METHODS AND APPARATUS FOR MONITORING CONSCIOUSNESS

• US 20110118619A1
• Filed: 08/09/2010
• Published: 05/19/2011
• Est. Priority Date: 06/13/2001
• Status: Abandoned Application

• Current Assignee: David Burton, Eugene Zilberg
• Original Assignee: David Burton, Eugene Zilberg
• Inventors: Burton, David, Zilberg, Eugene
• Application Number:
  US12/853,262
• Publication Number:
  US 20110118619A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/6821 Eye
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The systems of the present invention provide improved accuracy in monitoring, analysing, detecting, predicting and/or providing alerts and alarms associated with depth of anaesthesia, depth of consciousness, hypnotic state, sedation depth, fatigue or vigilance of a subject, with as few as 3 surface electrodes. The systems incorporate real-time phase, amplitude and frequency analysis of a subject'"'"'s electro-encephalogram. The systems weight outputs of various types of analyses to produce an integrated analysis or display for precise indication or alert to users of the systems including anaesthetists, nurses and other medical personnel, transport drivers and machine workers. The systems weight the outputs of one or more analysis algorithms including combinations of simultaneous, real-time R&K analysis, AEP spectral analysis-SEF-MF, Bi-coherence analysis, initial wave analysis, auditory response, arousal analysis, body movement analysis, 95% spectral edge analysis and anaesthetic phase and spectral energy variance measurement in association with a subject'"'"'s state of consciousness.

Claims

1. A method of monitoring consciousness of a sentient subject and automatically detecting whether the subject is in a transition from a conscious state to a less conscious state or vice versa, by reducing effects of frequency-based changes in neurological data from the subject, said method including:

obtaining an electro-encephalogram signal from the subject;

performing a frequency-based analysis on the electro-encephalogram signal to obtain a frequency-based signal;

performing a phase-based analysis on the electro-encephalogram signal to obtain a phase-based signal;

detecting by comparing the frequency-based signal and the phase-based signal whether the subject is in transition from said conscious state to said less conscious state or vice versa; and

providing a warning signal when said subject is in said transition to said conscious state.

2. The method according to claim 1, wherein said frequency-based analysis includes depth of sleep analysis, and wherein said phase-based analysis includes at least one of optimized bicoherence, bispectrum or triple product analysis.

3. The method according to claim 2, wherein said depth of sleep analysis includes real-time optimized scoring of human sleep physiology.

4. The method according to claim 1, wherein said step of detecting is augmented with optimized audio evoked potential.

5. The method according to claim 2, wherein said step of detecting is augmented with optimized audio evoked potential.

6. The method according to claim 3, wherein said step of detecting is augmented with optimized audio evoked potential.

7. The method according to claim 1, including means for adapting the or each analysis to parameters specific to said subject, including body mass index, age and sex of said subject.

8. The method according to claim 2, including means for adapting the or each analysis to parameters specific to said subject, including body mass index, age and sex of said subject.

9. The method according to claim 3, including means for adapting the or each analysis to parameters specific to said subject, including body mass index, age and sex of said subject.

10. The method according to claim 4, including means for adapting the or each analysis to parameters specific to said subject, including body mass index, age and sex of said subject.