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Cortical Dynamics LtdCortical Dynamics Ltd is an Australian neurotechnology company focused on developing advanced brain monitoring technologies for clinical environments. The company specializes in EEG-based systems and AI-powered applications that enhance anaesthesia monitoring, aiming to improve patient outcomes and reduce healthcare costs. Its flagship solution is designed to more accurately detect the effect of anaesthetic agents and provide continuous, real-time patient state assessment to aid anaesthesiologists and clinicians.
Cortical Dynamics Ltd is an Australian neurotechnology company focused on developing advanced brain monitoring technologies for clinical environments. The company specializes in EEG-based systems and AI-powered applications that enhance anaesthesia monitoring, aiming to improve patient outcomes and reduce healthcare costs. Its flagship solution is designed to more accurately detect the effect of anaesthetic agents and provide continuous, real-time patient state assessment to aid anaesthesiologists and clinicians.
Publication Number
US-7937138-B2
Publication Date
2011-05-03
Expiration Date
Abstract
A method for assessing brain state by analysing mammalian brain electroencephalogram (“EEG”) recordings using an eighth order autoregressive and fifth order moving average discrete time equation.
Core Innovation
The invention assesses brain state using mammalian brain electroencephalogram (EEG) recordings. Digitised EEG data signal is segmented into time frames of fixed length, and each digitised time frame is approximated by a first equation comprising coefficients to be determined and a Gaussian white noise process. The fitted equation is transformed by a z-transform to obtain a second z-domain equation, and poles are determined by solving A(z)=0.
Pole positions are determined in the complex plane and plotted and displayed. For each sample, the method repeats the fitting and z-domain pole determination for each frame to determine clusters of poles in the complex plane, and it displays the positions and distribution of at least some pole clusters.
The invention uses movement of pole clusters, including movement of poles or clusters of poles, to assess brain state and to assess the efficacy of an intervention by reference to movement of displayed pole clusters in the complex plane.
Claims Coverage
The independent claims cover three main categories: a method for assessing brain state by modelling EEG frames with an eighth order autoregressive and fifth order moving average discrete time equation and analysing z-domain poles; methods for assessing brain state over time by repeating the analysis with a second EEG and assessing movement of pole clusters; and systems for displaying brain activity and assessing intervention efficacy by computing and displaying pole positions and pole-cluster distributions. Across these claims, the number of repeated inventive features centers on the specified ARMA-based equation, z-transform to a z-domain equation, solving A(z)=0 for poles, clustering across frames, and displaying pole positions and distributions on the complex plane.
Arma discrete-time EEG modelling with z-domain pole analysis
analysing mammalian brain electroencephalogram (EEG) recordings using an eighth order autoregressive and fifth order moving average discrete time equation; obtaining a z-domain equation by taking a z-transform for said eighth order autoregressive and fifth order moving average discrete time equation; determining poles and zeroes in the solution of the z-domain equation; plotting the poles onto the complex plane; and displaying positions of the poles.
Fixed-length frame approximation with Gaussian white noise and pole-cluster display
digitising the EEG to define a digitised EEG data signal; segmenting the EEG data signal into time frames of fixed length; approximating each digitised time frame by a first equation comprising coefficients to be determined and a Gaussian white noise process; solving the first equation to determine coefficients; performing a z-transform to obtain a second, z-domain equation; substituting each of the values of the coefficients into the z-domain equation; solving A(z)=0 for z in the second equation to determine the poles; determining the positions of the poles in the complex plane; repeating steps for each frame in the sample to determine clusters of poles in the complex plane; and displaying the position and distribution of at least some of said clusters of poles in the complex plane.
Brain-state assessment by repeated pole-cluster movement across first and second EEGs
obtaining a first electroencephalogram (EEG) from the brain; digitising the EEG to define a digitised EEG data signal; segmenting the EEG data signal into time frames of fixed length; approximating each digitised time frame by a first equation comprising coefficients to be determined and a Gaussian white noise process; solving the first equation to determine coefficients; performing a z-transform to obtain a second, z-domain equation; substituting each of the values of the coefficients into the z-domain equation; solving A(z)=0 for z in the second equation to determine the poles; determining the positions of the poles in the complex plane; repeating steps for each frame in the sample to determine clusters of poles in the complex plane; obtaining a second EEG from said brain at a later time; repeating steps in relation to the second EEG at least once; displaying the positions of at least some of the poles derived from the first and second EEGs respectively in the complex plane; and assessing the state of the brain by reference to movement of at least some of said clusters of poles.
Intervention-efficacy assessment using first and second pole clusters and pole movement
obtaining an electroencephalogram (EEG) from the brain; digitising the EEG to define a digitised EEG data signal; segmenting the EEG data signal into time frames of fixed length; approximating each digitised time frame by a first equation comprising coefficients to be determined and a Gaussian white noise process; solving the first equation to determine coefficients; performing a z-transform and obtaining a second, z-domain equation; substituting each of the values of the coefficients into the z-domain equation; solving A(z)=0 for z in the second equation to determine the poles; determining the positions of the poles in the complex plane; repeating steps for each frame in the sample to determine first clusters of poles in the complex plane; administering an intervention to the brain; repeating steps at least once to obtain second clusters of poles in the complex plane; displaying the positions of at least some of the first and second clusters of poles; and assessing the efficacy of the intervention by reference to movement of at least some of said first and second clusters of said displayed poles in the complex plane.
System for displaying brain activity via z-domain pole clusters
a plurality of electrodes for picking up EEG signals from the brain; digitising means for converting the EEG signals to a digitised EEG data signal; computing means for segmenting the EEG data signal into time frames of fixed length; approximating each digitised time frame by a first equation comprising coefficients to be determined and a Gaussian white noise process; solving the first equation to determine coefficients; performing a z-transform to obtain a second, z-domain equation; substituting each of the values of the coefficients into the z-domain equation; solving A(z)=0 for z in the second equation to determine the poles; determining the positions of the poles in the complex plane; repeating steps for each frame in the sample to determine clusters of poles in the complex plane; and display means for displaying the position and distribution of at least some of said clusters of poles in the complex plane.
System for intervention-efficacy assessment with display of first and second pole clusters
a plurality of electrodes for picking up EEG signals from the brain; digitising means for converting the EEG signals to a digitised EEG data signal; computing means for segmenting the EEG data signal into time frames of fixed length; approximating each digitised time frame by a first equation comprising coefficients to be determined and a Gaussian white noise process; solving the first equation to determine coefficients; performing a z-transform to obtain a second, z-domain equation; substituting each of the values of the coefficients into the z-domain equation; solving A(z)=0 for z in the second equation to determine the poles; determining the positions of the poles in the complex plane; repeating steps to determine first clusters of poles in the complex plane and, after administration of an intervention to the brain, repeating steps at least once in order to obtain second clusters of poles in the complex plane; and display means for displaying the position and distribution of at least some of said first and second clusters of poles in the complex plane in order to assess the efficacy of the intervention.
Repeated EEG pole-cluster display for later-time assessment
a plurality of electrodes for picking up EEG signals from the brain; digitising means for converting the EEG signals to a digitised EEG data signal; computing means for segmenting the EEG data signal into time frames of fixed length; approximating each digitised time frame by a first equation comprising coefficients to be determined and a Gaussian white noise process; solving the first equation to determine coefficients; performing a z-transform to obtain a second, z-domain equation; substituting each of the values of the coefficients into the z-domain equation; solving A(z)=0 for z in the second equation to determine the poles; determining positions of the poles in the complex plane; repeating steps for each frame in the sample to determine clusters of poles in the complex plane; obtaining a second EEG from said brain at a later time; repeating steps at least once in relation to the second EEG; and display means for displaying the position and distribution of at least some of said clusters of poles in the complex plane derived from the first and second EEGs.
Across the independent claims, the core coverage is based on fitting each fixed-length digitised EEG frame with an eighth order autoregressive and fifth order moving average discrete time equation with Gaussian white noise, converting to a z-domain equation via a z-transform, solving A(z)=0 to determine poles, determining pole positions and clustering pole positions across frames, and displaying the position and distribution of pole clusters on the complex plane. Additional independent claim coverage extends to repeating the analysis for a second EEG at a later time or before/after an administered intervention, with assessment based on movement of displayed pole clusters.
Stated Advantages
Enables assessment of brain state by reference to movement of clusters of poles displayed in the complex plane.
Enables assessing the efficacy of an intervention by reference to movement of first and second displayed clusters of poles in the complex plane.
Provides display of positions and distribution of at least some clusters of poles in the complex plane for EEG-based activity of a mammalian brain.
Documented Applications
Vigilance or alertness assessment by using displayed clusters of poles.
Sleep assessment.
Anaesthesia depth monitoring.
Assessing efficacy of interventions including alprazolam (benzodiazepine), placebo, and a cognitively active pharmaceutical agent.
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