METHODS AND DEVICES FOR ANALYSIS OF CLUSTERED DATA, IN PARTICULAR ACTION POTENTIALS (I.E. NEURON FIRING SIGNALS IN THE BRAIN)
First Claim
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1. A method of identifying regions within the brain, the method comprising the steps of:
- a. providing a collection of N-dimensional data points, each data point being representative of a location in the brain and containing N>
1 different types of data therefrom;
b. sampling N different types of data from a location in the brain, thereby defining a sampled N-dimensional data point;
c. automatically defining within a processor M discrete data point clusters (M>
1) from the collected data points and from the sampled data point, wherein;
i. each data point cluster contains data points which are proximate in N-dimensional space, andii. the M data point clusters correspond to M discrete regions within the grey matter of the brain; and
d. indicating whether the sampled data point is within a particular data point cluster, and therefore within a particular one of the M regions of the brain.
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Abstract
Methods for clustering of multi-dimensional data allow unsupervised grouping of multi-dimensional data points into clusters having like characteristics. The methods may be usefully applied to extracellular action potentials (neuronal spikes) measured from the brain, whereby spike data may be grouped in accordance with dimensions such as spike period, spike shape, etc., to assist in identification and location of individual neurons and/or regions of the brain.
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22 Claims
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1. A method of identifying regions within the brain, the method comprising the steps of:
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a. providing a collection of N-dimensional data points, each data point being representative of a location in the brain and containing N>
1 different types of data therefrom;b. sampling N different types of data from a location in the brain, thereby defining a sampled N-dimensional data point; c. automatically defining within a processor M discrete data point clusters (M>
1) from the collected data points and from the sampled data point, wherein;i. each data point cluster contains data points which are proximate in N-dimensional space, and ii. the M data point clusters correspond to M discrete regions within the grey matter of the brain; and d. indicating whether the sampled data point is within a particular data point cluster, and therefore within a particular one of the M regions of the brain. - View Dependent Claims (2, 3, 4, 5, 7, 8, 9)
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10. A method of identifying regions within the grey matter of the brain, the method comprising the steps of:
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a. sampling data at different locations within the grey matter, wherein N different types of data are sampled at each location (N>
1), thereby generating a data set containing N-dimensional data points sampled at the locations;b. resolving within a processor M data point clusters (M>
1) from the data points, wherein each data cluster includes data points which are proximate in N-dimensional space,c. defining M regions of the brain, each region including the locations corresponding to the data points of the data cluster. - View Dependent Claims (6, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A device for identifying regions within the grey matter of the brain, the device comprising:
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a. a probe insertable within a brain, the probe having an electrode thereon which captures data points from the brain, each data point having N dimensions (N>
1);b. a processor in communication with the electrode, wherein the processor; (1) receives the measured N-dimensional data points, and (2) resolves M data point clusters (M>
1) from the data points, each data cluster including data points which are proximate in N-dimensional space,thereby identifying M regions of the brain, each region corresponding to one of the data clusters. - View Dependent Claims (20, 21, 22)
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Specification