METHOD OF BUILDING CLASSIFIERS FOR REAL-TIME CLASSIFICATION OF NEUROLOGICAL STATES
First Claim
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1. A method of building a binary classifier for classifying subjects into one of two brain function categories, comprising the steps of:
- providing a signal processing device operatively connected to a memory device storing a population reference database, the signal processing device comprising a processor configured to perform the steps of;
obtaining brain electrical signals in machine readable format from the population reference database, wherein the signals are recorded from a plurality of individuals in the presence or absence of brain abnormalities using one or more neurological electrodes;
extracting quantitative signal features from the recorded brain electrical signals;
storing the extracted signal features in the population reference database;
applying one or more data reduction criteria to the stored features in the population reference database to create a reduced pool of signal features;
selecting a subset of signal features from the reduced pool of features to construct the binary classifier; and
determining classification accuracy of the binary classifier by using it to classify data records having a priori classification information.
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Abstract
A method of building binary classifiers for classification of brain electrical activity data into one or more neurological classes is described. The method comprises the steps of extracting quantitative features from the brain electrical activity data, and reducing the pool of extracted features into a computationally manageable and statistically relevant set of features which can then be used for designing one or more classifiers.
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Citations
34 Claims
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1. A method of building a binary classifier for classifying subjects into one of two brain function categories, comprising the steps of:
providing a signal processing device operatively connected to a memory device storing a population reference database, the signal processing device comprising a processor configured to perform the steps of; obtaining brain electrical signals in machine readable format from the population reference database, wherein the signals are recorded from a plurality of individuals in the presence or absence of brain abnormalities using one or more neurological electrodes; extracting quantitative signal features from the recorded brain electrical signals; storing the extracted signal features in the population reference database; applying one or more data reduction criteria to the stored features in the population reference database to create a reduced pool of signal features; selecting a subset of signal features from the reduced pool of features to construct the binary classifier; and determining classification accuracy of the binary classifier by using it to classify data records having a priori classification information. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method of building a binary classifier for classification of individual data into one of two categories, comprising the steps of:
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providing a processor configured to build a binary classifier; accessing a pool of quantitative features from a population reference database stored in a memory device operatively coupled to the processor; applying one or more data reduction criteria to the pool of quantitative features; creating a reduced pool of features that are statistically relevant to the classification; selecting a subset of features from the reduced pool of features to construct the binary classifier; and evaluating performance of the binary classifier using pre--labeled data records stored in the memory device, wherein the pre-labeled data records are assigned a priori to one of the two categories. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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Specification