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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-8483815-B2
Publication Date
2013-07-09
Expiration Date
Abstract
A process for analysing an electroencephalogram signal representative of activity of a brain, including: i) generating coefficient data for a signal representation of a portion of said signal; ii) generating first gain data representing an average amplitude of an output signal generated based on said signal representation and said coefficient data; iii) generating second gain data representing an average amplitude of said portion; and iv) generating, based on said first gain data and said second gain data, brain state data representing subcortical activity of said brain.
Core Innovation
The invention displays the activity of a brain by obtaining an electroencephalogram (EEG) signal, segmenting the EEG signal into contiguous or overlapping segments, and representing each segment using a fixed order autoregressive moving average (ARMA) signal representation. The ARMA representation uses an autoregressive order between 8 and 13 and a moving average order between 5 and 11, and AR coefficient data and MA coefficient data are generated for the segments and substituted into the fixed order ARMA representation to generate signal representations for the segments.
A zero mean unit variance Gaussian random process is provided as a driving input to the EEG signal representation, and an ARMA gain value is generated as the root mean square of the amplitude values for the signal representations of the EEG segments. Brain product data representative of input to cortex is generated by dividing the root mean square of the amplitude of the EEG signal for the segments by the ARMA gain value for the signal representations.
The invention outputs and displays the brain product data on display means. The brain product data is displayed in graphical form and can be rendered using different colours for different predetermined ranges of values, and in some embodiments the brain product data is represented as an index value and includes brain response data derived from the brain product data for the activity of the brain.
Claims Coverage
The independent claims cover a pipeline that segments EEG into contiguous or overlapping segments, models each segment with a fixed order ARMA representation, uses a zero mean unit variance Gaussian random process as the driving input, computes an ARMA gain as a root mean square amplitude, derives brain product data representative of input to cortex by dividing RMS EEG amplitude by ARMA gain, and displays that brain product data on display means. The claims present five inventive features.
Fixed-order ARMA modeling of segmented EEG
Representing EEG segments as a fixed order autoregressive moving average (ARMA) signal representation with an autoregressive order between 8 and 13 and a moving average order between 5 and 11.
Gaussian-driven ARMA signal representations
Generating signal representations for said segments by substituting generated AR coefficient data and MA coefficient data for said segments into said fixed order ARMA representation, and providing as a driving input to said EEG signal representation a zero mean unit variance Gaussian random process.
ARMA gain as root mean square amplitude
Generating an ARMA gain value as the root mean square of the amplitude values for said signal representations for said segments.
Brain product data as RMS EEG divided by ARMA gain
Generating brain product data representative of input to cortex by dividing the root mean square of the amplitude of the EEG signal for said segments by the ARMA gain value for said signal representations of said EEG segments.
Displaying brain product data
Displaying said brain product data on display means.
The claim coverage centers on deriving brain product data representative of input to cortex from segmented EEG using fixed order ARMA modeling driven by a zero mean unit variance Gaussian random process, computing an ARMA gain as root mean square amplitude, dividing RMS EEG amplitude by that ARMA gain, and displaying the resulting brain product data.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Not explicitly described in patent.
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