ANAESTHESIA AND CONSCIOUSNESS DEPTH MONITORING SYSTEM

• US 20120277548A1
• Filed: 08/13/2010
• Published: 11/01/2012
• Est. Priority Date: 08/14/2009
• Status: Active Grant

• Current Assignee: David Burton
• Original Assignee: David Burton
• Inventors: Burton, David
• Granted Patent: US 10,595,772 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02116 of pulse wave amplitude A61B5/02225 takes precedence
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/325 Preparing electrode sites, e.g. by abrasion
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/377 using evoked responses
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4029 for evaluating the peripheral nervous systems A61B5/24 takes precedence
• CPC Class Codes: A61B 5/4047 afferent nerves, i.e. nerves that relay impulses to the central nervous system
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: A61B 5/415 the glands, e.g. tonsils, adenoids or thymus
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 : of noise induced by motion artifacts
• CPC Class Codes: A61B 5/721 : using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7285 : for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: G01R 29/26 : Measuring noise figure; Measuring signal-to-noise ratio
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

Methods and systems incorporating non-linear dynamic (NLD) analysis such as entropy or other complexity analysis monitoring continuous or evoked signals from a biological subject are presented, where such a system comprises of processing steps including: a) the combination of a biological signal evoked as a result of patient stimulation presented to a biological subject and a non-linear analysis method capable of capturing temporal changes in signal order or regularity; b) any combination of processed evoked or continuous central nervous or peripheral physiological mechanisms b) a means to generate a measure indicative of a patient'"'"'s level of anaesthesia and consciousness depth (A&CD), sedation or sleep/wake state. Methods and systems incorporating a NLD analysis means to improve the discrimination between different signals origins including any combination of: a) central nervous system (CNS), b) peripheral control or nervous system (PNS), c) autonomic control or nervous system (ANS), d) arousals, and e) artifacts.

Claims

1-681. -681. (canceled)

682. A method of transforming evoked electrical signal data from sensors using a microprocessor, including the steps of:

stimulating a subject to evoke electrical signals;

collecting and storing the evoked electrical signals into a computer file;

synchronizing said stimulus and corresponding evoked electrical signals;

determining changes or absolute values from said time-syncrhonized electrical signals by computing a non-linear dynamic transfer function across the evoked electrical signals to produce resulting data; and

storing the resulting data in at least one computer file.

683. The method of claim 682, further including the step of displaying the resulting data in a numeric or graphic representation of a subject state, the subject state comprising any of anesthesia depth;

consciousness depth;

awareness or attention;

hypnosis;

physiological responsiveness;

cognitive responsiveness;

sentient;

sedation depth;

degree of pain or analgesia;

degree of anxiolysis;

degree of hypnosis;

degree of immobility;

degree of or attention awareness;

degree and type of cognition;

degree of brain activity.

684. An apparatus for collecting, transforming and displaying electrical signal data, comprising:

a sensor for collecting evoked electrical signals;

a means for storage of electrical signal data; and

at least one microprocessor executing a computer program implementing a non-linear dynamic computation algorithm for transforming the evoked electrical signals into a marker of a biological, a neurological, a psychological or a cognitive state of a subject.

685. The apparatus according to claim 684 wherein the transfer function comprises any of:

a quantitative measure of disorder in a system;

an ability to predict future data based on previous data characteristics;

a spectral entropy;

a complexity analysis;

an entropy or a means of delineating between underlying non-linear neurological signals implicated in deep hypnosis and signals reflective of wake or conscious periods.

686. The apparatus according to claim 684 wherein the transfer function comprises a hybrid combinational analysis algorithm determination whereby inputs to said combinational analysis can comprise any of:

an electroencephalography signal;

an evoked electrical signal;

an electromyography signal;

a post auricular muscle signal;

a vital sign or autonomic signal;

or a determination of slower continuous electroencephalography measures or moving time auditory evoked potential averages combined with faster moving time auditory evoked potential averages;

whereby said auditory evoked potential determinations can comprise any of;

differential and non-linear dynamic (entropy) auditory evoked potential values computed across a wide-band of different latency intervals, including early latency (0-28 ms) signals appearing across the post auricular muscle response region, and latter latency AEP measures (80-140 ms), enabling resultant online or real-time monitoring measure(s) applicable to subject anaesthesia-depth, sedation, consciousness-depth, mobility, amnesia-propensity, or analgesia measures, wherein the combination of these slower and faster measures enable fast detection of consciousness transitions, high responsiveness to online events such as noxious stimuli, discriminators of different arousal types, detection of artifact and movement events, and discrimination between peripheral and central physiological signals;

a determination of combinational analysis parameters whereby the weighting or influence of stimulus evoked potential input data, such as auditory evoked potentials, versus continuous signals, such as electroencephalography or electromyography or vital signs is determined by from non-linear dynamic processing applied to said evoked potential data or continuous data, applicable to online or real-time measure(s) of anaesthesia-depth, sedation, consciousness-depth, mobility, amnesia-propensity, or analgesia measures;

or

a processing of EEG and EMG signals in the determination of sates of anaesthesia-depth or consciousness-depth or sedation-depth or pain-level or cognitive responsiveness whereby at least one vital sign signal such as pulse or heart rate or blood-pressure-related-variable is incorporated as part of the decision matrix and/or sais state determination in order to detect states of anxiety as measured for example by elevated oximetry pulse rate detection, where unresponsive or unconsciousness could otherwise be determined versus the determination of critical states such as muscle paralysis during intraoperative awareness.

687. The apparatus according to claim 684 wherein the transfer function comprises determining a transition between any of states of:

consciousness to unconsciousness and vice-versa;

hypnosis to awareness to attentive and vice-versa;

cognitive responsiveness to cognitive unresponsiveness and vice-versa;

muscle responsiveness to unresponsiveness and vice-versa;

or amnesia to memory recall and vice-versa.

688. The apparatus according to claim 684 wherein the transfer function comprises the determination or discrimination of online event conditions, the online event conditions comprising any of:

body movement;

movement time;

micro-arousal;

cortical arousal;

subcortical (autonomic) arousal;

artifact;

noxious stimuli events;

noxious stimuli;

noxious stimuli arousal;

noxious stimuli cortical arousal;

noxious stimuli sub-cortical arousal;

noxious stimuli body movement;

electrosurgical interference;

mains interference;

eye-movements;

electrooculography;

post auricular muscles responses;

electromyography;

external sound interference;

electrical noise interference;

electromagnetic interference;

background noise interference;

background physiological signal interference;

fast dosage changes;

pharmacodynamic dosage changes;

hypnosis state changes;

amnesia propensity state changes;

analgesia state changes;

mobility state changes;

anxiolysis state changes;

or fast vital sign changes.

689. The apparatus according to claim 685 wherein the said transfer function comprises differentiating between or improving the differentiation between EMG and EEG signals, including the intrusion of electromyography signals upon electroencephalography signals.

690. The apparatus according to claim 684 wherein the said transfer function comprises any of:

a) computation of time-series complexity analysis;

or

b) computation of AEP latency-intervaldependent analysis;

or

c) computation of absolute AEP values;

or

d)computation of square root of AEP values;

or

e) computation of power of 2 of AEP values;

or

f) computation of AEP waveform amplitude;

or

g) computation of AEP differentiated amplitude;

or

h) computation of AEP integral amplitude;

or

i) computation of AEP peak values;

or

j) computation of a first output measure from fast moving time averages (MTA), fast moving time averages (MTA) wherein the output measure incorporates a means to respond to and indicate fast-occuring transient events or online events;

or

k) computation of signal peaks within AEP data or AEP processed data by way of comparing non-linear dynamic AEP computed values with a second analysis method'"'"'s computed values;

or

l) computation of and classification of signal peaks within AEP data or AEP processed data by way of comparing non-linear dynamic AEP computed values with a second analysis method'"'"'s computed values;

or

m) computation and classification of signal peaks within AEP data or AEP processed data by way of comparing non-linear dynamic AEP computed values with a second analysis method'"'"'s computed values, the classification including any online events conditions applicable to any of;

body movement;

movement time;

micro-arousal;

cortical arousal;

subcortical (autonomic) arousal;

artifact;

noxious stimuli events;

noxious stimuli;

noxious stimuli arousal;

noxious stimuli cortical arousal;

noxious stimuli sub-cortical arousal;

noxious stimuli body movement;

electrosurgical interference;

mains interference;

eye-movements;

electrooculography;

post auricular muscle responses;

electromyography;

external sound interference;

electrical noise interference;

electromagnetic interference;

background noise interference;

background physiological signal interference;

fast dosage changes;

pharmacodynamic dosage changes;

hypnosis state changes;

amnesia state changes;

analgesia-propensity state changes;

mobility state changes;

anxiolysis state changes;

orvital sign changes;

n) slower moving time averages (MTA) wherein the output measure incorporates a means to respond to and indicate slower-occurring changes/trends;

or

o) slower moving time averages (MTA) wherein the output measure incorporates a means to respond to and indicate and indicate slower-occurring comprising of any of;

computation of gradual dosage graduations;

computation of slower or more subtle hypnosis state changes;

computation of slower or more subtle amnesia state changes;

computation of slower or more subtle analgesia state changes;

computation of slower or more subtle mobility state changes;

computation of slower or more subtle anxiolysis state changes;

computation of slower or more subtle vital sign changes.

p) computation of combined output measure incorporating values from both the said slower moving time average and the faster moving time average;

q) computation of average and the faster moving time average, wherein the third combined output measure indicates fast and slower changes from a subjects monitored signals and/or changing states corresponding to both fast and slower changing information;

r) computation of values from the said slower moving time average, the faster moving time average, and EEG-based measures;

s) computation of values from the said slower moving time average, the faster moving time average, and EEG-based measures, wherein the EEG-based measures enable a narrower frequency spectrum with correspondingly less susceptibility to interference such as during electrosurgical procedures;

t) computation of values by way of averaging a number of stimulus evoked signals in the first processing step, followed by averaging a larger number (than said first step) of stimulus evoked signals in the second processing step, and then by computing a difference signal between that of the first step and the second step as a means of enhancing the conventional noise cancelled signal resulting from averaging-noise-subtraction and the identification of neural correlates which behave differently based on increased averaging computational outcomes;

or

u) computation of values from a time series processing or from signal pattern recognition processing or from signal morphological processing;

v) The deployment of optimal combinations of faster moving time averages incorporating autogressionmodelling with an external input function and evoked potential analysis methods such as differential/first derivative analysis combined with non-linear dynamic analysis methods as a means to determine anaesthesia-depth or consciousness depth or sedation-depth, or pain-level or sentient state of a subject.

691. The apparatus according to claim 684 wherein the transfer function comprises the determination of neural source localization or the estimation of neural source localization comprising any of:

a) means of computing or estimating online or realtimeelectroencephalography anteriorisation;

or

b) means of computing or estimating online or realtimeelectroencephalography cognitive unbinding;

or

c) means of computing or estimating online or realtimeelectroencephalography lateralization or hemispherical changes;

or

d) means of computing or estimating online or realtimeneural source localization from 3 or more electroencephalography monitoring sensors;

or

e) means of computing or estimating online or realtimeneural source localization whereby said electroencephalography monitoring sensors can include frontal central (Fpz or nasion), left (Fp1), and right (Fp2) sensor registrations;

or

f) means of computing or estimating online or realtimeneural source localization whereby said frontal central (Fpz or nasion), left (Fp1), and right (Fp2) sensor registrations can enable the estimated hemispherical neural source localisation;

or

g) means of computing or estimating online or realtimeneural source localization whereby posterior (nape/Inion/Iz) sensor registrations combined with any of said (Fpz or nasion), left (Fp1), or right (Fp2) can enable anteriorisation neural source localization;

or

h) means of computing or estimating online or realtimeneural source localization spatiotemporal dynamics;

or

i) means of computing or estimating online or realtimeneural source localization spatiotemporal dynamics corresponding to monitored subject'"'"'s audiovisual processing stimulated from an external source;

or

j) means of computing or estimating online or realtimeneural source localization spatiotemporal dynamics corresponding to monitored subject'"'"'s audiovisual processing stimulated from an external source;

or

k) means of computing or estimating online or realtimebrain signal responses corresponding to monitored subject'"'"'s sound localisation cues;

or

means of computing or estimating online or realtime neural lateralisation or anteriorisation changes combined with any combination of non-linear dynamic spectral, power or other amplitude relationships, and/or phase or bispectral and related output values.

692. The apparatus according to claim 684 wherein the transfer function comprises the determination of drug delivery or drug composition or drug development comprising any of:

online delivery of anaesthesia or muscle suppressant gases or drugs;

or

closed loop control of said delivery of anaesthesia or muscle suppressant gases or drugs or final mixture or composition of said drugs whereby said composition and amount of drug delivery or usage is determined in accordance to subject'"'"'s physiological and/or psychological and/or neurological monitored parameters.

693. The apparatus according to claim 684 wherein the transfer function comprises the determination of Stimulus validation function whereby randomized versus more orderly signal characteristics can be computed by way of evoked potential non-linear dynamic or entropy factor.

694. The apparatus according to claim 684, wherein the microprocessor incorporates an algorithm to process a hierarchical series of analysis outcomes from monitored evoked response signals comprising the disassembly or decomposition of composite AEP signals into the different functional information channels representative of the lower level ABR sensory responses and/or the N1 markers indicative of the arrival of the stimulus through the cochlear and auditory nerves to the cerebral cortex (such as N1 presence) and/or the afferent neural responses (such as N1-effect) and/or elevated N1-P2 amplitudes corresponding to increased refractory period (inter-stimulus interval;

ISI) and/or MLAEP amplitude-latency changes with deeper anaesthesia and/or processing contingent potentials (PCP) and/or mis-matched negativity evoked response factors indicative of the higher order attention states indicative of the laying down of longer term memories implicated during intraoperative recall, whereby the computation of resulting neurophysiological responses can be distinguished or segregated in accordance to the said evoked response (such as AEP) psychological and physiological origins.

695. The apparatus according to claim 684, further comprising a means for a stimulus function to simultaneously monitor AEP response, computer, and servo-control stimulus generation (dynamical adjustment between stimulus generation and resulting AEP responses) AEP responses, and whereby said generation includes adjustments of stimulus rate and/or intensity and/or standard/deviant ratio and/or stimuli shape and/or stimulus spectral parameters and/or different stimulus types such as any of standard and/or deviant stimuli sequences and/or click and/or warble and/or chirp and/or tone and/or speech and/or specialised audio sequences, whereby said monitoring enables online interrogation and assessment of the monitored subject'"'"'s brain'"'"'s state of cognition and/or responsiveness and/or awareness and/or attention and/or pain and/or in the context of memory-recall functionality.