves I, II, 111, IV, V; early cortical or middle-latency signals (10-100 ms): MLAEP: Na, Pa, TP41 , Pb, Nb);slow "Vertex” latency AEP signals (50-300 ms); P1, N1, P2, N2, and late latency AEP signals (150-1000 ms); mismatched negativity (MMN), processing negativity (Nd), N2b, P3a, P3b, N400, P600.
  • first latency AEP signals (0-5 ms
  • fast latency AEP signals (2-20 ms
  • the method preferably includes the step of making one or more independent or integrated decisions representative of a state of a living being's consciousness state or graduations thereof, that can be derived from the said AEP processes or signals.
  • the method preferably includes the step whereby at least one processed continuous EEG signal is incorporated into an index or indices indicating the depth of anaesthesia.
  • the method preferably includes a step whereby at least one of said AEP latency band's processing results enables the determination of AEP signal quality and/or integrity and/or validity.
  • the invention provides a method or apparatus for monitoring and/or processing and/or generating output measures of a patient's physiological data
  • said device or method comprises the steps of acquiring physiological signals from a live body from at least one EEG channel, computing at least one EEG channel whereby the continuous EEG signal and at least one evoked potential is computed from the at least one EEG channel, whereby the evoked potential includes any combination of somatosensory, auditory or visual evoked response.
  • tha method or apparatus for monitoring and/or processing and/or generating output measures of a patient's physiological data of claim includes a single index derived from the combination of one or more monitored channels of physiological data.
  • the method or apparatus includes means to combine one or more channels of physiological data using a third analysis combining or mediation processing means, whereby said combining or mediation processing means enables the optimal weighting or transition of weighting from two or more separate analysis processes.
  • the mediation determination is derived from the patient monitored physiological data.
  • the mediated determination method comprises at least one separate and unique processing characteristic enabling some degree of independent mediation analysis.
  • the mediation determination comprises of the optimal weighting of two or more separate processing methods.
  • the invention provides a method or apparatus incorporating AEP test and measurement capability suitable for real-time or near-real-time monitoring of a subject during anaesthesia, sedation and other applications involving the determination of depth-of-consciousness conditions, and where said test paradigms include a series or combination of stimuli capable of stimuli-related AEP response eliciiat ⁇ on reflective of auditory pathway functionality, afferent neural functionality, and/or processing contingent potentials reflective of states of attention; awareness or probability of memory recall.
  • test paradigms include a series or combination of stimuli capable of stimuli-related AEP response eliciiat ⁇ on reflective of auditory pathway functionality, afferent neural functionality, and/or processing contingent potentials reflective of states of attention; awareness or probability of memory recall.
  • apparatus includes any combination and sequence of AER stimulus elicitation test paradigms programmed in a sequence whereby such a sequence enables near-real-time computation of said responses while incorporating optimal temporal sequencing and distribution of said stimuli, presentation to an individual whereupon the Butler effect is measured by way of context analysis capable of determining whether said AER change is reflective of habituation after a change in stimulus or alternatively whether said AER response change is likely to be related to the conscious-linked or attention -linked higher order processing contingent potential AER related signals, any combination and sequence of AER-eliciting stimulus test paradigms programmed in a sequence where by such a sequence enables near-real-time computation of above said responses while incorporating optimal temporal sequencing and distribution of said stimuli presentation to an individual whereupon overlap effect, whereupon one elicited stimulus can have an excessive impact on the next elicited stimulus causing unacceptable or ambiguous AEP measures, thus enabling herarchial analysis capable of processing an individual's AEP in order to differentiate various levels of consciousness, associated with level of PC
  • the multiple AER bands comprise any group or combination of a digitally or analogue-recorded verbal responses whereupon any sequence or combination of predetermined AEP stimuli comprising verbal, sound, odd-ball, and or deviant responses can be presented to an individual under investigation.
  • said verbal, sound, odd-ball, or familiar individualized attention-sensitive verbal or other sound stimuli comprises a recording of a voice expressing a familiar sound or name according to the audio "cocktail effect" and the higher likelihood of an individual's response to a more familiar sound or name such as one's own name and possibly even the voice recorded from one's own relative or close contact.
  • said known or familiar word or phrase or sound is suited .
  • EEG event related response measurable by EEG
  • Such means of elicitation of a response can be integrated into one or more otherstimulus paradigms.
  • Each of said stimulus paradigms is preferably suited to increasingly higher levels of processing and the increased levels, of processing are related to increased latency of the AEP response to a stimulus.
  • the integrated stimulus test paradigms include a click stimulus which is more robust and less susceptible to inter- and intra-patient variables, and also novel responses punctuating said click stimuli in such a manner that the time between said click stimuli and spaces are distributed in a manner where appropriate response-time measures are able to be derived from the stimuli (latency time may be 300 ms versus the click time of 150 ms, for example), while minimizing the "overlap" effect whereupon several stimuli-elicited responses overlap and cause ambiguous AEP measures.
  • the stimuli are presented in order to produce a hierarchical range of markers processed analysis in which first (0-5 ms), fast (2-20 ms), middle (10-100 ms), slow (50- 300 ms), and late (150-1000 ms) latency AER waveforms are measured.
  • the processed AER can comprise any combination of stimuli test-paradigms along with typical AEP responses including stimuli capable of eliciting any sequence or combination of AEP signals including eighth nerve CAP; ABR waves including I, II,III, IV, V; middle-latency AEP waves including Na, Pa, TP41 , Pb, Nb; slow vertex potential waves including P1 , N1 , P2, N2; mismatch negativity; and/or speech response.
  • the invention provides apparatus and methods for monitoring the state of consciousness for patients undergoing anaesthetic treatment or other transitions between consciousness states.
  • a previous invention for generally monitoring the state of consciousness of a sentient being was described in PCT/AU2002/0077B, published as. WO02100267, which is incorporated herein by reference.
  • the invention exploits the property that attributes of AER biosignals are more sensitive and more specific of brain function than continuous EEG measures commonly known in the art for determining the state of consciousness, particularly while under anaesthesia. Said AER measures can be synchronised to a specific brain-function response directly related to the stimuli-elicited response.
  • a continuous EEG signal includes a broad spectrum of neural control signals comprising autonomic and involuntary biological functions and exogenous and endogenous functions.
  • the invention provides apparatus and means to incorporate an arbitration/mediation/facilitation analysis step into the method of analysis of EEG biosignals acquired from a subject in a state of consciousness such as under the effect of an anaesthetic.
  • the method can be carried out in real-time or near-real-time.
  • the method incorporates a signal integrity determination within the dynamic algorithm compensation method.
  • the method of the invention further incorporates an analysis adaptation method whereby linkages between algorithm rules and intra- and inter-subject variables modify any combination of sensitivity, specificity and/or analysis processing rules, thresholds, or ranges.
  • the method includes the capability to separately determine at an earlier point, than otherwise possible without independent arbitration/mediation/facilitation analysis, the optimal point prior to an AEP signal being rendered ineffective at which time EEG analysis should be relied upon. Accordingly the independent arbitration/mediation/facilitation analysis is then able to similarly determine the optimal point when the AEP signal integrity and validity re-emerges to a state . where the AEP analysis can against be relied upon.
  • the invention exploits the measurable biosignais elicited in EEG from different sources within the brain. It advantageously incorporates AEPs characteristic of any one, or a combination of, biosignal indicators of auditory, acoustic nerve, and brain function.
  • the invention incorporates analysis of refractory or sensory lower-order processing processes, such as ABRs, associated with detection of difference in physical stimuli which may be associated with the direct auditory cortex processing regions, and signals associated with the higher order processing or more central brain functional processing.
  • the invention exploits the information that can be extracted from middle latency and late latency ERPs or ML-AEPs and LL-AEPs.
  • the invention includes apparatus and methods for using of ML to LL-AEP stimulus and measures as markers of anaesthesia awareness or consciousness.
  • the N1 waveform may be associated with auditory discrimination.
  • the N1 waveform also may be associated with somatosensory processes such as response to incisions. It may also be associated with responses to touch.
  • Other biosignals may be associated with different phenomena.
  • the amplitude of the N1 waveform may be associated with a change in the state of awareness.
  • the invention includes embodiments utilising these biosignals.
  • the invention also exploits the transitional waveforms of such EEG signals that are compatible for analysis as a processing contingent potential (PCP) for determination of a consciousness state.
  • PCP processing contingent potential
  • the invention exploits a plurality of AER early-awareness detection-measures in a hierarchical detection sequence, shown in Figure 7, for example, starting with a hearing sensory test and measurement validation, followed by refractoriness level, and mismatched negative (MMN) measures as a marker of PCP linked to attention or awareness during anaesthesia or other suitable state of consciousness.
  • MNN mismatched negative
  • the invention includes methods and apparatus that incorporate the surprising knowledge that ML-AEP are not linear processes but are non-linear signals, having a presence or absence depending on the altered physiology occurring in transition from one conscious state to another.
  • ML-AEP responses can be reflective of awareness, consciousness depth or attention level.
  • the invention incorporates a hierarchical method of processing signals to arrive at an index of the state of conscious, in particular, the transition to awareness from an anaesthetic state.
  • the invention includes monitoring of the TP41 biosignal in an EEG.
  • the EEG data is transformed using Laplace calculations to determine the TP41 biosignal in the EEG.
  • Stimuli may be somatosensory; such as touch or pressure or squeezing and the like; sounds such as filtered pink noise, click stimulus, steady state stimulus, variations in stimulus onset times, variations in stimulus offset times, variations in stimulus plateau time; different combinations of deviant and standard stimulus ratio.
  • a stimulus is an auditory stimulus
  • the stimulus includes a 40 Hz sound.
  • the 40 Hz sound is a steady state sound, inducing an AER, i.e, an auditory steady state response
  • the invention includes methods to incorporate that an ERP evoked by a 40 Hz sound may not be present during a state of anaesthesia. Alternatively, or in addition, 80 Hz sounds may be used as the stimulus in practicing the invention.
  • the invention includes monitoring changes in the relationships between measurable biosignals over time.
  • the N1 biosignal can be influenced by changes in amplitude, frequency or special source of auditory stimuli, or by speech stimuli.
  • changes in frequency such as from 250 Hz to 1000 Hz elicit greater increases in N1 amplitude than do frequency changes from 2000 Hz to 4000 Hz
  • N1 is evoked from abrupt changes in stimulus level, for at least a minimal period of time.
  • the slope of the stimulus change influences the N1 amplitude and latency. Changes in the interstimuius interval produce substantial effects on N1-P2 amplitude.
  • the invention includes measurement of EEG and AEP of more than one stimulus, A considerable amount of N1 amplitude reduction with repetitive stimulus is attributed to greater refractoriness.
  • An important property of repetitive stimulus is that N1 amplitude substantially recovers or re-emerges after a change in frequency or other parameter. This recovery effect has been described in terms of the degree of overlap between two groups of neurons activated by the two dissimilar stimuli. Stimuli which deviate significantly from the ongoing stimuli have been described as activating or "fresh" elements and subsequently elicit a larger N1. This, effect has been referred to as "N1 effect” or “Butler effect”.
  • the invention includes calculation of the Butler effect from biosignal data.
  • the Butler effect is similar to the effect known as habituation, wherein an attenuation of an AER signal occurs after an initial commencement of repeated stimulus presentations. Habituation is related to the initial presentation of a stimulus, while the Butler effect may occur at any time associated with changes in the characteristics of a stimulus.
  • the invention also incorporates the calculation of habituation effects.
  • Awareness may be associated with increased muscle activity as well as changes in brain activity.
  • the invention includes the use of biosignals, other than EEG, such as EMG signals for monitoring muscle activity as a measure of awareness.
  • the invention includes monitoring of both EEG and EMG biosignals.
  • the invention uses the AER signals from an EEG recorded from electrodes placed on the scalp of a patient under anaesthesia.
  • a typical EEG electrode system is shown in Figure 1 , which shows a 10 to 20 electrode EEG measurement system. It will be understood that the objects of the invention can be achieved with much fewer electrodes.
  • Typical measured EEG biosignals are shown in Fig. 2a and 2b, which are illustrative of the classification system for the temporal sequence of waveforms of biosignals in a single EEG waveform. It will be understood that each electrode of Figure 1 may output an EEG waveform with properties characteristic of the underlying physiological activity. Any appropriate channel, can be monitored.
  • channels monitored are Pa, about 25 msec, TP41, about 41 msec, P1, about 50 msec, Pb, about 50 msec, N1, 80 to 100 msec, N2, and P2, 180 to 200 m sec from response commencement after stimulus are monitored.
  • the model incorporated in the method of the invention is shown in Figure 3.
  • a stimulus S 0
  • the EEG trace indicated, at the AER amplitude, has certain characteristics, shown as Ng 1 at spaced timed intervals, indicative of the source of the biosignal.
  • Figure 4 further shows how the amplitude of a measured biosignal, incorporating its polarity, is analysed.
  • Figure 5 shows the timeframes, sources, and characteristics of AER responses to an external stimulus used in the method of the invention.
  • Figure 6 how the signal morphology is determined for parameters from an EEG trace.
  • Figure 7 shows a representative hierarchical awareness- detection sequence starting with a hearing sensory test and measurement validation, followed by refractoriness level, and finally MMN measures as a marker of processing contingent potential as linked to attention or awareness during anaesthesia.
  • MMN measures as a marker of processing contingent potential as linked to attention or awareness during anaesthesia.
  • the invention includes many embodiments of biosignal acquisition apparatus and methods.
  • One embodiment includes measuring EEG biosignals.
  • the measurement of an EEG biosignal may be as simple as including an electrode placed appropriately on the forehead as an active electrode with a reference electrode placed below an ear as illustrated in Figure 9.
  • the signals from the active and reference electrodes are used to calculate the EEG biosignal by difference.
  • Other embodiments include measuring other biosignals than EEG. This is shown in Figure 10 where a plurality of sensors can be used to measure a plurality of biosignals for determining the state of consciousness of a being.
  • Each sensor may be incorporated into a suitable headgear as indicated in Figure 10 in a suitable position for measuring the target biosignal.
  • Indicated in Figure 10 are also the appropriate positions for sensors measuring biosignals including EEG signals Pa, Pb and Fz for drowsiness indication; TP41 and Fp1 and Fp2 for cortical arousal, a nasal reference, EOG, blinks with tape or other means for incorporation of the sensor, EMG, and a reference electrode.
  • the stimulus for evoking a biosignal response is preferably an auditory stimulus evoking an AEP response.
  • the invention includes the use of AER stimuli including clicks, pink noise capable of providing frequency compensation or masking of stimulus noise.
  • an AER stimulus may be somatosensory, including touching the subject to induce the AEP.
  • Said AEP may be evoked by any suitable sound emitting means.
  • the sound- emitting means may be an earpiece, such as that illustrated in Figure 11.
  • a sound-emitting earpiece may be operable using any of a battery, whether or not rechargeable, connected by wire, or having wireless control.
  • the monitoring system may have an auto-calibration control system that includes input from a sound-emitting means and an AEP monitoring means.
  • An embodiment of such an auto-calibration system is shown in Figure 12, the system including a means to monitor an EEG signal, a sound-emitting means, and an AEP control system to control the output of the sound-emitting means.
  • the sound-emitting means is an earpiece. It will be understood that out sound-emitting means may be used.
  • the auto-calibrated AEP control system is further illustrated in the embodiment shown in the schematic diagram in Figure 13. One input is a white noise generator. Another is the EEG signal. The AEP signal is extracted through signal processing.
  • the signal from the white noise generator is filtered with suitable filters according to an automatic filter control, which is determined from the AEP audio stimulus control change requirements.
  • the automatic filter controlled is subjected to automatic gain control.
  • the output is the AEP stimulus signal control output.
  • FIG 14. A schematic diagram showing the elements of an embodiment of an awareness state monitoring system incorporate a simple EEG electrode system as illustrated in Figure 9 is shown in Figure 14.
  • the embodiment includes the measurement of a continuous EEG signal from an electrode conveniently placed on the head of a subject between the upper forehead below the hairline and a reference electrode placed at the mastoid A1 or A2 reference.
  • a defined stimulus is characterised and applied to the subject.
  • the AER is measured as the continuous running average grand mean signal . extraction. This is subject to AER independent component analysis and then normalised with input previously recorded for the subject.
  • the AER independent components are calculated and the variation during the progression of anaesthesia or other brain state is recorded.
  • the absolute value and variation in Pa values is recorded and used for an anaesthesia awareness probability computation;
  • Figure 15 shows the steps used for calculating the AEP component variations when multiple component analysis processes are utilised.
  • the method tracks each signal by taking into account the variation in signal-to-noise ratio of each calculated AEP output parameter.
  • the invention includes combining data from different sources of biosignals, such as spatial imaging technologies, with EEG data to determine the state of awareness of a subject. This is illustrated in Figure 16.
  • FIG. 17 Further sources of biosignals such as EMG or eyelid movement may be incorporated into the measure of awareness of a subject.
  • the state of awareness is determined by input from eyelid movement [Block 1], continuous EEG [Block 2], AER running grand mean [Block 3], and brain awareness function stimulus interrogation [Block 4].
  • the AER running grand mean is determined with artefact reduction and input into a stimulus test regime [Block 5], as is a brain awareness function, which may be calculated from any of a plurality of pre-determined stimulus testing regimes.
  • the stimulus testing regimes include different and appropriate criteria differing according to the stage of awareness.
  • the level of alertness may be determined with the elicitation of the ML and higher processing levels including "odd ball"-type responses. If the subject is in a state of transition to awareness, the level of alertness may be determined by a higher-order processing method. If the subject is in a state of unconsciousness, the level of alertness is determined by are range of ML, or alternatively, BSER responses: The outputs of the stimulus test regimes are combined with the output of the continuous EEG awareness determination [Block 6] to determine which of a plurality of overall states of awareness the subject occupies [Block 7]. The AER awareness stage is then determined to be one of conscious, transition from conscious to unconscious or vice versa [Block 8] with the AER running grand mean [Block 9].
  • Figure 18a shows a schematic diagram an embodiment of the steps in operation of the method of the invention for an individual subject analysis.
  • the method starts with a stimulus, in this example, being at 6.8 Hz noise, which stimulates and MLAEP response in the subject.
  • a stimulus in this example, being at 6.8 Hz noise, which stimulates and MLAEP response in the subject.
  • the analyses undertaken in relation to thresh holds and signal processing as hereinbefore described according to a pre-defined AER thresh hold or change the stimulus is generated.
  • Resultant signals are collected and analysed according to the methods herein described.
  • Two indices are calculated for specific time intervals, denoted A to J, one index for AEP and one index for BlC, the plots of which are shown in Figure 18b. It can be seen in Figure 18b that the two indices diverge at various time stages.
  • the method of the invention exploits this divergence in the hierarchical analysis hereinbefore described.
  • the method arbitrates between the two
  • Figure 19 shows an embodiment of the method of the invention including an algorithm for collecting, processing, and displaying data for EEG, AER, and feedback control.
  • the biosignal may be transformed according to the Laplace transformation to achieve improved signal extraction.
  • the recorded biosignals measured are analysed for extraction of individual components (such as ICA) and appropriate signal to noise (such as Fsp).
  • the baseline for biosignals is determined over 50 to 100 msec preceding the stimulus.
  • the invention includes the use of disposable electrode for AER recording during anaesthesia. Ideally an electrode is located at temporal placement positions.
  • the invention further includes the use of a prompting or alerting means to indicate the status of the means for acquiring a biosignal such as an EEG, EMG, or EOG.
  • the prompting or alerting means includes a device to monitor the output of the means for acquiring the biosignal, in particular, an electrode. When no signal is communicated from an electrode, the prompting or alerting means indicates the status of the electrode.
  • a prompting or alerting means includes a suitable light-emitting device of a suitable colour, or a sound- emitting means. For example, such a means could be incorporated into the headgear as illustrated in Figure 10.
  • the prompting or alerting means and operator may check the status of the awareness-monitoring apparatus described herein to ensure that the optimal number of biosignals is being acquired and that the optimum signal-to-noise ratio for each biosignal is achieved to ensure the optimal operation of the apparatus.
  • the invention includes the use of different techniques to calculate said index of state-of-consciousness from markers herein. This may include independent component analysis or discrete component analysis, or wavelet analysis.
  • the apparatus includes temporal electrode for the optimal measure of the TP41 biosignal. This is used to differentiate the Pa and Pb signals.
  • the apparatus may be used by an ECG sleep-disordered-breathing patient as a means to acquire an accurate sleep-state using any combination of actigraphy, rv, and conventional sleep parameters.
  • The. method includes the use of P3 long-latency endogenous effects as a marker for awareness, consciousness or level of sedation. If a subject is asked to listen for an "oddball" response that elicits the maximal level of anticipation of prediction that a reliable and durable P3-response is inevitable if the subject is an aware or alert state, particularly in the response is verified with 20 to 30 random verbal subject-name-specific responses.
  • the invention further includes that the number of sequential P3 responses to such a subject- specific verbal command is likely to give some measure of the actual level of awareness of consciousness during sedation or anaesthesia.
  • the invention provides that a microphone or combined sensor placed in a subject's ear serves to record breathing sounds, as well as ear temperature and possibly Sp) @ , heart rate, HRV, actigraphy, movement, light status, position and stimuli evoked response for alertness or sleepiness. Temperature and pulse may be detectable from infra-red sensor.
  • the invention provides that AEP-independent component signal-to-noise levels may be extracted from the data.
  • An object of the present Invention is to minimise the variables associated with AEP recording. Innovations such as battery operated and wireless enabled AEP earpieces will help reduce the patient set-up time and reduce the restrictions associated with tethered connection to equipment or entanglement associated with earpiece connection wires or tubes.
  • Automatic calibration procedures can reduce the normal inconsistencies associated with hearing variations between different subjects. These hearing variations between subjects could be associated with a subject's auditory function, placement of an audio-stimulus earpiece or even factors such as blocked ears due to colds or excessive waxed material within a subject's external hearing paths.
  • the method provides a means to activate an auditory click via wireless remote control and to vary such click amplitude and frequency constitution to allow the optimisation of the AEP signal responding from such stimulus. In this manner, variations evident between different subject's hearing function can, to some degree, be minimised.
  • Minimisation of variances associated with. different subjects' hearing functions include automatic calibration of optimal dynamic range characteristics, frequency response and gain characteristics of a specific subject's hearing circuit.
  • the hearing circuit during AEP monitoring includes the AEP stimulus, the subject's hearing characteristics, the subject's cochlear and nervous system response to such audio stimulus, the surface EEG conduction of the AEP, and, the recording system signal-to-noise ratio and other AEP-recording system related characteristics.
  • the invention provides a method and apparatus to tack, measure, modify and apply treatment comprising real-time Laplacian analysis and a means to derive cognitive or awareness-linked components such as temporal components (such as TP41 ) or T-complex components (such as Ta or Tb).
  • temporal components such as TP41
  • T-complex components such as Ta or Tb
  • an embodiment of the invention includes placing two EEG electrodes adjacent the head of a patient for monitoring brain biosignals, for example, Pa, Pb, and TP41 waveforms.
  • the output biosignals may be analysed with a technique such as independent component analysis.
  • the invention further provides apparatus or methods of enabling the stimulus presentation, and/or elicitation of AERs, and/or monitoring, and/or analysis, and/or user display related to such monitoring or analysis, and/or the storage of associated monitoring or analysis data, wherein said device or method includes:
  • the said AEP responses comprising any combination of responses reflective of hearing threshold performance, hearing frequency response performance, responses reflective of the auditory sensory system and related to "obligatory" brain responses associated with the physical properties of the said stimuli stimulating the said auditory sensory system (from the cochlear to the cortex),
  • said PCP responses comprising any combination of responses reflective of the psychological demands or higher order of processing related to a stimulus or stimulus change situation (and little related to the specific sensory stimuli), 10.
  • said stimuli can include auditory tone, click, speech, sentence, complex or simple sound, change in train of stimulus, omitted or "oddball” stimulus, or change in stimulus sequence; or target designated stimulus, or stimulus created from filtering or pink noise, or any other stimulus,
  • the said AER comprising any combination or number of a subject's neurologically generated responses, .
  • the said AER comprising any combination of hearing threshold performance, hearing frequency response performance, or any responses reflective of the auditory sensory system and related to "obligatory" brain responses associated with the physical properties of the said stimuli stimulating the said auditory sensory system (from the cochlear to the cortex),
  • the invention provides a method and means to characterisation of AEP signal using automatic tracking of independent component analysis, FFT or other signal component-extraction methods along with pre-determined signal to noise thresholds to enable analysis probability computation.
  • spectral analysis such as FFT, half-period amplitude analysis, or phase (bicoherence analysis) or independent component analysis may be used.
  • the invention may include means to dynamically track and target AER components.
  • the determination of awareness is also shown to be supplemented by the detection analysis of continuous EEG and the detection of eye opening effort.
  • the invention also includes a unique AER-based anaesthesia mismatched negative PCP measuring method.
  • the response to changes in MMN is computed by the following method:
  • N1 using, say, 10 msec average amplitude or "area” measures using either high-pass or pre-stim ⁇ lus.
  • base-line calculations referred to latency range of nominated range of say 80 to 100 msec (adjustable variable but set at this default value).
  • MMN change as a) either sensory or refractory, or (AEP) b) brain response to change in stimulus and /or task (PGP) based on:
  • N1 value changes by- X % (adjust variable of change threshold based on empirical normalative clinical data) set over a defined period (set range from X msec to Y msec based on empirical clinical normalative data), ignore MMN measures from one. PCP MMN otherwise.
  • Patient sample About 200 patients were presented to theatre each week. Of these 200 patients about 60 were deemed eligible and of these 60 eligible patients, 1 patient was randomly selected for investigation each week, until the 20 patient studies were completed.
  • Patients Included: those undergoing surgery, under general anaesthesia, with an expected medical procedure duration of about thirty to ninety minutes.
  • Patients Excluded: those with neurological disorders, psychiatric disorders, hearing abnormalities, taking anticonvulsant medication, taking medication affecting the central nervous system (CNS), aged below 18 or above 75 years, with a history of alcohol or drug abuse, and undergoing head and neck, surgery or neurosurgery.
  • CNS central nervous system
  • the anaesthetist also provided normal procedural care to the patent during the period of the study, including the monitoring of vital signs, gauging of consciousness depth using BIS, and observation of visual signs (such as patient movement, response to verbal command, and the opening of eyes).
  • a clinical investigator was responsible for ensuring correct lead placement, data quality, and managing the data recording systems. Prior to the commencement of anaesthesia, the recording of the output data from both BIS and Compumedics/Neuroscan systems was initiated and the initially recorded data was denoted as the baseline conscious values. From the time of injection of anaesthetic drugs, patients were asked at 20 sec intervals to open their eyes.
  • Electrode preparation Electrode sites were cleaned using Nuprep abrasive gel and the skin was then wiped with alcohol and allowed to dry. The AEP EEG was recorded using three Ag/AgCI, 200 Medi-Trace Mini electrodes. The electrodes were placed on the left mastoid (A 1 ;active); the right mastoid (A2;ground), and centre forehead (F PZ ;reference). At the beginning of each study the electrode skin impedance was verified to be less than 5 K Ohms.
  • AEP/EEG data acquisition device Neuroscan SynAmp (Compumedics- Neuroscan Syn Amp system manufactured by Neuroscan, El Paso, Texas & Compumedics, Abbotsford, Australia).
  • Equivalent input noises 2 uV peak-to- peak maximum; Input amplifier common mode rejection ratio: 100 dB; Channel sample rate: 20,000 Hz; Channel high-pass filter type: 0.05 Hz; Channel low-pass filter: 3000 Hz; Channel notch filter type; 50 Hz; Gain: 1000; Channel sensitivity: (maximum peak-to-peak input voltage): 5.5 mV; A/D accuracy: 7nV (16 bit A/D resolution); Isolation voltage and type: Linear optical signal Isolation.
  • Audio stimulus device STIM audio system manufactured by Neuroscan.
  • BlS Aspect monitor data download The Aspect monitor's BIS data was downloaded periodically every 1 second via a serial port interface with Compumedics' system (using a serial data file capture program). Each data packet was time-stamped, enabling BIS to be synchronised with the separately recorded AEP stimulus signal, and the related EEG data,
  • Event 1 start of study and baseline, or first recorded data
  • Event 2 15 secs before induction of anaesthesia
  • Event 3 the start of anaesthesia
  • Event 4 loss of response to verbal command
  • Event 5 30 sec before skin incision
  • Event 6 time of first incision
  • Event 7 30 sec after skin incision
  • Event 8 5 min after skin incision
  • Event 9 at the end of surgery and anaesthesia
  • Event 10 3 min before eye opening
  • Event 11 1 min before eye opening
  • Event 12 at the time of eye opening
  • Event 13 at the time of removal of laryngeal mask airway (LMA) and the end of the study.
  • LMA laryngeal mask airway
  • anaesthetic agents were discontinued and patients were asked at 30-second intervals to open their eyes.
  • the return of the response to commands was denoted as the transition, from unconsciousness (UNCO) to consciousness (CO). Once the patient was conscious they were transferred to the recovery area, for continuation of their routine care.
  • a graphs (A1;A8) were computed with patient 13 AEP Differential Analysis (AEP DA ) applied to AEP gwm across the total study period data, for selected latency time bins (LTB;Na-N1) , while column C also presents the mean and SD values for the AEP DA analysis method for selected LTB, but as applied to predetermined events 1 to 13.
  • Column B graphs (B1:B8) were computed with patient 13 AEP Power Distribution Analysis (AEP PD ) applied to AEP gwm across total study period data, for LTB ranges, while column D also presents the AEP PD mean and SD values for analysis for selected LTB, but as applied to events 1 to 13.
  • the AEP PD , AEP DA and AEP Index (G) functions were applied to consecutive AEP gmw for each of 16 patient studies.
  • the respective analysis and patient data sets were generated across the series of 140 msec AEP gwm events to produce average results for each consecutive 5 msec time bin.
  • the average result for each group of 5 msec time bins within the range of each LTB segment was then computed.
  • AEP grand mean waveform (AEP gmw ): EEG data was recorded with a sampling rate of 20, 000 Hz along with each sequential click stimulus event time (6.8/sec). EEG data filters were configured to 0.05 Hz high-pass, 3000 Hz low-pass and 50 Hz notch. The 20,000 Hz was then down-sampled to 1818 Hz by averaging each successive sequence of 11 samples (20,000/11), thus generating a sample rate of 1118, which in turn represented 266 data sample points for each stimulus click (-1818/6). The first 256 data points from the said 266 points (representing a maximal AEP latency of 140 msec) were then stored as successive AEP single sweep data arrays' (AEP s ).
  • AEP AF artefact sweep
  • AEP gwm "running" AEP average
  • AEP AF artefact contaminated AEP gmw
  • AEP gmw ⁇ grand mean (average) wave AEPs- single sweep auditory evoked potential AEP [k ] - raw AEP waveform at the instant k ⁇ t - time interval between the audio stimulus clicks
  • AEP Index is a measure of overall AEP gmw "curviness” or in other words a measure or mathematical derivative, reflecting the waveform deflection (neural activity) for morphology of the AEP signal. It is calculated across select AEP gmw latency time bins (LTB), by computing the sum of the square root of the absolute difference between every two successive data samples of the AEP waveform.
  • AEP Amplitude Differential Analysis (AEP D ⁇ ): In contrast to AEP Index (G) the AEP DA analysis method looks at a range of individual LTB segments.
  • ADP DA reflects the amount of waveform deflection within each of the LTB (Na, Pa, TP41, Pb, N1 , ⁇ N1 , >N1 and 0-140 msec). This measure is proportional to the AEP gmw waveform deflection, which in turn is related to the underlying neural synchronised activity.
  • the 140 msec AEP gmw data array was divided into 5 msec time period "bins", AEP DA analysed and results averaged for each LTB segment.
  • BIS Index As noted this data was directly accessed from BIS monitor.
  • the bispectral EEG analysis Incorporates intra-component relationships such as power coupling within the EEG signal [31].
  • Multi-channel EEG studies High density multiple channel recordings are likely to be impractical for routine clinical anaesthesia consciousness monitoring. However, it is also evident from this study that further investigation into high density topographic functional EEG AEP recordings, accompanied by various stimulus paradigms, and conducted during anaesthesia or sleep may be required to determine optimal neural source electrode location, analysis, and associated correlates of awareness and anaesthesia consciousness.
  • Main supply interference Mains interference in this study (50 Hz) and the 3 rd harmonic (150 Hz) proved to constrain the accurate interpretation and measurement of AEP amplitudes with an adverse impact on accuracy of both AEP PD and AEP DA .
  • Filter characteristics have proven to be a significant consideration for the analysis or AER neural sources or associated regions.
  • Filter delay The delay characteristics of FIR filters utilised within this study generated delays of the order of 10 msec. These filter delays can have a significant effect when measuring waveform voltages across latency regions of 0 to 10 msec. In particular these filter delays can distort the accuracy of independent AEP components related measures (such as amplitude), especially as is the case in this study where such measures are neurally synchronised within a range and resolution often critical to a few msec or less of resolution. In this study we were able to identify this problem and subsequently compensate our results in order to diminish the impact of such delays.
  • Filter Linearity Filter linearity can be an issue. This factor must be understood. Correctly designed filters (as already noted) such aa FlR filters with insignificant phase distortion are an important design consideration.
  • EOG artefact proved to be a significant concern, particularly at the commencement of the studies where the presence of high EOG activity associated with highly alert and conscious states was evident. However, in a practical sense the patient's conscious state EOG artefact was not as serious a concern as the potential contamination associated with EOG artefact during more critical unconscious states (such as during surgery), or consciousness transitional states..

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Abstract

L'invention concerne un dispositif et des procédés servant à déterminer l'état de conscience d'un sujet, notamment d'un sujet soumis à un traitement anesthésique, et comprenant l'acquisition de données physiologiques incluses dans des biosignaux tels que des mesures EEG. Le dispositif selon l'invention comprend des moyens pour l'acquisition d'au moins un biosignal continu, de préférence un signal EEG, ainsi que des moyens pour stimuler au moins un signal de potentiel évoqué dans le signal EEG, tel qu'un potentiel évoqué auditif (PEA) ou une réponse évoquée auditive (REA), et pour analyser les caractéristiques de ces signaux afin de déterminer un index pour les caractéristiques. Le stimulus pour une REA peut consister en tout stimulus approprié, notamment un stimulus de type clic ou des mots parlés. Il peut également s'agir d'un stimulus somatosensoriel. Les réponses de potentiels évoqués particulièrement intéressantes sont les réponses de latence moyenne. L'index est calculé lors d'un processus de classification et de pondération qui arbitre entre l'importance des signaux en fonction de l'état de conscience. L'index peut intégrer des informations provenant de biosignaux acquis à partir du mouvement musculaire en plus des signaux EEG.
PCT/AU2006/000643 2005-05-17 2006-05-17 Procede et dispositif pour surveiller la conscience pendant l'anesthesie WO2006122349A1 (fr)

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EP2191772A1 (fr) * 2008-11-26 2010-06-02 General Electric Company Mesure de la réactivité d'un sujet
EP2196144A1 (fr) * 2008-12-12 2010-06-16 Universite de Rennes 1 Dispositif de détermination d'un état de stress chez un sujet vivant, procédé et produit programme d'ordinateur correspondants
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