Analysis of EEG signals to detect hypoglycaemia

A computer programmed to accept an EEG signal as an input and to perform thereon the steps of:

• obtaining from said EEG signal a plurality of components thereof each comprising a different band of frequencies;
• obtaining a measure of the varying intensity of each said component, obtaining a long term estimate of the mean of each said intensity measure and obtaining a long term estimate of the variability of each said intensity measure;
• normalising each said intensity measure by a process arithmetically equivalent to subtracting from the intensity measure the long term estimate of the mean and dividing the result by the long term estimate of the variability so as to generate from each frequency band a normalised feature;
• using machine analysis of the normalised features to obtain a time-varying hypoglycaemia cost function;
• classifying values of said hypoglycaemia cost function according to a probability of the values of said hypoglycaemia cost function being indicative of hypoglycaemia;
• integrating the probabilities obtained during a selected time period comprising a plurality of time segments,
• determining in said computer that the EEG signals are indicative of hypoglycaemia being present or being impending based on said integration;
• in response to said determining step, providing an output notification of present or impending hypoglycaemia, wherein a previous session estimate of the mean and variability is used at the start of a current session and is progressively updated as the current session progresses;
• detecting artefact time segments, from said plurality of time segments of said EEG, which contain signal contaminating artefacts by obtaining a sum of a linear or non-linear function of the normalised features using a pre-established set of weighting coefficients to thereby obtain a time-varying artefact detection cost function, and classifying values of each said artefact detection cost function according to the probability of values of said artefact detection cost function being indicative of one of said artefacts;
• and
• excluding said artefact time segments from generating events to be included in said integration.

A non-transitory computer-readable medium carrying thereon a machine instruction set containing instructions for causing a compatible computer to carry out the step of receiving as an input an EEG signal and to perform thereon the steps of:

• obtaining from said EEG signal a plurality of components thereof each comprising a different band of frequencies;
• obtaining a measure of the varying intensity of each said component, obtaining a long term estimate of the mean of each said intensity measure and obtaining a long term estimate of the variability of each said intensity measure;
• normalising each said intensity measure by a process arithmetically equivalent to subtracting from the intensity measure the long term estimate of the mean and dividing the result by the long term estimate of the variability so as to generate from each frequency band a normalised feature;
• using machine analysis of the normalised features to obtain a time-varying hypoglycaemia cost function;
• classifying values of said hypoglycaemia cost function according to a probability of the values of said hypoglycaemia cost function being indicative of hypoglycaemia;
• integrating the probabilities obtained during a selected time period comprising a plurality of time segments, and determining in said computer that the EEG signals are indicative of hypoglycaemia being present or being impending based on said integration;
• in response to said determining step, providing an output notification of present or impending hypoglycaemia, wherein a previous session estimate of the mean and variability is used at the start of a current session and is progressively updated as the current session progresses;
• detecting artefact time segments, from said plurality of time segments of said EEG, which contain signal contaminating artefacts by obtaining a sum of a linear or non-linear function of the normalised features using a pre-established set of weighting coefficients to thereby obtain a time-varying artefact detection cost function, and classifying values of each said artefact detection cost function according to the probability of values of said artefact detection cost function being indicative of one of said artefacts;
• and
• excluding said artefact time segments from generating events to be included in said integration.

A wearable apparatus for detecting hypoglycaemia or impending hypoglycaemia by analysis of an EEG, said apparatus comprising one or more EEG measuring electrodes for gathering an EEG signal, a computer for receiving said EEG signals, said computer being programmed so as to carry out the steps of:

• obtaining from said EEG signal a plurality of components thereof, each component comprising a different band of frequencies;
• obtaining a measure of the varying intensity of each said component, obtaining a long term estimate of the mean of each said intensity measure and obtaining a long term estimate of the variability of each said intensity measure;
• normalising each said intensity measure by a process arithmetically equivalent to subtracting from the intensity measure the long term estimate of the mean and dividing the result by the long term estimate of the variability so as to generate from each frequency band a normalised feature;
• using machine analysis of the normalised features to obtain a time-varying hypoglycaemia cost function;
• classifying values of said hypoglycaemia cost function according to a probability of the values of said hypoglycaemia cost function being indicative of hypoglycaemia;
• integrating the probabilities obtained during a selected time period comprising a plurality of time segments,
• determining in said computer that the EEG signals are indicative of hypoglycaemia being present or being impending based on said integration;
• in response to said determining step, providing an output notification of present or impending hypoglycaemia, wherein a previous session estimate of the mean and variability is used at the start of a current session and is progressively updated as the current session progresses;
• detecting artefact time segments, from said pluraliy of time segments of said EEG, which contain signal contaminating artefacts confusable with hypoglycaemia patterns and excluding of a linear or non-linear function of the normalised features using a pre-established set of weighting coefficients to thereby obtain a time-varying artefact detection cost function, and classifying values of each said artefact detection cost function according to the probability of values of said artefact detection cost function being indicative of one of said artefacts;
• and
• excluding said artefact time segments from generating events to be included in said integration.